BIOMARKERS AND METHODS FOR PREDICTING PRETERM BIRTH

This application incorporates by reference a Sequence Listing submitted herewith as an ASCII text file entitled 13271-060-999_SL.txt created on Jun. 19, 2021, and having a size of 216,425 bytes.

The invention relates generally to the field of personalized medicine and, more specifically to compositions and methods for determining the probability for preterm birth in a pregnant female.

BACKGROUND

According to the World Health Organization, an estimated 15 million babies are born preterm (before 37 completed weeks of gestation) every year. In almost all countries with reliable data, preterm birth rates are increasing. See, World Health Organization; March of Dimes; The Partnership for Maternal, Newborn & Child Health; Save the Children, Born too soon: the global action report on preterm birth, ISBN 9789241503433(2012). An estimated 1 million babies die annually from preterm birth complications. Globally, preterm birth is the leading cause of newborn deaths (babies in the first four weeks of life) and the second leading cause of death after pneumonia in children under five years. Many survivors face a lifetime of disability, including learning disabilities and visual and hearing problems.

Across 184 countries with reliable data, the rate of preterm birth ranges from 5% to 18% of babies born. Blencowe et al., “National, regional and worldwide estimates of preterm birth.” The Lancet, 9; 379(9832):2162-72 (2012). While over 60% of preterm births occur in Africa and south Asia, preterm birth is nevertheless a global problem. Countries with the highest numbers include Brazil, India, Nigeria and the United States of America. Of the 11 countries with preterm birth rates over 15%, all but two are in sub-Saharan Africa. In the poorest countries, on average, 12% of babies are born too soon compared with 9% in higher-income countries. Within countries, poorer families are at higher risk. More than three-quarters of premature babies can be saved with feasible, cost-effective care, for example, antenatal steroid injections given to pregnant women at risk of preterm labour to strengthen the babies' lungs.

Infants born preterm are at greater risk than infants born at term for mortality and a variety of health and developmental problems. Complications include acute respiratory, gastrointestinal, immunologic, central nervous system, hearing, and vision problems, as well as longer-term motor, cognitive, visual, hearing, behavioral, social-emotional, health, and growth problems. The birth of a preterm infant can also bring considerable emotional and economic costs to families and have implications for public-sector services, such as health insurance, educational, and other social support systems. The greatest risk of mortality and morbidity is for those infants born at the earliest gestational ages. However, those infants born nearer to term represent the greatest number of infants born preterm and also experience more complications than infants born at term.

To prevent preterm birth in women who are less than 24 weeks pregnant with an ultrasound showing cervical opening, a surgical procedure known as cervical cerclage can be employed in which the cervix is stitched closed with strong sutures. For women less than 34 weeks pregnant and in active preterm labor, hospitalization may be necessary as well as the administration of medications to temporarily halt preterm labor and/or promote the fetal lung development. If a pregnant women is determined to be at risk for preterm birth, health care providers can implement various clinical strategies that may include preventive medications, for example, hydroxyprogesterone caproate (Makena) injections and/or vaginal progesterone gel, cervical pessaries, restrictions on sexual activity and/or other physical activities, and alterations of treatments for chronic conditions, such as diabetes and high blood pressure, that increase the risk of preterm labor.

There is a great need to identify and provide women at risk for preterm birth with proper antenatal care. Women identified as high-risk can be scheduled for more intensive antenatal surveillance and prophylactic interventions. Current strategies for risk assessment are based on the obstetric and medical history and clinical examination, but these strategies are only able to identify a small percentage of women who are at risk for preterm delivery. Reliable early identification of risk for preterm birth would enable planning appropriate monitoring and clinical management to prevent preterm delivery. Such monitoring and management might include: more frequent prenatal care visits, serial cervical length measurements, enhanced education regarding signs and symptoms of early preterm labor, lifestyle interventions for modifiable risk behaviors, cervical pessaries and progesterone treatment. Finally, reliable antenatal identification of risk for preterm birth also is crucial to cost-effective allocation of monitoring resources.

The present invention addresses this need by providing compositions and methods for determining whether a pregnant woman is at risk for preterm birth. Related advantages are provided as well.

SUMMARY

The present invention provides compositions and methods for predicting the probability of preterm birth in a pregnant female.

In one aspect, the invention provides a panel of isolated biomarkers comprising N of the biomarkers listed in Tables 1 through 63. In some embodiments, N is a number selected from the group consisting of 2 to 24. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR

In further embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

In a further aspect, the invention provides a panel of isolated biomarkers comprising N of the biomarkers listed in Tables 1 through 63. In some embodiments, N is a number selected from the group consisting of 2 to 24. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

In some embodiments, the invention provides a biomarker panel comprising at least two of the isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

In some embodiments, the invention provides a biomarker panel comprising at least two of the isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In other embodiments, the invention provides a biomarker panel comprising lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT).

In other embodiments, the invention provides a biomarker panel comprising Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In additional embodiments, the invention provides a biomarker panel comprising at least two of the isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 51 and the biomarkers set forth in Table 53.

Also provided by the invention is a method of determining probability for preterm birth in a pregnant female comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from the pregnant female, and analyzing the measurable feature to determine the probability for preterm birth in the pregnant female. In some embodiments, the invention provides a method of predicting GAB, the method encompassing detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from a pregnant female, and analyzing said measurable feature to predict GAB.

In some embodiments, a measurable feature comprises fragments or derivatives of each of the N biomarkers selected from the biomarkers listed in Tables 1 through 63. In some embodiments of the disclosed methods detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63, combinations or portions and/or derivatives thereof in a biological sample obtained from the pregnant female. In additional embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female further encompass detecting a measurable feature for one or more risk indicia associated with preterm birth.

In some embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of N biomarkers, wherein N is selected from the group consisting of 2 to 24. In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR. In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

In other embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

In other embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT).

In some embodiments of the methods of determining probability for preterm birth in a pregnant female, the probability for preterm birth in the pregnant female is calculated based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63. In some embodiments, the disclosed methods for determining the probability of preterm birth encompass detecting and/or quantifying one or more biomarkers using mass spectrometry, a capture agent or a combination thereof.

In some embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass an initial step of providing a biomarker panel comprising N of the biomarkers listed in Tables 1 through 63. In additional embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass an initial step of providing a biological sample from the pregnant female.

In some embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass communicating the probability to a health care provider. In additional embodiments, the communication informs a subsequent treatment decision for the pregnant female. In further embodiments, the treatment decision of one or more selected from the group of consisting of more frequent prenatal care visits, serial cervical length measurements, enhanced education regarding signs and symptoms of early preterm labor, lifestyle interventions for modifiable risk behaviors and progesterone treatment.

In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass analyzing the measurable feature of one or more isolated biomarkers using a predictive model. In some embodiments of the disclosed methods, a measurable feature of one or more isolated biomarkers is compared with a reference feature.

In additional embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass using one or more analyses selected from a linear discriminant analysis model, a support vector machine classification algorithm, a recursive feature elimination model, a prediction analysis of microarray model, a logistic regression model, a CART algorithm, a flex tree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, and a combination thereof. In one embodiment, the disclosed methods of determining probability for preterm birth in a pregnant female encompass logistic regression.

In some embodiments, the invention provides a method of determining probability for preterm birth in a pregnant female, the method encompassing quantifying in a biological sample obtained from the pregnant female an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63; multiplying the amount by a predetermined coefficient, and determining the probability for preterm birth in the pregnant female comprising adding the individual products to obtain a total risk score that corresponds to the probability

In additional embodiments, the invention provides a method of predicting GAB, the method comprising: (a) quantifying in a biological sample obtained from said pregnant female an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63; (b) multiplying or thresholding said amount by a predetermined coefficient, (c) determining the predicted GAB birth in said pregnant female comprising adding said individual products to obtain a total risk score that corresponds to said predicted GAB.

In further embodiments, the invention provides a method of predicting time to birth in a pregnant female, the method comprising: (a) obtaining a biological sample from said pregnant female; (b) quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in said biological sample; (c) multiplying or thresholding said amount by a predetermined coefficient, (d) determining predicted GAB in said pregnant female comprising adding said individual products to obtain a total risk score that corresponds to said predicted GAB; and (e) subtracting the estimated gestational age (GA) at time biological sample was obtained from the predicted GAB to predict time to birth in said pregnant female.

Other features and advantages of the invention will be apparent from the detailed description, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Scatterplot of actual gestational age at birth versus predicted gestational age from random forest regression model.

FIG. 2. Distribution of predicted gestational age from random forest regression model versus actual gestational age at birth (GAB), where actual GAB is given in categories of (i) less than 37 weeks, (ii) 37 to 39 weeks, and (iii) 40 weeks or greater (peaks left to right, respectively).

DETAILED DESCRIPTION

The present disclosure is based, in part, on the discovery that certain proteins and peptides in biological samples obtained from a pregnant female are differentially expressed in pregnant females that have an increased risk of preterm birth relative to controls. The present disclosure is further based, in part, on the unexpected discovery that panels combining one or more of these proteins and peptides can be utilized in methods of determining the probability for preterm birth in a pregnant female with high sensitivity and specificity. These proteins and peptides disclosed herein serve as biomarkers for classifying test samples, predicting probability of preterm birth, predicting probability of term birth, predicting gestational age at birth (GAB), predicting time to birth and/or monitoring of progress of preventative therapy in a pregnant female, either individually or in a panel of biomarkers.

The disclosure provides biomarker panels, methods and kits for determining the probability for preterm birth in a pregnant female. One major advantage of the present disclosure is that risk of developing preterm birth can be assessed early during pregnancy so that appropriate monitoring and clinical management to prevent preterm delivery can be initiated in a timely fashion. The present invention is of particular benefit to females lacking any risk factors for preterm birth and who would not otherwise be identified and treated.

By way of example, the present disclosure includes methods for generating a result useful in determining probability for preterm birth in a pregnant female by obtaining a dataset associated with a sample, where the dataset at least includes quantitative data about biomarkers and panels of biomarkers that have been identified as predictive of preterm birth, and inputting the dataset into an analytic process that uses the dataset to generate a result useful in determining probability for preterm birth in a pregnant female. As described further below, this quantitative data can include amino acids, peptides, polypeptides, proteins, nucleotides, nucleic acids, nucleosides, sugars, fatty acids, steroids, metabolites, carbohydrates, lipids, hormones, antibodies, regions of interest that serve as surrogates for biological macromolecules and combinations thereof.

In addition to the specific biomarkers identified in this disclosure, for example, by accession number in a public database, sequence, or reference, the invention also contemplates use of biomarker variants that are at least 90% or at least 95% or at least 97% identical to the exemplified sequences and that are now known or later discovered and that have utility for the methods of the invention. These variants may represent polymorphisms, splice variants, mutations, and the like. In this regard, the instant specification discloses multiple art-known proteins in the context of the invention and provides exemplary accession numbers associated with one or more public databases as well as exemplary references to published journal articles relating to these art-known proteins. However, those skilled in the art appreciate that additional accession numbers and journal articles can easily be identified that can provide additional characteristics of the disclosed biomarkers and that the exemplified references are in no way limiting with regard to the disclosed biomarkers. As described herein, various techniques and reagents find use in the methods of the present invention. Suitable samples in the context of the present invention include, for example, blood, plasma, serum, amniotic fluid, vaginal secretions, saliva, and urine. In some embodiments, the biological sample is selected from the group consisting of whole blood, plasma, and serum. In a particular embodiment, the biological sample is serum. As described herein, biomarkers can be detected through a variety of assays and techniques known in the art. As further described herein, such assays include, without limitation, mass spectrometry (MS)-based assays, antibody-based assays as well as assays that combine aspects of the two.

Protein biomarkers associated with the probability for preterm birth in a pregnant female include, but are not limited to, one or more of the isolated biomarkers listed in Tables 1 through 63. In addition to the specific biomarkers, the disclosure further includes biomarker variants that are about 90%, about 95%, or about 97% identical to the exemplified sequences. Variants, as used herein, include polymorphisms, splice variants, mutations, and the like.

Additional markers can be selected from one or more risk indicia, including but not limited to, maternal characteristics, medical history, past pregnancy history, and obstetrical history. Such additional markers can include, for example, previous low birth weight or preterm delivery, multiple 2nd trimester spontaneous abortions, prior first trimester induced abortion, familial and intergenerational factors, history of infertility, nulliparity, placental abnormalities, cervical and uterine anomalies, short cervical length measurements, gestational bleeding, intrauterine growth restriction, in utero diethylstilbestrol exposure, multiple gestations, infant sex, short stature, low prepregnancy weight, low or high body mass index, diabetes, hypertension, urogenital infections (i.e. urinary tract infection), asthma, anxiety and depression, asthma, hypertension, hypothyroidism. Demographic risk indicia for preterm birth can include, for example, maternal age, race/ethnicity, single marital status, low socioeconomic status, maternal age, employment-related physical activity, occupational exposures and environment exposures and stress. Further risk indicia can include, inadequate prenatal care, cigarette smoking, use of marijuana and other illicit drugs, cocaine use, alcohol consumption, caffeine intake, maternal weight gain, dietary intake, sexual activity during late pregnancy and leisure-time physical activities. (Preterm Birth: Causes, Consequences, and Prevention, Institute of Medicine (US) Committee on Understanding Premature Birth and Assuring Healthy Outcomes; Behrman RE, Butler AS, editors. Washington (DC): National Academies Press (US); 2007). Additional risk indicia useful for as markers can be identified using learning algorithms known in the art, such as linear discriminant analysis, support vector machine classification, recursive feature elimination, prediction analysis of microarray, logistic regression, CART, FlexTree, LART, random forest, MART, and/or survival analysis regression, which are known to those of skill in the art and are further described herein.

Provided herein are panels of isolated biomarkers comprising N of the biomarkers selected from the group listed in Tables 1 through 63. In the disclosed panels of biomarkers N can be a number selected from the group consisting of 2 to 24. In the disclosed methods, the number of biomarkers that are detected and whose levels are determined, can be 1, or more than 1, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or more. In certain embodiments, the number of biomarkers that are detected, and whose levels are determined, can be 1, or more than 1, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, or more. The methods of this disclosure are useful for determining the probability for preterm birth in a pregnant female.

While certain of the biomarkers listed in Tables 1 through 63 are useful alone for determining the probability for preterm birth in a pregnant female, methods are also described herein for the grouping of multiple subsets of the biomarkers that are each useful as a panel of three or more biomarkers. In some embodiments, the invention provides panels comprising N biomarkers, wherein N is at least three biomarkers. In other embodiments, N is selected to be any number from 3-23 biomarkers.

In yet other embodiments, N is selected to be any number from 2-5, 2-10, 2-15, 2-20, or 2-23. In other embodiments, N is selected to be any number from 3-5, 3-10, 3-15, 3-20, or 3-23. In other embodiments, N is selected to be any number from 4-5, 4-10, 4-15, 4-20, or 4-23. In other embodiments, N is selected to be any number from 5-10, 5-15, 5-20, or 5-23. In other embodiments, N is selected to be any number from 6-10, 6-15, 6-20, or 6-23. In other embodiments, N is selected to be any number from 7-10, 7-15, 7-20, or 7-23. In other embodiments, N is selected to be any number from 8-10, 8-15, 8-20, or 8-23. In other embodiments, N is selected to be any number from 9-10, 9-15, 9-20, or 9-23. In other embodiments, N is selected to be any number from 10-15, 10-20, or 10-23. It will be appreciated that N can be selected to encompass similar, but higher order, ranges.

In certain embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, or five isolated biomarkers comprising an amino acid sequence selected from AFTECCVVASQLR, ELLESYIDGR, ITLPDFTGDLR, TDAPDLPEENQAR and SFRPFVPR. In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, or five isolated biomarkers comprising an amino acid sequence selected from FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, or three of the isolated biomarkers consisting of an amino acid sequence selected from AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, or three of the isolated biomarkers consisting of an amino acid sequence selected from FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, or three of the isolated biomarkers consisting of an amino acid sequence selected from the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

In some embodiments, the panel of isolated biomarkers comprises one or more peptides comprising a fragment from lipopolysaccharide-binding protein (LBP), Schumann et al., Science 249 (4975), 1429-1431 (1990) (UniProtKB/Swiss-Prot: P18428.3); prothrombin (THRB), Walz et al., Proc. Natl. Acad. Sci. U.S.A. 74 (5), 1969-1972(1977) (NCBI Reference Sequence: NP_000497.1); complement component C5 (C5 or CO5) Haviland, J. Immunol. 146 (1), 362-368 (1991) (GenBank: AAA51925.1); plasminogen (PLMN) Petersen et al., J. Biol. Chem. 265 (11), 6104-6111(1990) (NCBI Reference Sequences: NP_000292.1 NP_001161810.1); and complement component C8 gamma chain (C8G or CO8G), Haefliger et al., Mol. Immunol. 28 (1-2), 123-131 (1991) (NCBI Reference Sequence: NP_000597.2).

In some embodiments, the panel of isolated biomarkers comprises one or more peptides comprising a fragment from cell adhesion molecule with homology to complement component 1, q subcomponent, B chain (C1QB), Reid, Biochem. J. 179 (2), 367-371 (1979) (NCBI Reference Sequence: NP_000482.3); fibrinogen beta chain (FIBB or FIB); Watt et al., Biochemistry 18 (1), 68-76 (1979) (NCBI Reference Sequences: NP_001171670.1 and NP_005132.2); C-reactive protein (CRP), Oliveira et al., J. Biol. Chem. 254 (2), 489-502 (1979) (NCBI Reference Sequence: NP_000558.2); inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) Kim et al., Mol. Biosyst. 7 (5), 1430-1440 (2011) (NCBI Reference Sequences: NP_001159921.1 and NP_002209.2); chorionic somatomammotropin hormone (CSH) Selby et al., J. Biol. Chem. 259 (21), 13131-13138 (1984) (NCBI Reference Sequence: NP_001308.1); and angiotensinogen (ANG or ANGT) Underwood et al., Metabolism 60(8):1150-7 (2011) (NCBI Reference Sequence: NP_000020.1).

In additional embodiments, the invention provides a panel of isolated biomarkers comprising N of the biomarkers listed in Tables 1 through 63. In some embodiments, N is a number selected from the group consisting of 2 to 24. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, ITLPDFTGDLR, TDAPDLPEENQAR and SFRPFVPR. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

In further embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G). In another embodiment, the invention provides a biomarker panel comprising at least three isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

In further embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In some embodiments, the invention provides a biomarker panel comprising lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT). In some embodiments, the invention provides a biomarker panel comprising Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In another aspect, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT) and the biomarkers set forth in Tables 51 and 53.

In another aspect, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

It must be noted that, as used in this specification and the appended claims, the singular forms “a”, “an” and “the” include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to “a biomarker” includes a mixture of two or more biomarkers, and the like.

The term “about,” particularly in reference to a given quantity, is meant to encompass deviations of plus or minus five percent.

As used in this application, including the appended claims, the singular forms “a,” “an,” and “the” include plural references, unless the content clearly dictates otherwise, and are used interchangeably with “at least one” and “one or more.”

As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “contains,” “containing,” and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, product-by-process, or composition of matter that comprises, includes, or contains an element or list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, product-by-process, or composition of matter.

As used herein, the term “panel” refers to a composition, such as an array or a collection, comprising one or more biomarkers. The term can also refer to a profile or index of expression patterns of one or more biomarkers described herein. The number of biomarkers useful for a biomarker panel is based on the sensitivity and specificity value for the particular combination of biomarker values.

As used herein, and unless otherwise specified, the terms “isolated” and “purified” generally describes a composition of matter that has been removed from its native environment (e.g., the natural environment if it is naturally occurring), and thus is altered by the hand of man from its natural state. An isolated protein or nucleic acid is distinct from the way it exists in nature.

The term “biomarker” refers to a biological molecule, or a fragment of a biological molecule, the change and/or the detection of which can be correlated with a particular physical condition or state. The terms “marker” and “biomarker” are used interchangeably throughout the disclosure. For example, the biomarkers of the present invention are correlated with an increased likelihood of preterm birth. Such biomarkers include, but are not limited to, biological molecules comprising nucleotides, nucleic acids, nucleosides, amino acids, sugars, fatty acids, steroids, metabolites, peptides, polypeptides, proteins, carbohydrates, lipids, hormones, antibodies, regions of interest that serve as surrogates for biological macromolecules and combinations thereof (e.g., glycoproteins, ribonucleoproteins, lipoproteins). The term also encompasses portions or fragments of a biological molecule, for example, peptide fragment of a protein or polypeptide that comprises at least 5 consecutive amino acid residues, at least 6 consecutive amino acid residues, at least 7 consecutive amino acid residues, at least 8 consecutive amino acid residues, at least 9 consecutive amino acid residues, at least 10 consecutive amino acid residues, at least 11 consecutive amino acid residues, at least 12 consecutive amino acid residues, at least 13 consecutive amino acid residues, at least 14 consecutive amino acid residues, at least 15 consecutive amino acid residues, at least 5 consecutive amino acid residues, at least 16 consecutive amino acid residues, at least 17 consecutive amino acid residues, at least 18 consecutive amino acid residues, at least 19 consecutive amino acid residues, at least 20 consecutive amino acid residues, at least 21 consecutive amino acid residues, at least 22 consecutive amino acid residues, at least 23 consecutive amino acid residues, at least 24 consecutive amino acid residues, at least 25 consecutive amino acid residues, or more consecutive amino acid residues.

The invention also provides a method of determining probability for preterm birth in a pregnant female, the method comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from the pregnant female, and analyzing the measurable feature to determine the probability for preterm birth in the pregnant female. As disclosed herein, a measurable feature comprises fragments or derivatives of each of said N biomarkers selected from the biomarkers listed in Tables 1 through 63. In some embodiments of the disclosed methods detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63, combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female.

The invention further provides a method of predicting GAB, the method encompassing detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from a pregnant female, and analyzing the measurable feature to predict GAB.

The invention also provides a method of predicting GAB, the method comprising: (a) quantifying in a biological sample obtained from the pregnant female an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63; (b) multiplying or thresholding the amount by a predetermined coefficient, (c) determining the predicted GAB birth in the pregnant female comprising adding the individual products to obtain a total risk score that corresponds to the predicted GAB.

The invention further provides a method of predicting time to birth in a pregnant female, the method comprising: (a) obtaining a biological sample from the pregnant female; (b) quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in the biological sample; (c) multiplying or thresholding the amount by a predetermined coefficient, (d) determining predicted GAB in the pregnant female comprising adding the individual products to obtain a total risk score that corresponds to the predicted GAB; and (e) subtracting the estimated gestational age (GA) at time biological sample was obtained from the predicted GAB to predict time to birth in said pregnant female. For methods directed to predicting time to birth, it is understood that “birth” means birth following spontaneous onset of labor, with or without rupture of membranes.

Although described and exemplified with reference to methods of determining probability for preterm birth in a pregnant female, the present disclosure is similarly applicable to the methods of predicting GAB, the methods for predicting term birth, methods for determining the probability of term birth in a pregnant female as well methods of predicting time to birth in a pregnant female. It will be apparent to one skilled in the art that each of the aforementioned methods has specific and substantial utilities and benefits with regard maternal-fetal health considerations.

In some embodiments, the method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of N biomarkers, wherein N is selected from the group consisting of 2 to 24. In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

In additional embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

In additional embodiments, the method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In further embodiments, the disclosed method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT).

In further embodiments, the disclosed method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

In additional embodiments, the methods of determining probability for preterm birth in a pregnant female further encompass detecting a measurable feature for one or more risk indicia associated with preterm birth. In additional embodiments the risk indicia are selected form the group consisting of previous low birth weight or preterm delivery, multiple 2nd trimester spontaneous abortions, prior first trimester induced abortion, familial and intergenerational factors, history of infertility, nulliparity, placental abnormalities, cervical and uterine anomalies, gestational bleeding, intrauterine growth restriction, in utero diethylstilbestrol exposure, multiple gestations, infant sex, short stature, low prepregnancy weight, low or high body mass index, diabetes, hypertension, and urogenital infections.

A “measurable feature” is any property, characteristic or aspect that can be determined and correlated with the probability for preterm birth in a subject. The term further encompasses any property, characteristic or aspect that can be determined and correlated in connection with a prediction of GAB, a prediction of term birth, or a prediction of time to birth in a pregnant female. For a biomarker, such a measurable feature can include, for example, the presence, absence, or concentration of the biomarker, or a fragment thereof, in the biological sample, an altered structure, such as, for example, the presence or amount of a post-translational modification, such as oxidation at one or more positions on the amino acid sequence of the biomarker or, for example, the presence of an altered conformation in comparison to the conformation of the biomarker in normal control subjects, and/or the presence, amount, or altered structure of the biomarker as a part of a profile of more than one biomarker. In addition to biomarkers, measurable features can further include risk indicia including, for example, maternal characteristics, age, race, ethnicity, medical history, past pregnancy history, obstetrical history. For a risk indicium, a measurable feature can include, for example, previous low birth weight or preterm delivery, multiple 2nd trimester spontaneous abortions, prior first trimester induced abortion, familial and intergenerational factors, history of infertility, nulliparity, placental abnormalities, cervical and uterine anomalies, short cervical length measurements, gestational bleeding, intrauterine growth restriction, in utero diethylstilbestrol exposure, multiple gestations, infant sex, short stature, low prepregnancy weight/low body mass index, diabetes, hypertension, urogenital infections, hypothyroidism, asthma, low educational attainment, cigarette smoking, drug use and alcohol consumption.

In some embodiments of the disclosed methods of determining probability for preterm birth in a pregnant female, the probability for preterm birth in the pregnant female is calculated based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63. In some embodiments, the disclosed methods for determining the probability of preterm birth encompass detecting and/or quantifying one or more biomarkers using mass spectrometry, a capture agent or a combination thereof.

In some embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass communicating the probability to a health care provider. The disclosed of predicting GAB, the methods for predicting term birth, methods for determining the probability of term birth in a pregnant female as well methods of predicting time to birth in a pregnant female similarly encompass communicating the probability to a health care provider. As stated above, although described and exemplified with reference to determining probability for preterm birth in a pregnant female, all embodiments described throughout this disclosure are similarly applicable to the methods of predicting GAB, the methods for predicting term birth, methods for determining the probability of term birth in a pregnant female as well methods of predicting time to birth in a pregnant female. Specifically, the biomarkers and panels recited throughout this application with express reference to methods for preterm birth can also be used in methods for predicting GAB, the methods for predicting term birth, methods for determining the probability of term birth in a pregnant female as well methods of predicting time to birth in a pregnant female. It will be apparent to one skilled in the art that each of the aforementioned methods have specific and substantial utilities and benefits with regard maternal-fetal health considerations.

In additional embodiments, the communication informs a subsequent treatment decision for the pregnant female. In some embodiments, the method of determining probability for preterm birth in a pregnant female encompasses the additional feature of expressing the probability as a risk score.

As used herein, the term “risk score” refers to a score that can be assigned based on comparing the amount of one or more biomarkers in a biological sample obtained from a pregnant female to a standard or reference score that represents an average amount of the one or more biomarkers calculated from biological samples obtained from a random pool of pregnant females. Because the level of a biomarker may not be static throughout pregnancy, a standard or reference score has to have been obtained for the gestational time point that corresponds to that of the pregnant female at the time the sample was taken. The standard or reference score can be predetermined and built into a predictor model such that the comparison is indirect rather than actually performed every time the probability is determined for a subject. A risk score can be a standard (e.g., a number) or a threshold (e.g., a line on a graph). The value of the risk score correlates to the deviation, upwards or downwards, from the average amount of the one or more biomarkers calculated from biological samples obtained from a random pool of pregnant females. In certain embodiments, if a risk score is greater than a standard or reference risk score, the pregnant female can have an increased likelihood of preterm birth. In some embodiments, the magnitude of a pregnant female's risk score, or the amount by which it exceeds a reference risk score, can be indicative of or correlated to that pregnant female's level of risk.

In the context of the present invention, the term “biological sample,” encompasses any sample that is taken from pregnant female and contains one or more of the biomarkers listed in Tables 1 through 63. Suitable samples in the context of the present invention include, for example, blood, plasma, serum, amniotic fluid, vaginal secretions, saliva, and urine. In some embodiments, the biological sample is selected from the group consisting of whole blood, plasma, and serum. In a particular embodiment, the biological sample is serum. As will be appreciated by those skilled in the art, a biological sample can include any fraction or component of blood, without limitation, T cells, monocytes, neutrophils, erythrocytes, platelets and microvesicles such as exosomes and exosome-like vesicles. In a particular embodiment, the biological sample is serum.

Preterm birth refers to delivery or birth at a gestational age less than 37 completed weeks. Other commonly used subcategories of preterm birth have been established and delineate moderately preterm (birth at 33 to 36 weeks of gestation), very preterm (birth at <33 weeks of gestation), and extremely preterm (birth at ≤28 weeks of gestation). With regard to the methods disclosed herein, those skilled in the art understand that the cut-offs that delineate preterm birth and term birth as well as the cut-offs that delineate subcategories of preterm birth can be adjusted in practicing the methods disclosed herein, for example, to maximize a particular health benefit. It is further understood that such adjustments are well within the skill set of individuals considered skilled in the art and encompassed within the scope of the inventions disclosed herein. Gestational age is a proxy for the extent of fetal development and the fetus's readiness for birth. Gestational age has typically been defined as the length of time from the date of the last normal menses to the date of birth. However, obstetric measures and ultrasound estimates also can aid in estimating gestational age. Preterm births have generally been classified into two separate subgroups. One, spontaneous preterm births are those occurring subsequent to spontaneous onset of preterm labor or preterm premature rupture of membranes regardless of subsequent labor augmentation or cesarean delivery. Two, indicated preterm births are those occurring following induction or cesarean section for one or more conditions that the woman's caregiver determines to threaten the health or life of the mother and/or fetus. In some embodiments, the methods disclosed herein are directed to determining the probability for spontaneous preterm birth. In additional embodiments, the methods disclosed herein are directed to predicting gestational birth.

As used herein, the term “estimated gestational age” or “estimated GA” refers to the GA determined based on the date of the last normal menses and additional obstetric measures, ultrasound estimates or other clinical parameters including, without limitation, those described in the preceding paragraph. In contrast the term “predicted gestational age at birth” or “predicted GAB” refers to the GAB determined based on the methods of the invention as disclosed herein. As used herein, “term birth” refers to birth at a gestational age equal or more than 37 completed weeks.

In some embodiments, the pregnant female is between 17 and 28 weeks of gestation at the time the biological sample is collected. In other embodiments, the pregnant female is between 16 and 29 weeks, between 17 and 28 weeks, between 18 and 27 weeks, between 19 and 26 weeks, between 20 and 25 weeks, between 21 and 24 weeks, or between 22 and 23 weeks of gestation at the time the biological sample is collected. In further embodiments, the pregnant female is between about 17 and 22 weeks, between about 16 and 22 weeks between about 22 and 25 weeks, between about 13 and 25 weeks, between about 26 and 28, or between about 26 and 29 weeks of gestation at the time the biological sample is collected. Accordingly, the gestational age of a pregnant female at the time the biological sample is collected can be 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 weeks.

In some embodiments of the claimed methods the measurable feature comprises fragments or derivatives of each of the N biomarkers selected from the biomarkers listed in Tables 1 through 63. In additional embodiments of the claimed methods, detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63, combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female.

The term “amount” or “level” as used herein refers to a quantity of a biomarker that is detectable or measurable in a biological sample and/or control. The quantity of a biomarker can be, for example, a quantity of polypeptide, the quantity of nucleic acid, or the quantity of a fragment or surrogate. The term can alternatively include combinations thereof. The term “amount” or “level” of a biomarker is a measurable feature of that biomarker.

In some embodiments, calculating the probability for preterm birth in a pregnant female is based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63. Any existing, available or conventional separation, detection and quantification methods can be used herein to measure the presence or absence (e.g., readout being present vs. absent; or detectable amount vs. undetectable amount) and/or quantity (e.g., readout being an absolute or relative quantity, such as, for example, absolute or relative concentration) of biomarkers, peptides, polypeptides, proteins and/or fragments thereof and optionally of the one or more other biomarkers or fragments thereof in samples. In some embodiments, detection and/or quantification of one or more biomarkers comprises an assay that utilizes a capture agent. In further embodiments, the capture agent is an antibody, antibody fragment, nucleic acid-based protein binding reagent, small molecule or variant thereof. In additional embodiments, the assay is an enzyme immunoassay (EIA), enzyme-linked immunosorbent assay (ELISA), and radioimmunoassay (MA). In some embodiments, detection and/or quantification of one or more biomarkers further comprises mass spectrometry (MS). In yet further embodiments, the mass spectrometry is co-immunoprecipitation-mass spectrometry (co-IP MS), where coimmunoprecipitation, a technique suitable for the isolation of whole protein complexes is followed by mass spectrometric analysis.

As used herein, the term “mass spectrometer” refers to a device able to volatilize/ionize analytes to form gas-phase ions and determine their absolute or relative molecular masses. Suitable methods of volatilization/ionization are matrix-assisted laser desorption ionization (MALDI), electrospray, laser/light, thermal, electrical, atomized/sprayed and the like, or combinations thereof. Suitable forms of mass spectrometry include, but are not limited to, ion trap instruments, quadrupole instruments, electrostatic and magnetic sector instruments, time of flight instruments, time of flight tandem mass spectrometer (TOF MS/MS), Fourier-transform mass spectrometers, Orbitraps and hybrid instruments composed of various combinations of these types of mass analyzers. These instruments can, in turn, be interfaced with a variety of other instruments that fractionate the samples (for example, liquid chromatography or solid-phase adsorption techniques based on chemical, or biological properties) and that ionize the samples for introduction into the mass spectrometer, including matrix-assisted laser desorption (MALDI), electrospray, or nanospray ionization (ESI) or combinations thereof.

Generally, any mass spectrometric (MS) technique that can provide precise information on the mass of peptides, and preferably also on fragmentation and/or (partial) amino acid sequence of selected peptides (e.g., in tandem mass spectrometry, MS/MS; or in post source decay, TOF MS), can be used in the methods disclosed herein. Suitable peptide MS and MS/MS techniques and systems are well-known per se (see, e.g., Methods in Molecular Biology, vol. 146: “Mass Spectrometry of Proteins and Peptides”, by Chapman, ed., Humana Press 2000; Biemann 1990. Methods Enzymol 193: 455-79; or Methods in Enzymology, vol. 402: “Biological Mass Spectrometry”, by Burlingame, ed., Academic Press 2005) and can be used in practicing the methods disclosed herein. Accordingly, in some embodiments, the disclosed methods comprise performing quantitative MS to measure one or more biomarkers. Such quantitative methods can be performed in an automated (Villanueva, et al., Nature Protocols (2006) 1(2):880-891) or semi-automated format. In particular embodiments, MS can be operably linked to a liquid chromatography device (LC-MS/MS or LC-MS) or gas chromatography device (GC-MS or GC-MS/MS). Other methods useful in this context include isotope-coded affinity tag (ICAT), tandem mass tags (TMT), or stable isotope labeling by amino acids in cell culture (SILAC), followed by chromatography and MS/MS.

As used herein, the terms “multiple reaction monitoring (MRM)” or “selected reaction monitoring (SRM)” refer to an MS-based quantification method that is particularly useful for quantifying analytes that are in low abundance. In an SRM experiment, a predefined precursor ion and one or more of its fragments are selected by the two mass filters of a triple quadrupole instrument and monitored over time for precise quantification. Multiple SRM precursor and fragment ion pairs can be measured within the same experiment on the chromatographic time scale by rapidly toggling between the different precursor/fragment pairs to perform an MRM experiment. A series of transitions (precursor/fragment ion pairs) in combination with the retention time of the targeted analyte (e.g., peptide or small molecule such as chemical entity, steroid, hormone) can constitute a definitive assay. A large number of analytes can be quantified during a single LC-MS experiment. The term “scheduled,” or “dynamic” in reference to MRM or SRM, refers to a variation of the assay wherein the transitions for a particular analyte are only acquired in a time window around the expected retention time, significantly increasing the number of analytes that can be detected and quantified in a single LC-MS experiment and contributing to the selectivity of the test, as retention time is a property dependent on the physical nature of the analyte. A single analyte can also be monitored with more than one transition. Finally, included in the assay can be standards that correspond to the analytes of interest (e.g., same amino acid sequence), but differ by the inclusion of stable isotopes. Stable isotopic standards (SIS) can be incorporated into the assay at precise levels and used to quantify the corresponding unknown analyte. An additional level of specificity is contributed by the co-elution of the unknown analyte and its corresponding SIS and properties of their transitions (e.g., the similarity in the ratio of the level of two transitions of the unknown and the ratio of the two transitions of its corresponding SIS).

Mass spectrometry assays, instruments and systems suitable for biomarker peptide analysis can include, without limitation, matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) MS; MALDI-TOF post-source-decay (PSD); MALDI-TOF/TOF; surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF) MS; electrospray ionization mass spectrometry (ESI-MS); ESI-MS/MS; ESI-MS/(MS)_n(n is an integer greater than zero); ESI 3D or linear (2D) ion trap MS; ESI triple quadrupole MS; ESI quadrupole orthogonal TOF (Q-TOF); ESI Fourier transform MS systems; desorption/ionization on silicon (DIOS); secondary ion mass spectrometry (SIMS); atmospheric pressure chemical ionization mass spectrometry (APCI-MS); APCI-MS/MS; APCI-(MS)_n; ion mobility spectrometry (IMS); inductively coupled plasma mass spectrometry (ICP-MS) atmospheric pressure photoionization mass spectrometry (APPI-MS); APPI-MS/MS; and APPI-(MS)_n. Peptide ion fragmentation in tandem MS (MS/MS) arrangements can be achieved using manners established in the art, such as, e.g., collision induced dissociation (CID). As described herein, detection and quantification of biomarkers by mass spectrometry can involve multiple reaction monitoring (MRM), such as described among others by Kuhn et al. Proteomics 4: 1175-86 (2004). Scheduled multiple-reaction-monitoring (Scheduled MRM) mode acquisition during LC-MS/MS analysis enhances the sensitivity and accuracy of peptide quantitation. Anderson and Hunter, Molecular and Cellular Proteomics 5(4):573 (2006). As described herein, mass spectrometry-based assays can be advantageously combined with upstream peptide or protein separation or fractionation methods, such as for example with the chromatographic and other methods described herein below. As further described herein, shotgun quantitative proteomics can be combined with SRM/MRM-based assays for high-throughput identification and verification of prognostic biomarkers of preterm birth.

A person skilled in the art will appreciate that a number of methods can be used to determine the amount of a biomarker, including mass spectrometry approaches, such as MS/MS, LC-MS/MS, multiple reaction monitoring (MRM) or SRM and product-ion monitoring (PIM) and also including antibody based methods such as immunoassays such as Western blots, enzyme-linked immunosorbant assay (ELISA), immunoprecipitation, immunohistochemistry, immunofluorescence, radioimmunoassay, dot blotting, and FACS. Accordingly, in some embodiments, determining the level of the at least one biomarker comprises using an immunoassay and/or mass spectrometric methods. In additional embodiments, the mass spectrometric methods are selected from MS, MS/MS, LC-MS/MS, SRM, PIM, and other such methods that are known in the art. In other embodiments, LC-MS/MS further comprises 1D LC-MS/MS, 2D LC-MS/MS or 3D LC-MS/MS. Immunoassay techniques and protocols are generally known to those skilled in the art (Price and Newman, Principles and Practice of Immunoassay, 2nd Edition, Grove's Dictionaries, 1997; and Gosling, Immunoassays: A Practical Approach, Oxford University Press, 2000.) A variety of immunoassay techniques, including competitive and non-competitive immunoassays, can be used (Self et al., Curr. Opin. Biotechnol., 7:60-65 (1996).

In further embodiments, the immunoassay is selected from Western blot, ELISA, immunoprecipitation, immunohistochemistry, immunofluorescence, radioimmunoassay (MA), dot blotting, and FACS. In certain embodiments, the immunoassay is an ELISA. In yet a further embodiment, the ELISA is direct ELISA (enzyme-linked immunosorbent assay), indirect ELISA, sandwich ELISA, competitive ELISA, multiplex ELISA, ELISPOT technologies, and other similar techniques known in the art. Principles of these immunoassay methods are known in the art, for example John R. Crowther, The ELISA Guidebook, 1st ed., Humana Press 2000, ISBN 0896037282. Typically ELISAs are performed with antibodies but they can be performed with any capture agents that bind specifically to one or more biomarkers of the invention and that can be detected. Multiplex ELISA allows simultaneous detection of two or more analytes within a single compartment (e.g., microplate well) usually at a plurality of array addresses (Nielsen and Geierstanger 2004. J Immunol Methods 290: 107-20 (2004) and Ling et al. 2007. Expert Rev Mol Diagn 7: 87-98 (2007)).

In some embodiments, Radioimmunoassay (RIA) can be used to detect one or more biomarkers in the methods of the invention. RIA is a competition-based assay that is well known in the art and involves mixing known quantities of radioactively-labelled (e.g., ¹²⁵I or ¹³¹I-labelled) target analyte with antibody specific for the analyte, then adding non-labelled analyte from a sample and measuring the amount of labelled analyte that is displaced (see, e.g., An Introduction to Radioimmunoassay and Related Techniques, by Chard T, ed., Elsevier Science 1995, ISBN 0444821198 for guidance).

A detectable label can be used in the assays described herein for direct or indirect detection of the biomarkers in the methods of the invention. A wide variety of detectable labels can be used, with the choice of label depending on the sensitivity required, ease of conjugation with the antibody, stability requirements, and available instrumentation and disposal provisions. Those skilled in the art are familiar with selection of a suitable detectable label based on the assay detection of the biomarkers in the methods of the invention. Suitable detectable labels include, but are not limited to, fluorescent dyes (e.g., fluorescein, fluorescein isothiocyanate (FITC), Oregon Green™, rhodamine, Texas red, tetrarhodimine isothiocynate (TRITC), Cy3, Cy5, etc.), fluorescent markers (e.g., green fluorescent protein (GFP), phycoerythrin, etc.), enzymes (e.g., luciferase, horseradish peroxidase, alkaline phosphatase, etc.), nanoparticles, biotin, digoxigenin, metals, and the like.

For mass-spectrometry based analysis, differential tagging with isotopic reagents, e.g., isotope-coded affinity tags (ICAT) or the more recent variation that uses isobaric tagging reagents, iTRAQ (Applied Biosystems, Foster City, Calif.), or tandem mass tags, TMT, (Thermo Scientific, Rockford, Ill.), followed by multidimensional liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis can provide a further methodology in practicing the methods of the invention.

A chemiluminescence assay using a chemiluminescent antibody can be used for sensitive, non-radioactive detection of protein levels. An antibody labeled with fluorochrome also can be suitable. Examples of fluorochromes include, without limitation, DAPI, fluorescein, Hoechst 33258, R-phycocyanin, B-phycoerythrin, R-phycoerythrin, rhodamine, Texas red, and lissamine. Indirect labels include various enzymes well known in the art, such as horseradish peroxidase (HRP), alkaline phosphatase (AP), beta-galactosidase, urease, and the like. Detection systems using suitable substrates for horseradish-peroxidase, alkaline phosphatase, beta-galactosidase are well known in the art.

A signal from the direct or indirect label can be analyzed, for example, using a spectrophotometer to detect color from a chromogenic substrate; a radiation counter to detect radiation such as a gamma counter for detection of ¹²⁵I; or a fluorometer to detect fluorescence in the presence of light of a certain wavelength. For detection of enzyme-linked antibodies, a quantitative analysis can be made using a spectrophotometer such as an EMAX Microplate Reader (Molecular Devices; Menlo Park, Calif.) in accordance with the manufacturer's instructions. If desired, assays used to practice the invention can be automated or performed robotically, and the signal from multiple samples can be detected simultaneously.

In some embodiments, the methods described herein encompass quantification of the biomarkers using mass spectrometry (MS). In further embodiments, the mass spectrometry can be liquid chromatography-mass spectrometry (LC-MS), multiple reaction monitoring (MRM) or selected reaction monitoring (SRM). In additional embodiments, the MRM or SRM can further encompass scheduled MRM or scheduled SRM.

As described above, chromatography can also be used in practicing the methods of the invention. Chromatography encompasses methods for separating chemical substances and generally involves a process in which a mixture of analytes is carried by a moving stream of liquid or gas (“mobile phase”) and separated into components as a result of differential distribution of the analytes as they flow around or over a stationary liquid or solid phase (“stationary phase”), between the mobile phase and said stationary phase. The stationary phase can be usually a finely divided solid, a sheet of filter material, or a thin film of a liquid on the surface of a solid, or the like. Chromatography is well understood by those skilled in the art as a technique applicable for the separation of chemical compounds of biological origin, such as, e.g., amino acids, proteins, fragments of proteins or peptides, etc.

Chromatography can be columnar (i.e., wherein the stationary phase is deposited or packed in a column), preferably liquid chromatography, and yet more preferably high-performance liquid chromatography (HPLC), or ultra high performance/pressure liquid chromatography (UHPLC). Particulars of chromatography are well known in the art (Bidlingmeyer, Practical HPLC Methodology and Applications, John Wiley & Sons Inc., 1993). Exemplary types of chromatography include, without limitation, high-performance liquid chromatography (HPLC), UHPLC, normal phase HPLC (NP-HPLC), reversed phase HPLC (RP-HPLC), ion exchange chromatography (IEC), such as cation or anion exchange chromatography, hydrophilic interaction chromatography (HILIC), hydrophobic interaction chromatography (HIC), size exclusion chromatography (SEC) including gel filtration chromatography or gel permeation chromatography, chromatofocusing, affinity chromatography such as immuno-affinity, immobilised metal affinity chromatography, and the like. Chromatography, including single-, two- or more-dimensional chromatography, can be used as a peptide fractionation method in conjunction with a further peptide analysis method, such as for example, with a downstream mass spectrometry analysis as described elsewhere in this specification.

Further peptide or polypeptide separation, identification or quantification methods can be used, optionally in conjunction with any of the above described analysis methods, for measuring biomarkers in the present disclosure. Such methods include, without limitation, chemical extraction partitioning, isoelectric focusing (IEF) including capillary isoelectric focusing (CIEF), capillary isotachophoresis (CITP), capillary electrochromatography (CEC), and the like, one-dimensional polyacrylamide gel electrophoresis (PAGE), two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), capillary gel electrophoresis (CGE), capillary zone electrophoresis (CZE), micellar electrokinetic chromatography (MEKC), free flow electrophoresis (FFE), etc.

In the context of the invention, the term “capture agent” refers to a compound that can specifically bind to a target, in particular a biomarker. The term includes antibodies, antibody fragments, nucleic acid-based protein binding reagents (e.g. aptamers, Slow Off-rate Modified Aptamers (SOMAmer™)), protein-capture agents, natural ligands (i.e. a hormone for its receptor or vice versa), small molecules or variants thereof.

Capture agents can be configured to specifically bind to a target, in particular a biomarker. Capture agents can include but are not limited to organic molecules, such as polypeptides, polynucleotides and other non polymeric molecules that are identifiable to a skilled person. In the embodiments disclosed herein, capture agents include any agent that can be used to detect, purify, isolate, or enrich a target, in particular a biomarker. Any art-known affinity capture technologies can be used to selectively isolate and enrich/concentrate biomarkers that are components of complex mixtures of biological media for use in the disclosed methods.

Antibody capture agents that specifically bind to a biomarker can be prepared using any suitable methods known in the art. See, e.g., Coligan, Current Protocols in Immunology (1991); Harlow & Lane, Antibodies: A Laboratory Manual (1988); Goding, Monoclonal Antibodies: Principles and Practice (2d ed. 1986). Antibody capture agents can be any immunoglobulin or derivative thereof, whether natural or wholly or partially synthetically produced. All derivatives thereof which maintain specific binding ability are also included in the term. Antibody capture agents have a binding domain that is homologous or largely homologous to an immunoglobulin binding domain and can be derived from natural sources, or partly or wholly synthetically produced. Antibody capture agents can be monoclonal or polyclonal antibodies. In some embodiments, an antibody is a single chain antibody. Those of ordinary skill in the art will appreciate that antibodies can be provided in any of a variety of forms including, for example, humanized, partially humanized, chimeric, chimeric humanized, etc. Antibody capture agents can be antibody fragments including, but not limited to, Fab, Fab′, F(ab′)2, scFv, Fv, dsFv diabody, and Fd fragments. An antibody capture agent can be produced by any means. For example, an antibody capture agent can be enzymatically or chemically produced by fragmentation of an intact antibody and/or it can be recombinantly produced from a gene encoding the partial antibody sequence. An antibody capture agent can comprise a single chain antibody fragment. Alternatively or additionally, antibody capture agent can comprise multiple chains which are linked together, for example, by disulfide linkages; and, any functional fragments obtained from such molecules, wherein such fragments retain specific-binding properties of the parent antibody molecule. Because of their smaller size as functional components of the whole molecule, antibody fragments can offer advantages over intact antibodies for use in certain immunochemical techniques and experimental applications.

Suitable capture agents useful for practicing the invention also include aptamers. Aptamers are oligonucleotide sequences that can bind to their targets specifically via unique three dimensional (3-D) structures. An aptamer can include any suitable number of nucleotides and different aptamers can have either the same or different numbers of nucleotides. Aptamers can be DNA or RNA or chemically modified nucleic acids and can be single stranded, double stranded, or contain double stranded regions, and can include higher ordered structures. An aptamer can also be a photoaptamer, where a photoreactive or chemically reactive functional group is included in the aptamer to allow it to be covalently linked to its corresponding target. Use of an aptamer capture agent can include the use of two or more aptamers that specifically bind the same biomarker. An aptamer can include a tag. An aptamer can be identified using any known method, including the SELEX (systematic evolution of ligands by exponential enrichment), process. Once identified, an aptamer can be prepared or synthesized in accordance with any known method, including chemical synthetic methods and enzymatic synthetic methods and used in a variety of applications for biomarker detection. Liu et al., Curr Med Chem. 18(27):4117-25 (2011). Capture agents useful in practicing the methods of the invention also include SOMAmers (Slow Off-Rate Modified Aptamers) known in the art to have improved off-rate characteristics. Brody et al., J Mol Biol. 422(5):595-606 (2012). SOMAmers can be generated using any known method, including the SELEX method.

It is understood by those skilled in the art that biomarkers can be modified prior to analysis to improve their resolution or to determine their identity. For example, the biomarkers can be subject to proteolytic digestion before analysis. Any protease can be used. Proteases, such as trypsin, that are likely to cleave the biomarkers into a discrete number of fragments are particularly useful. The fragments that result from digestion function as a fingerprint for the biomarkers, thereby enabling their detection indirectly. This is particularly useful where there are biomarkers with similar molecular masses that might be confused for the biomarker in question. Also, proteolytic fragmentation is useful for high molecular weight biomarkers because smaller biomarkers are more easily resolved by mass spectrometry. In another example, biomarkers can be modified to improve detection resolution. For instance, neuraminidase can be used to remove terminal sialic acid residues from glycoproteins to improve binding to an anionic adsorbent and to improve detection resolution. In another example, the biomarkers can be modified by the attachment of a tag of particular molecular weight that specifically binds to molecular biomarkers, further distinguishing them. Optionally, after detecting such modified biomarkers, the identity of the biomarkers can be further determined by matching the physical and chemical characteristics of the modified biomarkers in a protein database (e.g., SwissProt).

It is further appreciated in the art that biomarkers in a sample can be captured on a substrate for detection. Traditional substrates include antibody-coated 96-well plates or nitrocellulose membranes that are subsequently probed for the presence of the proteins. Alternatively, protein-binding molecules attached to microspheres, microparticles, microbeads, beads, or other particles can be used for capture and detection of biomarkers. The protein-binding molecules can be antibodies, peptides, peptoids, aptamers, small molecule ligands or other protein-binding capture agents attached to the surface of particles. Each protein-binding molecule can include unique detectable label that is coded such that it can be distinguished from other detectable labels attached to other protein-binding molecules to allow detection of biomarkers in multiplex assays. Examples include, but are not limited to, color-coded microspheres with known fluorescent light intensities (see e.g., microspheres with xMAP technology produced by Luminex (Austin, Tex.); microspheres containing quantum dot nanocrystals, for example, having different ratios and combinations of quantum dot colors (e.g., Qdot nanocrystals produced by Life Technologies (Carlsbad, Calif.); glass coated metal nanoparticles (see e.g., SERS nanotags produced by Nanoplex Technologies, Inc. (Mountain View, Calif.); barcode materials (see e.g., sub-micron sized striped metallic rods such as Nanobarcodes produced by Nanoplex Technologies, Inc.), encoded microparticles with colored bar codes (see e.g., CellCard produced by Vitra Bioscience, vitrabio.com), glass microparticles with digital holographic code images (see e.g., CyVera microbeads produced by Illumina (San Diego, Calif.); chemiluminescent dyes, combinations of dye compounds; and beads of detectably different sizes.

In another aspect, biochips can be used for capture and detection of the biomarkers of the invention. Many protein biochips are known in the art. These include, for example, protein biochips produced by Packard BioScience Company (Meriden Conn.), Zyomyx (Hayward, Calif.) and Phylos (Lexington, Mass.). In general, protein biochips comprise a substrate having a surface. A capture reagent or adsorbent is attached to the surface of the substrate. Frequently, the surface comprises a plurality of addressable locations, each of which location has the capture agent bound there. The capture agent can be a biological molecule, such as a polypeptide or a nucleic acid, which captures other biomarkers in a specific manner. Alternatively, the capture agent can be a chromatographic material, such as an anion exchange material or a hydrophilic material. Examples of protein biochips are well known in the art.

Measuring mRNA in a biological sample can be used as a surrogate for detection of the level of the corresponding protein biomarker in a biological sample. Thus, any of the biomarkers or biomarker panels described herein can also be detected by detecting the appropriate RNA. Levels of mRNA can measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed with qPCR). RT-PCR is used to create a cDNA from the mRNA. The cDNA can be used in a qPCR assay to produce fluorescence as the DNA amplification process progresses. By comparison to a standard curve, qPCR can produce an absolute measurement such as number of copies of mRNA per cell. Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure expression levels of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

Some embodiments disclosed herein relate to diagnostic and prognostic methods of determining the probability for preterm birth in a pregnant female. The detection of the level of expression of one or more biomarkers and/or the determination of a ratio of biomarkers can be used to determine the probability for preterm birth in a pregnant female. Such detection methods can be used, for example, for early diagnosis of the condition, to determine whether a subject is predisposed to preterm birth, to monitor the progress of preterm birth or the progress of treatment protocols, to assess the severity of preterm birth, to forecast the outcome of preterm birth and/or prospects of recovery or birth at full term, or to aid in the determination of a suitable treatment for preterm birth.

The quantitation of biomarkers in a biological sample can be determined, without limitation, by the methods described above as well as any other method known in the art. The quantitative data thus obtained is then subjected to an analytic classification process. In such a process, the raw data is manipulated according to an algorithm, where the algorithm has been pre-defined by a training set of data, for example as described in the examples provided herein. An algorithm can utilize the training set of data provided herein, or can utilize the guidelines provided herein to generate an algorithm with a different set of data.

In some embodiments, analyzing a measurable feature to determine the probability for preterm birth in a pregnant female encompasses the use of a predictive model. In further embodiments, analyzing a measurable feature to determine the probability for preterm birth in a pregnant female encompasses comparing said measurable feature with a reference feature. As those skilled in the art can appreciate, such comparison can be a direct comparison to the reference feature or an indirect comparison where the reference feature has been incorporated into the predictive model. In further embodiments, analyzing a measurable feature to determine the probability for preterm birth in a pregnant female encompasses one or more of a linear discriminant analysis model, a support vector machine classification algorithm, a recursive feature elimination model, a prediction analysis of microarray model, a logistic regression model, a CART algorithm, a flex tree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, or a combination thereof. In particular embodiments, the analysis comprises logistic regression.

An analytic classification process can use any one of a variety of statistical analytic methods to manipulate the quantitative data and provide for classification of the sample. Examples of useful methods include linear discriminant analysis, recursive feature elimination, a prediction analysis of microarray, a logistic regression, a CART algorithm, a FlexTree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, machine learning algorithms; etc.

For creation of a random forest for prediction of GAB one skilled in the art can consider a set of k subjects (pregnant women) for whom the gestational age at birth (GAB) is known, and for whom N analytes (transitions) have been measured in a blood specimen taken several weeks prior to birth. A regression tree begins with a root node that contains all the subjects. The average GAB for all subjects can be calculated in the root node. The variance of the GAB within the root node will be high, because there is a mixture of women with different GAB's. The root node is then divided (partitioned) into two branches, so that each branch contains women with a similar GAB. The average GAB for subjects in each branch is again calculated. The variance of the GAB within each branch will be lower than in the root node, because the subset of women within each branch has relatively more similar GAB's than those in the root node. The two branches are created by selecting an analyte and a threshold value for the analyte that creates branches with similar GAB. The analyte and threshold value are chosen from among the set of all analytes and threshold values, usually with a random subset of the analytes at each node. The procedure continues recursively producing branches to create leaves (terminal nodes) in which the subjects have very similar GAB's. The predicted GAB in each terminal node is the average GAB for subjects in that terminal node. This procedure creates a single regression tree. A random forest can consist of several hundred or several thousand such trees.

Classification can be made according to predictive modeling methods that set a threshold for determining the probability that a sample belongs to a given class. The probability preferably is at least 50%, or at least 60%, or at least 70%, or at least 80% or higher. Classifications also can be made by determining whether a comparison between an obtained dataset and a reference dataset yields a statistically significant difference. If so, then the sample from which the dataset was obtained is classified as not belonging to the reference dataset class. Conversely, if such a comparison is not statistically significantly different from the reference dataset, then the sample from which the dataset was obtained is classified as belonging to the reference dataset class.

The predictive ability of a model can be evaluated according to its ability to provide a quality metric, e.g. AUROC (area under the ROC curve) or accuracy, of a particular value, or range of values. Area under the curve measures are useful for comparing the accuracy of a classifier across the complete data range. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest. In some embodiments, a desired quality threshold is a predictive model that will classify a sample with an accuracy of at least about 0.5, at least about 0.55, at least about 0.6, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, or higher. As an alternative measure, a desired quality threshold can refer to a predictive model that will classify a sample with an AUC of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.

As is known in the art, the relative sensitivity and specificity of a predictive model can be adjusted to favor either the selectivity metric or the sensitivity metric, where the two metrics have an inverse relationship. The limits in a model as described above can be adjusted to provide a selected sensitivity or specificity level, depending on the particular requirements of the test being performed. One or both of sensitivity and specificity can be at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.

The raw data can be initially analyzed by measuring the values for each biomarker, usually in triplicate or in multiple triplicates. The data can be manipulated, for example, raw data can be transformed using standard curves, and the average of triplicate measurements used to calculate the average and standard deviation for each patient. These values can be transformed before being used in the models, e.g. log-transformed, Box-Cox transformed (Box and Cox, Royal Stat. Soc., Series B, 26:211-246(1964). The data are then input into a predictive model, which will classify the sample according to the state. The resulting information can be communicated to a patient or health care provider.

To generate a predictive model for preterm birth, a robust data set, comprising known control samples and samples corresponding to the preterm birth classification of interest is used in a training set. A sample size can be selected using generally accepted criteria. As discussed above, different statistical methods can be used to obtain a highly accurate predictive model. Examples of such analysis are provided in Example 2.

In one embodiment, hierarchical clustering is performed in the derivation of a predictive model, where the Pearson correlation is employed as the clustering metric. One approach is to consider a preterm birth dataset as a “learning sample” in a problem of “supervised learning.” CART is a standard in applications to medicine (Singer, Recursive Partitioning in the Health Sciences, Springer (1999)) and can be modified by transforming any qualitative features to quantitative features; sorting them by attained significance levels, evaluated by sample reuse methods for Hotelling's T²statistic; and suitable application of the lasso method. Problems in prediction are turned into problems in regression without losing sight of prediction, indeed by making suitable use of the Gini criterion for classification in evaluating the quality of regressions.

This approach led to what is termed FlexTree (Huang, Proc. Nat. Acad. Sci. U.S.A 101:10529-10534(2004)). FlexTree performs very well in simulations and when applied to multiple forms of data and is useful for practicing the claimed methods. Software automating FlexTree has been developed. Alternatively, LARTree or LART can be used (Turnbull (2005) Classification Trees with Subset Analysis Selection by the Lasso, Stanford University). The name reflects binary trees, as in CART and FlexTree; the lasso, as has been noted; and the implementation of the lasso through what is termed LARS by Efron et al. (2004) Annals of Statistics 32:407-451 (2004). See, also, Huang et al., Proc. Natl. Acad. Sci. USA. 101(29):10529-34 (2004). Other methods of analysis that can be used include logic regression. One method of logic regression Ruczinski, Journal of Computational and Graphical Statistics 12:475-512 (2003). Logic regression resembles CART in that its classifier can be displayed as a binary tree. It is different in that each node has Boolean statements about features that are more general than the simple “and” statements produced by CART.

Another approach is that of nearest shrunken centroids (Tibshirani, Proc. Natl. Acad. Sci. U.S.A 99:6567-72(2002)). The technology is k-means-like, but has the advantage that by shrinking cluster centers, one automatically selects features, as is the case in the lasso, to focus attention on small numbers of those that are informative. The approach is available as PAM software and is widely used. Two further sets of algorithms that can be used are random forests (Breiman, Machine Learning 45:5-32 (2001)) and MART (Hastie, The Elements of Statistical Learning, Springer (2001)). These two methods are known in the art as “committee methods,” that involve predictors that “vote” on outcome.

To provide significance ordering, the false discovery rate (FDR) can be determined. First, a set of null distributions of dissimilarity values is generated. In one embodiment, the values of observed profiles are permuted to create a sequence of distributions of correlation coefficients obtained out of chance, thereby creating an appropriate set of null distributions of correlation coefficients (Tusher et al., Proc. Natl. Acad. Sci. U.S.A 98, 5116-21 (2001)). The set of null distribution is obtained by: permuting the values of each profile for all available profiles; calculating the pair-wise correlation coefficients for all profile; calculating the probability density function of the correlation coefficients for this permutation; and repeating the procedure for N times, where N is a large number, usually 300. Using the N distributions, one calculates an appropriate measure (mean, median, etc.) of the count of correlation coefficient values that their values exceed the value (of similarity) that is obtained from the distribution of experimentally observed similarity values at given significance level.

The FDR is the ratio of the number of the expected falsely significant correlations (estimated from the correlations greater than this selected Pearson correlation in the set of randomized data) to the number of correlations greater than this selected Pearson correlation in the empirical data (significant correlations). This cut-off correlation value can be applied to the correlations between experimental profiles. Using the aforementioned distribution, a level of confidence is chosen for significance. This is used to determine the lowest value of the correlation coefficient that exceeds the result that would have obtained by chance. Using this method, one obtains thresholds for positive correlation, negative correlation or both. Using this threshold(s), the user can filter the observed values of the pair wise correlation coefficients and eliminate those that do not exceed the threshold(s). Furthermore, an estimate of the false positive rate can be obtained for a given threshold. For each of the individual “random correlation” distributions, one can find how many observations fall outside the threshold range. This procedure provides a sequence of counts. The mean and the standard deviation of the sequence provide the average number of potential false positives and its standard deviation.

In an alternative analytical approach, variables chosen in the cross-sectional analysis are separately employed as predictors in a time-to-event analysis (survival analysis), where the event is the occurrence of preterm birth, and subjects with no event are considered censored at the time of giving birth. Given the specific pregnancy outcome (preterm birth event or no event), the random lengths of time each patient will be observed, and selection of proteomic and other features, a parametric approach to analyzing survival can be better than the widely applied semi-parametric Cox model. A Weibull parametric fit of survival permits the hazard rate to be monotonically increasing, decreasing, or constant, and also has a proportional hazards representation (as does the Cox model) and an accelerated failure-time representation. All the standard tools available in obtaining approximate maximum likelihood estimators of regression coefficients and corresponding functions are available with this model.

In addition the Cox models can be used, especially since reductions of numbers of covariates to manageable size with the lasso will significantly simplify the analysis, allowing the possibility of a nonparametric or semi-parametric approach to prediction of time to preterm birth. These statistical tools are known in the art and applicable to all manner of proteomic data. A set of biomarker, clinical and genetic data that can be easily determined, and that is highly informative regarding the probability for preterm birth and predicted time to a preterm birth event in said pregnant female is provided. Also, algorithms provide information regarding the probability for preterm birth in the pregnant female.

Accordingly, one skilled in the art understands that the probability for preterm birth according to the invention can be determined using either a quantitative or a categorical variable. For example, in practicing the methods of the invention the measurable feature of each of N biomarkers can be subjected to categorical data analysis to determine the probability for preterm birth as a binary categorical outcome. Alternatively, the methods of the invention may analyze the measurable feature of each of N biomarkers by initially calculating quantitative variables, in particular, predicted gestational age at birth. The predicted gestational age at birth can subsequently be used as a basis to predict risk of preterm birth. By initially using a quantitative variable and subsequently converting the quantitative variable into a categorical variable the methods of the invention take into account the continuum of measurements detected for the measurable features. For example, by predicting the gestational age at birth rather than making a binary prediction of preterm birth versus term birth, it is possible to tailor the treatment for the pregnant female. For example, an earlier predicted gestational age at birth will result in more intensive prenatal intervention, i.e. monitoring and treatment, than a predicted gestational age that approaches full term.

Among women with a predicted GAB of j days plus or minus k days, p(PTB) can estimated as the proportion of women in the PAPR clinical trial (see Example 1) with a predicted GAB of j days plus or minus k days who actually deliver before 37 weeks gestational age. More generally, for women with a predicted GAB of j days plus or minus k days, the probability that the actual gestational age at birth will be less than a specified gestational age, p(actual GAB<specified GAB), was estimated as the proportion of women in the PAPR clinical trial with a predicted GAB of j days plus or minus k days who actually deliver before the specified gestational age.

In the development of a predictive model, it can be desirable to select a subset of markers, i.e. at least 3, at least 4, at least 5, at least 6, up to the complete set of markers. Usually a subset of markers will be chosen that provides for the needs of the quantitative sample analysis, e.g. availability of reagents, convenience of quantitation, etc., while maintaining a highly accurate predictive model. The selection of a number of informative markers for building classification models requires the definition of a performance metric and a user-defined threshold for producing a model with useful predictive ability based on this metric. For example, the performance metric can be the AUC, the sensitivity and/or specificity of the prediction as well as the overall accuracy of the prediction model.

As will be understood by those skilled in the art, an analytic classification process can use any one of a variety of statistical analytic methods to manipulate the quantitative data and provide for classification of the sample. Examples of useful methods include, without limitation, linear discriminant analysis, recursive feature elimination, a prediction analysis of microarray, a logistic regression, a CART algorithm, a FlexTree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, and machine learning algorithms.

As described in Example 2, various methods are used in a training model. The selection of a subset of markers can be for a forward selection or a backward selection of a marker subset. The number of markers can be selected that will optimize the performance of a model without the use of all the markers. One way to define the optimum number of terms is to choose the number of terms that produce a model with desired predictive ability (e.g. an AUC>0.75, or equivalent measures of sensitivity/specificity) that lies no more than one standard error from the maximum value obtained for this metric using any combination and number of terms used for the given algorithm.

TABLE 1

Transitions with p-values less than 0.05

in univariate Cox Proportional Hazards

analyses to predict Gestational Age

at Birth

p-value

Cox

Transition
Protein
univariate

ITLPDFTGDLR_
LBP_HUMAN
0.006

624.34_920.4

ELLESYIDGR_
THRB_HUMAN
0.006

597.8_710.3

TDAPDLPEENQAR_
CO5_HUMAN
0.007

728.34_613.3

AFTECCVVASQLR_
CO5_HUMAN
0.009

770.87_574.3

SFRPFVPR_
LBP_HUMAN
0.011

335.86_272.2

ITLPDFTGDLR_
LBP_HUMAN
0.012

624.34_288.2

SFRPF_
LBP_HUMAN
0.015

VPR_

335.86_63_5.3

ELLESYIDGR_
THRB_HUMAN
0.018

597.8_839.4

LEQGENVFLQATDK_
C1QB_HUMAN
0.019

796.4_822.4

ETAASLLQAGYK_
THRB_HUMAN
0.021

626.33_679.4

VTGWGNLK_
THRB_HUMAN
0.021

437.74_617.3

EAQLPV1ENK_
PLMN_HUMAN
0.023

570.82_699.4

EAQLP_
PLMN_HUMAN
0.023

VIENK_

570.82_329.1

FLQEQGHR_
CO8G_HUMAN
0.025

338.84_497.3

IRPFFPQQ_
FIBB_HUMAN
0.028

516.79_661.4

ETAASLLQAGYK_
THRB_HUMAN
0.029

626.33_879.5

AFTECCVVASQLR_
CO5_HUMAN
0.030

770.87_673.4

TLLPVSKPEIR_
CO5_HUMAN
0.030

418.26_288.2

LSSPAVITDK_
PLMN_HUMAN
0.033

515.79_743.4

YEVQGEVFTKPQLWP_
CRP_HUMAN
0.036

910.96_392.2

LQGTLPVEAR_
CO5_HUMAN
0.036

542.31_571.3

VRPQQLVK_
ITIH4_HUMAN
0.036

484.31_609.3

IEEIAAK_
CO5_HUMAN
0.041

387.22_531.3

TLLPVSKPEIR_
CO5__HUMAN
0.042

418.26_514.3

VQEAHLTEDQIFYFPK_
CO8G_HUMAN
0.047

655.66_701.4

ISLLLIESWLEPVR_
CSH_HUMAN
0.048

834.49_371.2

ALQDQLVLVAAK_
ANGT_HUMAN
0.048

634.88_289.2

YEFLNGR_
PLMN_HUMAN
0.049

449.72_293.1

TABLE 2

Transitions selected by the Cox stepwise AIC analysis

Transition
coef
exp(coef)
se(coef)
z
Pr(>|z|)

Collection.Window.
1.28E−01
1.14E+00
2.44E−02
5.26
1.40E−07

GA.in.Days

ITLPDFTGDLR_
2.02E+00
7.52E+00
1.14E+00
1.77
0.07667

624.34_920.4

TPSAAYLWVGTGASEAEK
2.85E+01
2.44E+12
3.06E+00
9.31
<2e−16

919.45_849.4

TATSEYQTFFNPR_
5.14E+00
1.70E+02
6.26E−01
8.21
2.20E−16

781.37_386.2

TASDFITK_
−1.25E+00
2.86E−01
1.58E+00
−0.79
0.42856

441.73_781.4

IITGLLEFEVYLEYLQNR_
1.30E+01
4.49E+05
1.45E+00
9
<2e−16

738.4_530.3

IIGGSDADIK_
−6.43E+01
1.16E−28
6.64E+00
−9.68
<2e−16

494.77_762.4

YTTEIIK_
6.96E+01
1.75E+30
7.06E+00
9.86
<2e−16

434.25_603.4

EDTPNSVWEPAK_
7.91E+00
2.73E+03
2.66E+00
2.98
0.00293

686.82_3

15.2

LYYGDDEK_
8.74E+00
6.23E+03
1.57E+00
5.57
2.50E−08

501.72_726.3

VRPQQLVK_
4.64E+01
1.36E+20
3.97E+00
11.66
<2e−16

484.31_609.3

GGEIEGFR_
−3.33E+00
3.57E−02
2.19E+00
−1.52
0.12792

432.71_379.2

DGSPDVTTADIGANTP
−1.52E+01
2.51E−07
1.41E+00
−10.8
<2e−16

DATK_973.45_844.4

VQEAHLTEDQIFYFPK_
−2.02E+01
1.77E−09
2.45E+00
−8.22
2.20E−16

655.66_391.2

VEIDTK_
7.06E+00
1.17E+03
1.45E+00
4.86
1.20E−06

352.7_476.3

AVLTIDEK_
7.85E+00
2.56E+03
9.46E−01
8.29
<2e−16

444.76_605.3

FSVVYAK_
−2.44E+01
2.42E−11
3.08E+00
−7.93
2.20E−15

407.23_579.4

YYLQGAK_
−1.82E+01
1.22E−08
2.45E+00
−7.44
1.00E−13

421.72_516.3

EENFYVDETTVVK_
−1.90E+01
5.36E−09
2.71E+00
−7.03
2.00E−12

786.88_259.1

YGFYTHVFR_
1.90E+01
1.71E+08
2.73E+00
6.93
4.20E−12

397.2_421.3

HTLNQIDEVK_
1.03E+01
3.04E+04
2.11E+00
4.89
9.90E−07

598.82_951.5

AFIQLWAFDAVK_
1.08E+01
4.72E+04
2.59E+00
4.16
3.20E−05

704.89_836.4

SGFSFGFK43_8.72_
1.35E+01
7.32E+05
2.56E+00
5.27
1.40E−07

585.3

GWVTDGFSSLK_
−3.12E+00
4.42E−02
9.16E−01
−3.4
0.00066

598.8_854.4

ITENDIQIALDDAK_
1.91E+00
6.78E+00
1.36E+00
1.4
0.16036

779.9_632.3

TABLE 3

Transitions selected by Cox lasso model

exp
se

Pr

Transition
coef
(coef)
(coef)
z
(>|z|)

Collection.
0.0233
1.02357
0.00928
2.51
0.012

Window.GA.

in.Days

AFTECCVVAS
1.07568
2.93198
0.84554
1.27
0.203

QLR_

770.87_574.3

ELLESYIDGR_
1.3847
3.99365
0.70784
1.96
0.05

597.8_710.3

ITLPDFTGDLR_
0.814
2.25691
0.40652
2
0.045

624.34_920.4

TABLE 4

Area under the ROC (AUROC) curve

for individual analytes to pre-term

birth subjects from non-pre-term

birth subjects. The 77 transitions

discriminate with the highest

AUROC are aare shown.

Transition
AUROC

ELLES_YIDGR_597.8_710.3
0.71

AFTECCWASQLR_770.87_574.3
0.70

ITLPDFTGDLR_624.34_920.4
0.70

IRPFFPQQ_516.79_661.4
0.68

TDAPDLPEENQ_AR_728.34_613.3
0.67

ITLPDFTGDLR_624.34_288.2
0.67

ELLESYIDGR_597.8_839.4
0.67

SFRPFVPR_3_35.86_635.3
0.67

ETAASLLQAGYK_626.33_879.5
0.67

TLLPVSKPEIR_418.26_288.2
0.66

ETAASLLQAGYK_626.33_679.4
0.66

SFRPFVPR_335.86_272.2
0.66

LQGTLP_VEAR542.31_571.3
0.66

VEPLYELVTATDFAYSSTV
0.66

R_754.38_712.4

DPDQTDGLGLSYLSSHIANVE
0.66

R_796.39_328.1

VTGWGNLK_437.74_617.3
0.65

ALQDQLVLVAAK_634.88_289.2
0.65

EAQLPVTENK_570.82_329.1
0.65

VRPQQLVK_484.31_609.3
0.65

AFTECCWASQLR_770.87_673.4
0.65

YEFLNGR_449.72_293.1
0.65

VGEYSLYIGR_578.8_871.5
0.64

EAQLPVIENK_570.82_699.4
0.64

TLLPVSKPEIR_418.26_514.3
0.64

IEEIAAK_387.22_531.3
0.64

LEQGENVFLQATDK_796.4_822.4
0.64

LQGTLPVEAR_542.31_842.5
0.64

FLQEQGHR_338.84_497.3
0.63

ISLLLIESWLEPVR_834.49_371.2
0.63

IITGLLEFEVYLEYLQNR_738.4_530.3
0.63

LSSPAVITDK515.79_743.4
0.63

VRPQQLVK_484.31_722.4
0.63

SLPVSDSVLSGFEQR_810.92_723.3
0.63

VQEAHLTEDQIFYFPK_655.66_701.4
0.63

NADYSYSVWK_616.78_333.2
0.63

DAQYAPGYDK_564.25_813.4
0.62

FQLPGQK_409.23_276.1
0.62

TASDFITK_441.73_781.4
0.62

YGLVTYATYPK_638.33_334.2
0.62

GSFALSFPVESDVAPIAR_931.99_363.2
0.62

TLLIANETLR_572.34_703.4
0.62

VILGAHQEVNLEPFIVQEIEVS
0.62

R_832.78_860.4

TATSEYQTFFNPR_781.37_386.2
0.62

YEVQGEVFTKPQLWP_910.96_392.2
0.62

DISEVVTPR_508.27_472.3
0.62

GSFALSFPVESDVAPIAR_931.99_456.3
0.62

YGFYTHVFR_397.2_421.3
0.62

TLEAQLTPR_514.79_685.4
0.62

YGFYTHVFR_397.2_659.4
0.62

AVGYLITGYQR_620.84_737.4
0.61

DPDQTDGLGLSYLSSHIAN
0.61

VER_796.39_456.2

FNAVLTNPQGDYDTSTGK_964.46_262.1
0.61

SPEQQETVLDGNLIIR_906.48_685.4
0.61

ALNFELPLEYNSALYSR_620.99_538.3
0.61

GGEIEGFR_432.71_508.3
0.61

GIVEECCFR_585.26_900.3
0.61

DAQYAPGYDK_564.25_315.1
0.61

FAFNLYR_465.75_712.4
0.61

YTTEIIK_434.25_603.4
0.61

AVLTIDEK_444.76_605.3
0.61

AITPPHPASQANIIFDITEG
0.60

NLR_825.77_459.3

EPGLCTWQSLR_673.83_790.4
0.60

AVYEAVLR_460.76_587.4
0.60

ALQDQLVLVAAK_634.88_956.6
0.60

AWVAWR_394.71_531.3
0.60

TNLESILSYPK_632.84_807.5
0.60

HLSLLTTLSNR_418.91_376.2
0.60

FTFTLHLETPKPSISSSNLNP
0.60

R_829.44_787.4

AVGYLITGYQR_620.84_523.3
0.60

FQLPGQK_409.23_429.2
0.60

YGLVTYATYPK_638.33_843.4
0.60

TELRPGETLNVNFLLR_624.68_662.4
0.60

LSSPAVITDK_515.79_830.5
0.60

TATSEYQTFFNPR_781.37_272.2
0.60

LPTAVVPLR_483.31_385.3
0.60

APLTKPLK_289.86_260.2
0.60

TABLE 5

AUROCs for random forest, boosting, lasso,

and logistic regression models for a

specific number of transitions permitted

in the model, as estimated by 100 rounds

of bootstrap resampling.

Number of

transitions
rf
boosting
logit
lasso

1
0.59
0.67
0.64
0.69

2
0.66
0.70
0.63
0.68

3
0.69
0.70
0.58
0.71

4
0.68
0.72
0.58
0.71

5
0.73
0.71
0.58
0.68

6
0.72
0.72
0.56
0.68

7
0.74
0.70
0.60
0.67

8
0.73
0.72
0.62
0.67

9
0.72
0.72
0.60
0.67

10
0.74
0.71
0.62
0.66

11
0.73
0.69
0.58
0.67

12
0.73
0.69
0.59
0.66

13
0.74
0.71
0.57
0.66

14
0.73
0.70
0.57
0.65

15
0.72
0.70
0.55
0.64

TABLE 6

Top 15 transitions selected by each

multivariate method, ranked by

importance for that method.

rf
boosting
lasso
logit

1
ELLES
AFTEC
AFTEC
ALQDQ

YIDGR_
CVVAS
CVVAS
LVLVA

597.8_
QLR_
QLR_
AK_

710.3
770.87_
770.87_
634.88_

574.3
574.3
289.2

2
TATSE
DPDQT
ISLLL
AVLTI

YQTFF
DGLGL
IESWL
DEK_

NPR_
SYLSS
EPVR_
444.76_

781.37_
HIANV
834.49_
605.3

386.2
ER_
371.2

796.39_

328.1

3
ITLPD
ELLES
LPTAV
Collection.

FTGDL
YIDGR_
VPLR_
Window.

R_
597.8_
483.31_
GA.in.Days

624.34_
710.3
385.3

920.4

4
AFTEC
TATSE
ALQDQ
AHYDL

CVVAS
YQTFF
LVLVA
R_

QLR_
NPR_
AK_
387.7_

770.87_
781.37_
634.88_
566.3

574.3
386.2
289.2

5
VEPLY
ITLPD
ETAAS
AEAQA

ELVTA
FTGDL
LLQAG
QYSAA

TDFAY
R_
YK_
VAK_

SSTVR_
624.34_
626.33_
654.33_

754.38_
920.4
679.4
908.5

712.4

6
GSFAL
GGEIE
IITGL
AEAQA

SFPVE
GFR_
LEFEV
QYSAA

SDVAP
432.71_
YLEYL
VAK_

IAR_
379.2
QNR_
654.33_

931.99_

738.4_
709.4

363.2

530.3

7
VGEYS
ALQDQ
ADSQA
ADSQA

LYIGR_
LVLVA
QLLLS
QLLLS

578.8_
AK_
TVVGV
TVVGV

871.5
634.88_
FTAPG
FTAPG

289.2
LHLK_
LHLK_

822.46_
822.46_

983.6
983.6

8
SFRPF
VGEYS
SLPVS
AITPP

VPR_
LYIGR_
DSVLS
HPASQ

33586_
578.8_
GFEQR_
ANIIF

635.3
871.5
810.92_
DITEG

723.3
NLR_

825.77_

459.3

9
ALQDQ
VEPLY
SFRPF
ADSQA

LVLVA
ELVTA
VPR_
QLLLS

AK_
TDFAY
335.86_
TVVGV

634.88_
SSTVR_
272.2
FTAPG

289.2
754.38_

LHLK_

712.4

822.46_

664.4

10
EDTPN
SPEQQ
IIGGS
AYSDL

SVWEP
ETVLD
DADIK_
SR_

AK_
GNLII
494.77_
406.2_

686.82_
R_
260.2
375.2

315.2
906.48_

685.4

11
YGFYT
YEFLN
NADYS
DALSS

HVFR_
GR_
YSVWK_
VQESQ

397.2_
449.72_
616.78_
VAQQA

421.3
293.1
333.2
R_

572.96_

672.4

12
DPDQT
LEQGE
GSFAL
ANRPF

DGLGL
NVFLQ
SFPVE
LVFIR_

SYLSS
ATDK_
SDVAP
411.58_

HIANV
796.4_
IAR_
435.3

ER_
822.4
931_

796.39_

99_

328.1

456.3

13
LEQGE
LQGTL
LSSPA
DALSS

NVFLQ
PVEAR_
VITDK_
VQESQ

ATDK_
542.31_
515.79_
VAQQA

796.4_
571.3
743.4
R_

822.4

572.96_

502.3

14
LQGTL
ISLLL
ELPEH
ALEQD

PVEAR_
IESWL
TVK_
LPVNI

542.31_
EPVR_
476.76_
K_

571.3
834.49_
347.2
620.35_

371.2

570.4

15
SFRPF
TASDF
EAQLP
AVLTI

VPR_
ITK_
VIENK_
DEK_

335.86_
441.73_
570.82_
444.76_

272.2
781.4
699.4
718.4

In yet another aspect, the invention provides kits for determining probability of preterm birth, wherein the kits can be used to detect N of the isolated biomarkers listed in Tables 1 through 63. For example, the kits can be used to detect one or more, two or more, or three of the isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. For example, the kits can be used to detect one or more, two or more, or three of the isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

In another aspect, the kits can be used to detect one or more, two or more, three or more, four or more, five or more, six or more, seven or more, or eight of the isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

In another aspect, the kits can be used to detect one or more, two or more, three or more, four or more, five or more, six or more, seven or more, or eight of the isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

The kit can include one or more agents for detection of biomarkers, a container for holding a biological sample isolated from a pregnant female; and printed instructions for reacting agents with the biological sample or a portion of the biological sample to detect the presence or amount of the isolated biomarkers in the biological sample. The agents can be packaged in separate containers. The kit can further comprise one or more control reference samples and reagents for performing an immunoassay.

In one embodiment, the kit comprises agents for measuring the levels of at least N of the isolated biomarkers listed in Tables 1 through 63. The kit can include antibodies that specifically bind to these biomarkers, for example, the kit can contain at least one of an antibody that specifically binds to lipopolysaccharide-binding protein (LBP), an antibody that specifically binds to prothrombin (THRB), an antibody that specifically binds to complement component C5 (C5 or CO5), an antibody that specifically binds to plasminogen (PLMN), and an antibody that specifically binds to complement component C8 gamma chain (C8G or CO8G).

In one embodiment, the kit comprises agents for measuring the levels of at least N of the isolated biomarkers listed in Tables 1 through 63. The kit can include antibodies that specifically bind to these biomarkers, for example, the kit can contain at least one of an antibody that specifically binds to Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

The kit can comprise one or more containers for compositions contained in the kit. Compositions can be in liquid form or can be lyophilized. Suitable containers for the compositions include, for example, bottles, vials, syringes, and test tubes. Containers can be formed from a variety of materials, including glass or plastic. The kit can also comprise a package insert containing written instructions for methods of determining probability of preterm birth.

From the foregoing description, it will be apparent that variations and modifications can be made to the invention described herein to adopt it to various usages and conditions. Such embodiments are also within the scope of the following claims.

The recitation of a listing of elements in any definition of a variable herein includes definitions of that variable as any single element or combination (or subcombination) of listed elements. The recitation of an embodiment herein includes that embodiment as any single embodiment or in combination with any other embodiments or portions thereof.

All patents and publications mentioned in this specification are herein incorporated by reference to the same extent as if each independent patent and publication was specifically and individually indicated to be incorporated by reference.

The following examples are provided by way of illustration, not limitation.

EXAMPLES
Example 1. Development of Sample Set for Discovery and Validation of Biomarkers for Preterm Birth

A standard protocol was developed governing conduct of the Proteomic Assessment of Preterm Risk (PAPR) clinical study. This protocol also specified that the samples and clinical information could be used to study other pregnancy complications for some of the subjects. Specimens were obtained from women at 11 Internal Review Board (IRB) approved sites across the United States. After providing informed consent, serum and plasma samples were obtained, as well as pertinent information regarding the patient's demographic characteristics, past medical and pregnancy history, current pregnancy history and concurrent medications. Following delivery, data were collected relating to maternal and infant conditions and complications. Serum and plasma samples were processed according to a protocol that requires standardized refrigerated centrifugation, aliquoting of the samples into 0.5 ml 2-D bar-coded cryovials and subsequent freezing at −80° C.

Following delivery, preterm birth cases were individually reviewed to determine their status as either a spontaneous preterm birth or a medically indicated preterm birth. Only spontaneous preterm birth cases were used for this analysis. For discovery of biomarkers of preterm birth, 80 samples were analyzed in two gestational age groups: a) a late window composed of samples from 23-28 weeks of gestation which included 13 cases, 13 term controls matched within one week of sample collection and 14 term random controls, and, b) an early window composed of samples from 17-22 weeks of gestation included 15 cases, 15 term controls matched within one week of sample collection and 10 random term controls.

The samples were subsequently depleted of high abundance proteins using the Human 14 Multiple Affinity Removal System (MARS 14), which removes 14 of the most abundant proteins that are treated as uninformative with regard to the identification for disease-relevant changes in the serum proteome. To this end, equal volumes of each clinical or a pooled human serum sample (HGS) sample were diluted with column buffer and filtered to remove precipitates. Filtered samples were depleted using a MARS-14 column (4.6×100 mm, Cat. #5188-6558, Agilent Technologies). Samples were chilled to 4° C. in the autosampler, the depletion column was run at room temperature, and collected fractions were kept at 4° C. until further analysis. The unbound fractions were collected for further analysis.

A second aliquot of each clinical serum sample and of each HGS was diluted into ammonium bicarbonate buffer and depleted of the 14 high and approximately 60 additional moderately abundant proteins using an IgY14-SuperMix (Sigma) hand-packed column, comprised of 10 mL of bulk material (50% slurry, Sigma). Shi et al., Methods, 56(2):246-53 (2012). Samples were chilled to 4° C. in the autosampler, the depletion column was run at room temperature, and collected fractions were kept at 4° C. until further analysis. The unbound fractions were collected for further analysis.

Depleted serum samples were denatured with trifluorethanol, reduced with dithiotreitol, alkylated using iodoacetamide, and then digested with trypsin at a 1:10 trypsin: protein ratio. Following trypsin digestion, samples were desalted on a C18 column, and the eluate lyophilized to dryness. The desalted samples were resolubilized in a reconstitution solution containing five internal standard peptides.

Depleted and trypsin digested samples were analyzed using a scheduled Multiple Reaction Monitoring method (sMRM). The peptides were separated on a 150 mm×0.32 mm Bio-Basic C18 column (ThermoFisher) at a flow rate of 5 μl/min using a Waters Nano Acquity UPLC and eluted using an acetonitrile gradient into a AB SCIEX QTRAP 5500 with a Turbo V source (AB SCIEX, Framingham, Mass.). The sMRM assay measured 1708 transitions that correspond to 854 peptides and 236 proteins. Chromatographic peaks were integrated using Rosetta Elucidator software (Ceiba Solutions).

Transitions were excluded from analysis, if their intensity area counts were less than 10000 and if they were missing in more than three samples per batch. Intensity area counts were log transformed and Mass Spectrometry run order trends and depletion batch effects were minimized using a regression analysis.

Example 2. Analysis I of Transitions to Identify Preterm Birth Biomarkers

The objective of these analyses was to examine the data collected in Example 1 to identify transitions and proteins that predict preterm birth. The specific analyses employed were (i) Cox time-to-event analyses and (ii) models with preterm birth as a binary categorical dependent variable. The dependent variable for all the Cox analyses was Gestational Age of time to event (where event is preterm birth). For the purpose of the Cox analyses, preterm birth subjects have the event on the day of birth. Term subjects are censored on the day of birth. Gestational age on the day of specimen collection is a covariate in all Cox analyses.

The assay data were previously adjusted for run order and depletion batch, and log transformed. Values for gestational age at time of sample collection were adjusted as follows. Transition values were regressed on gestational age at time of sample collection using only controls (non-pre-term subjects). The residuals from the regression were designated as adjusted values. The adjusted values were used in the models with pre-term birth as a binary categorical dependent variable. Unadjusted values were used in the Cox analyses.

Univariate Cox Proportional Hazards Analyses

Univariate Cox Proportional Hazards analyses was performed to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate. Table 1 shows the transitions with p-values less than 0.05. Five proteins have multiple transitions among those with p-value less than 0.05: lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

Multivariate Cox Proportional Hazards Analyses: Stepwise AIC selection

Cox Proportional Hazards analyses was performed to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate, using stepwise and lasso models for variable selection. These analyses include a total of n=80 subjects, with number of PTB events=28. The stepwise variable selection analysis used the Akaike Information Criterion (AIC) as the stopping criterion. Table 2 shows the transitions selected by the stepwise AIC analysis. The coefficient of determination (R²) for the stepwise AIC model is 0.86 (not corrected for multiple comparisons).

Multivariate Cox Proportional Hazards Analyses: Lasso Selection

Lasso variable selection was used as the second method of multivariate Cox Proportional Hazards analyses to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate. This analysis uses a lambda penalty for lasso estimated by cross validation. Table 3 shows the results. The lasso variable selection method is considerably more stringent than the stepwise AIC, and selects only 3 transitions for the final model, representing 3 different proteins. These 3 proteins give the top 4 transitions from the univariate analysis; 2 of the top 4 univariate are from the same protein, and hence are not both selected by the lasso method. Lasso tends to select a relatively small number of variables with low mutual correlation. The coefficient of determination (R²) for the lasso model is 0.21 (not corrected for multiple comparisons).

Univariate AUROC Analysis of Preterm Birth as a Binary Categorical Dependent Variable

Univariate analyses was performed to discriminate pre-term subjects from non-pre-term subjects (pre-term as a binary categorical variable) as estimated by area under the receiver operating characteristic (AUROC) curve. These analyses use transition values adjusted for gestational age at time of sample collection, as described above. Table 4 shows the AUROC curve for the 77 transitions with the highest AUROC area of 0.6 or greater.

Multivariate Analysis of Preterm Birth as a Binary Categorical Dependent Variable

Multivariate analyses was performed to predict preterm birth as a binary categorical dependent variable, using random forest, boosting, lasso, and logistic regression models. Random forest and boosting models grow many classification trees. The trees vote on the assignment of each subject to one of the possible classes. The forest chooses the class with the most votes over all the trees.

For each of the four methods (random forest, boosting, lasso, and logistic regression) each method was allowed to select and rank its own best 15 transitions. We then built models with 1 to 15 transitions. Each method sequentially reduces the number of nodes from 15 to 1 independently. A recursive option was used to reduce the number of nodes at each step: To determine which node to remove, the nodes were ranked at each step based on their importance from a nested cross-validation procedure. The least important node was eliminated. The importance measures for lasso and logistic regression are z-values. For random forest and boosting, the variable importance was calculated from permuting out-of-bag data: for each tree, the classification error rate on the out-of-bag portion of the data was recorded; the error rate was then recalculated after permuting the values of each variable (i.e., transition); if the transition was in fact important, there would have been be a big difference between the two error rates; the difference between the two error rates were then averaged over all trees, and normalized by the standard deviation of the differences. The AUCs for these models are shown in Table 5, as estimated by 100 rounds of bootstrap resampling. Table 6 shows the top 15 transitions selected by each multivariate method, ranked by importance for that method. These multivariate analyses suggest that models that combine 3 or more transitions give AUC greater than 0.7, as estimated by bootstrap.

In multivariate models, random forest (rf), boosting, and lasso models gave the best area under the AUROC curve. The following transitions were selected by these models, as significant in Cox univariate models, and/or having high univariate ROC'S:

AFTECCVVASQLR770.87_574.3

ELLESYIDGR_597.8_710.3

ITLPDFTGDLR_624.34920.4

TDAPDLPEENQAR_728.34613.3

SFRPFVPR_335.86_635.3

In summary, univariate and multivariate Cox analyses was performed using transitions to predict Gestational Age at Birth (GAB), including Gestational age on the day of specimen collection as a covariate. In the univariate Cox analysis, five proteins were identified that have multiple transitions among those with p-value less than 0.05: lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

In multivariate Cox analyses, stepwise AIC variable analysis selects 24 transitions, while the lasso model selects 3 transitions, which include the 3 top proteins in the univariate analysis. Univariate (AUROC) and multivariate (random forest, boosting, lasso, and logistic regression) analyses were performed to predict pre-term birth as a binary categorical variable. Univariate analyses identified 63 analytes with AUROC of 0.6 or greater. Multivariate analyses suggest that models that combine 3 or more transitions give AUC greater than 0.7, as estimated by bootstrap.

Example 3. Study II to Identify and Confirm Preterm Birth Biomarkers

A further study was performed using essentially the same methods described in the preceding Examples unless noted below. In this study, 2 gestational aged matched controls were used for each case of 28 cases and 56 matched controls, all from the early gestational window only (17-22 weeks).

The samples were processed in 4 batches with each batch composed of 7 cases, 14 matched controls and 3 HGS controls. Serum samples were depleted of the 14 most abundant serum samples by MARS14 as described in Example 1. Depleted serum was then reduced with dithiothreitol, alkylated with iodacetamide, and then digested with trypsin at a 1:20 trypsin to protein ratio overnight at 37° C. Following trypsin digestion, the samples were desalted on an Empore C18 96-well Solid Phase Extraction Plate (3M Company) and lyophilized to dryness. The desalted samples were resolubilized in a reconstitution solution containing five internal standard peptides.

The LC-MS/MS analysis was performed with an Agilent Poroshell 120 EC-C18 column (2.1×50 mm, 2.7 μm) and eluted with an acetonitrile gradient into a Agilent 6490 Triple Quadrapole mass spectrometer.

Data analysis included the use of conditional logistic regression where each matching triplet (case and 2 matched controls) was a stratum. The p-value reported in the table indicates whether there is a significant difference between cases and matched controls.

TABLE 7

Results of Study II

Transi-

tion
Protein
Annotation
p-value

DFHIN
CFAB_
Complement
0.006729512

LFQVL
HUMAN
factor B

PWLK

ITLPD
LBP_
Lipopolysaccharide-
0.012907017

FTGDL
HUMAN
binding

R

protein

WWGGQ
ENPP2_
Ectonucleotide
0.013346

PLWIT
HUMAN
pyrophosphatase/

ATK

Phosphodiesterase

family

member 2

TASDF
GELS_
Gelsolin
0.013841221

ITK
HUMAN

AGLLR
PGRP2_
N-acetylmuramoyl-
0.014241979

PDYAL
HUMAN
L-alanine

LGHR

amidase

FLQEQ
CO8G_
Complement
0.014339596

GHR
HUMAN
component C8 gamma

chain

FLNWI
HABP2_
Hyaluronan-binding
0.014790418

K
HUMAN
protein 2

EKPAG
BPIB1_
BPI fold-
0.019027746

GIPVL
HUMAN
containing

GSLVN

family B

TVLK

member 1

ITGFL
LBP_
Lipopolysaccharide-
0.019836986

KPGK
HUMAN
binding protein

YGLVT
CFAB_
Complement
0.019927774

YATYP
HUMAN
factor B

K

SLLQP
CO8A_
Complement
0.020930939

NK
HUMAN
component C8

alpha chain

DISEV
CFAB_
Complement
0.021738046

VTPR
HUMAN
factor B

VQEAH
CO8G_
Complement
0.021924548

LTEDQ
HUMAN
component C8

IFYFP

gamma chain

K

SPELQ
APOA2_
Apolipoprotein
0.025944285

AEAK
HUMAN
A-II

TYLHT
ENPP2_
Ectonucleotide
0.026150038

YESEI
HUMAN
pyrophosphatase/

phosphodiesterase

family

member 2

DSPSV
PROF1_
Profilin-1
0.026607371

WAAVP
HUMAN

GK

HYINL
NPY_
Pro-neuropeptide
0.027432804

ITR
HUMAN
Y

SLPVS
CO8G_
Complement
0.029647857

DSVLS
HUMAN
component C8

GFEQR

gamma chain

IPGIF
CO8B_
Complement
0.030430996

ELGIS
HUMAN
component

SQSDR

C8 beta

chain

IQTHS
F13B_
Coagulation
0.031667664

TTYR
HUMAN
factor XIII

B chain

DGSPD
PGRP2_
N-acetylmuramoyl-
0.034738338

VTTAD
HUMAN
L-alanine amidase

IGANT

PDATK

QLGLP
ITIH4_
Inter-alpha-
0.043130591

GPPDV
HUMAN
trypsin

PDHAA

inhibitor

YHPF

heavy

chain H4

FPLGS
LCAP_
Leucyl-cystinyl
0.044698045

YTIQN
HUMAN
aminopeptidase

IVAGS

TYLFS

TK

AHYDL
FETUA_
Alpha-2-HS-
0.046259201

R
HUMAN
glycoprotein

SFRPF
LBP_
Lipopolysaccharide-
0.047948847

VPR
HUMAN
binding protein

Example 4. Study III Shotgun Identification of Preterm Birth Biomarkers

A further study used a hypothesis-independent shotgun approach to identify and quantify additional biomarkers not present on our multiplexed hypothesis dependent MRM assay. Samples were processed as described in the preceding Examples unless noted below.

Tryptic digests of MARS depleted patient (preterm birth cases and term controls) samples were fractionated by two-dimensional liquid chromatography and analyzed by tandem mass spectrometry. Aliquots of the samples, equivalent to 3-4 μl of serum, were injected onto a 6 cm×75 μm self-packed strong cation exchange (Luna SCX, Phenomenex) column. Peptides were eluded from the SCX column with salt (15, 30, 50, 70, and 100% B, where B=250 mM ammonium acetate, 2% acetonitrile, 0.1% formic acid in water) and consecutively for each salt elution, were bound to a 0.5 μl C18 packed stem trap (Optimize Technologies, Inc.) and further fractionated on a 10 cm×75 μm reversed phase ProteoPep II PicoFrit column (New Objective). Peptides were eluted from the reversed phase column with an acetonitrile gradient containing 0.1% formic acid and directly ionized on an LTQ-Orbitrap (ThermoFisher). For each scan, peptide parent ion masses were obtained in the Orbitrap at 60K resolution and the top seven most abundant ions were fragmented in the LTQ to obtain peptide sequence information.

Parent and fragment ion data were used to search the Human RefSeq database using the Sequest (Eng et al., J. Am. Soc. Mass Spectrom 1994; 5:976-989) and X! Tandem (Craig and Beavis, Bioinformatics 2004; 20:1466-1467) algorithms. For Sequest, data was searched with a 20 ppm tolerance for the parent ion and 1 AMU for the fragment ion. Two missed trypsin cleavages were allowed, and modifications included static cysteine carboxyamidomethylation and methionine oxidation. After searching the data was filtered by charge state vs. Xcorr scores (charge+1≥1.5 Xcorr, charge+2≥2.0, charge+3≥2.5). Similar search parameters were used for X!tandem, except the mass tolerance for the fragment ion was 0.8 AMU and there is no Xcorr filtering. Instead, the PeptideProphet algorithm (Keller et al., Anal. Chem 2002; 74:5383-5392) was used to validate each X!Tandem peptide-spectrum assignment and Protein assignments were validated using ProteinProphet algorithm (Nesvizhskii et al., Anal. Chem 2002; 74:5383-5392). Data was filtered to include only the peptide-spectrum matches that had PeptideProphet probability of 0.9 or more. After compiling peptide and protein identifications, spectral count data for each peptide were imported into DAnTE software (Polpitiya et al., Bioinformatics. 2008; 24:1556-1558). Log transformed data was mean centered and missing values were filtered, by requiring that a peptide had to be identified in at least 4 cases and 4 controls. To determine the significance of an analyte, Receiver Operating Characteristic (ROC) curves for each analyte were created where the true positive rate (Sensitivity) is plotted as a function of the false positive rate (1-Specificity) for different thresholds that separate the SPTB and Term groups. The area under the ROC curve (AUC) is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. Peptides with AUC greater than or equal to 0.6 found uniquely by Sequest or Xtandem are found in Tables 8 and 9, respectively, and those identified by both approaches are found in Table 10.

TABLE 8

Significant peptides (AUC > 0.6)

for Sequest only

Uniprot

ID

S_

Protein Description
(name)
Peptide
AUC

5'-AMP-activated
Q9UGI9
K.LVIFDTM*L
0.78

protein kinase
(AAKG3_
EIK.K

subunit gamma-3
HUMAN)

afamin precursor
P43652
K.FIEDNIEYIT
0.79

(AFAM_
IIAFAQYVQEAT

HUMAN)
FEEMEK.L

afamin precursor
P43652
K.IAPQLSTEEL
0.71

(AFAM_
VSLGEK.M

HUMAN)

afamin precursor
P43652
K.LKHELTDEE
0.60

(AFAM_
LQSLFTNFANV

HUMAN)
VDK.C

afamin precursor
P43652
K.LPNNVLQEK.I
0.60

(AFAM_

HUMAN)

afamin precursor
P43652
K.SDVGFLPPFPT
0.71

(AFAM_
LDPEEK.C

HUMAN)

afamin precursor
P43652
K.VMNHICSK.Q
0.68

(AFAM_

HUMAN)

afamin precursor
P43652
R.ESLLNHFLY
0.69

(AFAM_
EVAR.R

HUMAN)

afamin precursor
P43652
R.LCFFYNKK.S
0.69

(AFAM_

HUMAN)

alpha-1-
P01011
K.AVLDVFEEGT
0.72

antichymotrypsin
(AACT_
EASAATAVK.I

HUMAN)

alpha-1-
P01011
K.EQLSLLDR.F
0.65

antichymotrypsin
(AACT_

precursor
HUMAN)

alpha-1-
P01011
K.EQLSLLDRFTE
0.64

antichymotrypsin
(AACT_
DAK.R

precursor
HUMAN)

alpha-1-
P01011
K.EQLSLLDRFT
0.60

antichymotrypsin
(AACT_
EDAKR.L

precursor
HUMAN)

alpha-1-
P01011
K.ITDLIKDLDSQT
0.65

antichymotrypsin
(AACT_
MM*VLVNYIFFK.A

precursor
HUMAN)

alpha-1-
P01011
K.ITLLSALVET
0.62

antichymotrypsin
(AACT_
R.T

precursor
HUMAN)

alpha-1-
P01011
K.RLYGSEAFATDF
0.62

antichymotrypsin
(AACT_
QDSAAAK.K

precursor
HUMAN)

alpha-1-
P01011
R.EIGELYLPK.F
0.65

antichymotrypsin
(AACT_

precursor
HUMAN)

alpha-lB-
P04217
R.CEGPIPDVTFE
0.67

glycoprotein
(A1BG_
LLR.E

precursor
HUMAN)

alpha-lB-
P04217
R.FALVR.E
0.79

glycoprotein
(A1BG_

precursor
HUMAN)

alpha-2-
P08697
K.SPPGVCSR.D
0.81

antiplasmin
(A2AP_

isoform a
HUMAN)

precursor

alpha-2-
P08697
R.DSFHLDEQFTV
0.69

antiplasmin
(A2AP_
PVEMMQAR.T

isoform a
HUMAN)

precursor

alpha-2-HS-
P02765
K.CNLLAEK.Q
0.67

glycoprotein
(FETUA_

preproprotein
HUMAN)

alpha-2-HS-
P02765
K.EHAVEGDCDFQ
0.67

glycoprotein
(FETUA_
LLK.L

preproprotein
HUMAN)

alpha-2-HS-
P02765
K.HTLNQIDEVKV
0.64

glycoprotein
(FETUA_
WPQQPSGELFEIE

preproprotein
HUMAN)
IDTLETTCHVLDP

TPVAR.C

alpha-2-
P01023
K.MVSGFIPLK
0.73

macroglobulin
(A2MG_
PTVK.M

precursor
HUMAN)

alpha-2-
P01023
R.AFQPFFVELT
0.68

macroglobulin
(A2MG_
M*PYSVIR.G

precursor
HUMAN)

alpha-2-
P01023
R.AFQPFFVEL
0.62

macroglobulin
(A2MG_
TMPYSVIR.G

precursor
HUMAN)

alpha-2-
P01023
R.NQGNTWLTA
0.73

macroglobulin
(A2MG_
FVLK.T

precursor
HUMAN)

angiotensinogen
P01019
K.IDRFMQAVT
0.81

preproprotein
(ANGT_
GWK.T

HUMAN)

angiotensinogen
P01019
K.LDTEDKLR.A
0.72

preproprotein
(ANGT_

HUMAN)

angiotensinogen
P01019
K.TGCSLMGASV
0.64

preproprotein
(ANGT_
DSTLAF

HUMAN)
NTYVHFQGK.M

angiotensinogen
P01019
R.AAMVGMLANF
0.62

preproprotein
(ANGT_
LGFR.I

HUMAN)

antithrombin-III
P01008
K.NDNDNIFLS
0.64

precursor
(ANT3_
PLSIST

HUMAN)
AFAMTK.L

antithrombin-III
P01008
K.SKLPGIVA
0.81

precursor
(ANT3_
EGRDDLY

HUMAN)
VSDAFHK.A

antithrombin-III
P01008
R.EVPLNTIIF
0.61

precursor
(ANT3_
MGR.V

HUMAN)

antithrombin-III
P01008
R.FATTFYQHL
0.66

precursor
(ANT3_
ADSKNDNDNIF

HUMAN)
LSPLSISTAFA

MTK.L

antithrombin-III
P01008
R.ITDVIPSE
0.60

precursor
(ANT3_
AINELTVL

HUMAN)
VLVNTIYFK.G

antithrombin-III
P01008
R.RVWELSK.A
0.63

precursor
(ANT3_

HUMAN)

antithrombin-III
P01008
R.VAEGTQVLELP
0.62

precursor
(ANT3_
FKGDDITM*VLIL

HUMAN)
PKPEK.S

antithrombin-III
P01008
R.VAEGTQVLELP
0.62

precursor
(ANT3_
FKGDDITMVLILP

HUMAN)
KPEK.S

apolipoprotein A-II
P02652
K.AGTELVNFLSY
0.61

preproprotein
(APOA2_
FVELGTQPATQ.-

HUMAN)

apolipoprotein A-II
P02652
K.EPCVESLVSQY
0.63

preproprotein
(APOA2_
FQTVTDYGK.D

HUMAN)

apolipoprotein A-IV
P06727
K. ALVQQMEQLR.Q
0.61

precursor
(APOA4_

HUMAN)

apolipoprotein A-IV
P06727
K.LGPHAGDVEGH
0.61

precursor
(APOA4_
LSFLEK.D

HUMAN)

apolipoprotein A-IV
P06727
K.SELTQQLNAL
0.71

precursor
(APOA4_
FQDK.L

HUMAN)

apolipoprotein A-IV
P06727
K.SLAELGGHLD
0.61

precursor
(APOA4_
QQVEEFRR.R

HUMAN)

apolipoprotein A-IV
P06727
K.VKIDQTVEEL
0.75

precursor
(APOA4_
RR.S

HUMAN)

apolipoprotein A-IV
P06727
K.VNSFFSTFK.E
0.63

precursor
(APOA4_

HUMAN)

apolipoprotein
P04114
K.ATFQTPDFIVP
0.65

B-100
(APOB_
LTDLR.I

precursor
HUMAN)

apolipoprotein
P04114
K.AVSM*PSFSIL
0.65

B-100
(APOB_
GSDVR.V

precursor
HUMAN)

apolipoprotein
P04114
K.AVSMPSFSILG
0.67

B-100
(APOB_
SDVR.V

precursor
HUMAN)

apolipoprotein
P04114
K.EQHLFLPFSY
0.65

B-100
(APOB_
K.N

precursor
HUMAN)

apolipoprotein
P04114
K.KIISDYHQQF
0.63

B-100
(APOB_
R.Y

precursor
HUMAN)

apolipoprotein
P04114
K.QVFLYPEKDEPT
0.64

B-100
(APOB_
YILNIK.R

precursor
HUMAN)

apolipoprotein
P04114
K.SPAFTDLHLR.Y
0.69

B-100
(APOB_

precursor
HUMAN)

apolipoprotein
P04114
K.TILGTMPAFEVS
0.62

B-100
(APOB_
LQALQK.A

precursor
HUMAN)

apolipoprotein
P04114
K.VLADKFIIPGL
0.72

B-100
(APOB_
K.L

precursor
HUMAN)

apolipoprotein
P04114
K.YSQPEDSLIPFF
0.61

B-100
(APOB_
EITVPESQLTVSQF

precursor
HUMAN)
TLPK.S

apolipoprotein
P04114
R.DLKVEDIPLA
0.64

B-100
(APOB_
R.I

precursor
HUMAN)

apolipoprotein
P04114
R.GIISALLVPPE
0.81

B-100
(APOB_
TEEAK.Q

precursor
HUMAN)

apolipoprotein
P04114
R.ILGEELGFASL
0.62

B-100
(APOB_
HDLQLLGK.L

precursor
HUMAN)

apolipoprotein
P04114
R.LELELRPTGEI
0.60

B-100
(APOB_
EQYSVSATYELQ

precursor
HUMAN)
R.E

apolipoprotein
P04114
R.NIQEYLSILT
0.68

B-100
(APOB_
DPDGK.G

precursor
HUMAN)

apolipoprotein
P04114
R.TFQIPGYTVPV
0.75

B-100
(APOB_
VNVEVSPFTIEMS

precursor
HUMAN)
AFGYVFPK.A

apolipoprotein
P04114
R.TIDQMLNSELQ
0.70

B-100
(APOB_
WPVPDIYLR.D

precursor
HUMAN)

apolipoprotein
P02654
K.MREWFSETFQ
0.61

C-I
(APOC1_
K.V

precursor
HUMAN)

apolipoprotein
P02655
K.STAAMSTYTGI
0.61

C-II
(APOC2_
FTDQVLSVLKGE

precursor
HUMAN)
E.-

apolipoprotein
P02656
R.GWVTDGFSSL
0.62

C-III
(APOC3_
K.D

precursor
HUMAN)

apolipoprotein
P02649
R.AATVGSLAGQP
0.61

E
(APOE_
LQER.A

precursor
HUMAN)

apolipoprotein
P02649
R.LKSWFEPLVED
0.65

E
(APOE_
MQR.Q

precursor
HUMAN)

apolipoprotein
P02649
R.WVQTLSEQVQE
0.64

E
(APOE_
ELLSSQVTQELR.A

precursor
HUMAN)

ATP-binding
O14678
K.LCGGGRWELM*
0.60

cassette
(ABCD4_
R.I

sub-family
HUMAN)

D member

4

ATP-binding
Q9NUQ8
K.LPGLLK.R
0.73

cassette
(ABCF3_

sub-family
HUMAN)

F member 3

beta-2-
P02749
K.EHSSLAFWK.T
0.64

glycoprotein 1
(APOH_

precursor
HUMAN)

beta-2-
P02749
R.TCPKPDDLPFS
0.60

glycoprotein
(APOH_
TVVPLK.T

1
HUMAN)

precursor

beta-2-
P02749
R.VCPFAGILENG
0.68

glycoprotein
(APOH_
AVR.Y

1
HUMAN)

precursor

beta-Ala-Flis
Q96KN2
K.LFAAFFLEMAQ
0.68

dipeptidase
(CNDP1_
LH.-

precursor
HUMAN)

biotinidase
P43251
K.SHLIIAQVAK.
0.62

precursor
(BTD_
N

HUMAN)

carboxypeptidase
Q96IY4
K.NAIWIDCGIHA
0.62

B2
(CBPB2_
R.E

preproprotein
HUMAN)

carboxypeptidase
P15169
R.EALIQFLEQVH
0.69

N
(CBPN_
QGIK.G

catalytic
HUMAN)

chain

precursor

carboxypeptidase N
P22792
R.LLNIQTYCAGP
0.62

subunit 2
(CPN2_
AYLK.G

precursor
HUMAN)

catalase
P04040
R.LCENIAGHLKD
0.62

(CATA_
AQIFIQK.K

HUMAN)

ceruloplasmin
P00450
K.AETGDKVYVHL
0.61

precursor
(CERU_
K.N

HUMAN)

ceruloplasmin
P00450
K.AGLQAFFQVQE
0.62

precursor
(CERU_
CNK.S

HUMAN)

ceruloplasmin
P00450
K.DIASGLIGPLI
0.63

precursor
(CERU_
ICK.K

HUMAN)

ceruloplasmin
P00450
K.DIFTGLIGPM*
0.63

precursor
(CERU_
K.I

HUMAN)

ceruloplasmin
P00450
K.DIFTGLIGPM
0.68

precursor
(CERU_
K.I

HUMAN)

ceruloplasmin
P00450
K.M*YYSAVDPTK
0.62

precursor
(CERU_
DIFTGLIGPMK.I

HUMAN)

ceruloplasmin
P00450
K.MYYSAVDPTKD
0.63

precursor
(CERU_
IFTGLIGPM*K.I

HUMAN)

ceruloplasmin
P00450
K.PVWLGFLGPII
0.63

precursor
(CERU_
K.A

HUMAN)

ceruloplasmin
P00450
R.ADDKVYPGEQY
0.64

precursor
(CERU_
TYMLLATEEQSPG

HUMAN)
EGDGNCVTR.I

ceruloplasmin
P00450
R.DTANLFPQTSL
0.71

precursor
(CERU_
TLHM*WPDTEGTF

HUMAN)
NVECLTTDHYTGG

MK.Q

ceruloplasmin
P00450
R.DTANLFPQTSL
0.68

precursor
(CERU_
TLHMWPDTEGTFN

HUMAN)
VECLTTDHYTGGM

K.Q

ceruloplasmin
P00450
R.FNKNNEGTYYS
0.74

precursor
(CERU_
PNYNPQSR.S

HUMAN)

ceruloplasmin
P00450
R.IDTINLFPATL
0.75

precursor
(CERU_
FDAYM*VAQNPGE

HUMAN)
WM*LSCQNLNHLK

.A

ceruloplasmin
P00450
R.IDTINLFPATL
0.86

precursor
(CERU_
FDAYM*VAQNPGE

HUMAN)
WMLSCQNLNHLK.

A

ceruloplasmin
P00450
R.IDTINLFPATL
0.60

precursor
(CERU_
FDAYMVAQNPGEW

HUMAN)
M*LSCQNLNHLK.

A

ceruloplasmin
P00450
R.KAEEEHLGILG
0.71

precursor
(CERU_
PQLHADVGDKVK.

HUMAN)
I

ceruloplasmin
P00450
R.TTIEKPVWLGF
0.63

precursor
(CERU_
LGPIIK.A

HUMAN)

cholinesterase
P06276
R.FWTSFFPK.V
0.76

precursor
(CHLE_

HUMAN)

clusterin
P10909
K.LFDSDPITVTV
0.78

preproprotein
(CLUS_
PVEVSR.K

HUMAN)

clusterin
P10909
R.ASSIIDELFQD
0.68

preproprotein
(CLUS_
R.F

HUMAN)

coagulation
P00740
K.WIVTAAHCVET
0.60

factor
(FA9_
GVK.I

IX
HUMAN)

preproprotein

coagulation
P08709
R.FSLVSGWGQLL
0.78

factor
(FA7_
DR.G

VII
HUMAN)

isoform

a

preproprotein

coagulation
P00742
K.ETYDFDIAVLR
0.75

factor
(FA10_
.L

X
HUMAN)

preproprotein

coiled-coil
Q8IYE1
K.VRQLEMEIGQ.
0.67

domain-
(CCD13_
LNVHYLR.N

containing
HUMAN)

protein

13

complement
P02745
R.PAFSAIR.R
0.66

C1q
(C1QA_

subcomponent
HUMAN)

subunit

A

precursor

complement
P02746
K.VVTFCDYAYNT
0.63

C1q
(C1QB_
FQVTTGGMVLK.L

subcomponent
HUMAN)

subunit

B

precursor

complement
P02747
K.FQSVFTVTR.Q
0.63

C1q
(C1QC_

subcomponent
HUMAN)

subunit

C

precursor

complement
P00736
K.TLDEFTIIQNL
0.62

C1r
(C1R_
QPQYQFR.D

subcomponent
HUMAN)

precursor

complement
P00736
R.MDVFSQNMFCA
0.68

C1r
(C1R_
GHPSLK.Q

subcomponent
HUMAN)

precursor

complement
P00736
R.WILTAAHTLYP
0.74

C1r
(C1R_
K.E

subcomponent
HUMAN)

precursor

complement C1s
P09871
K.FYAAGLVSWGP
0.68

subcomponent
(C1S_
Q.CGTYGLYTR.V

precursor
HUMAN)

complement C1s
P09871
K.GFQVVVTLR.R
0.63

subcomponent
(C1S_

precursor
HUMAN)

complement C2
P06681
R.GALISDQWVLT
0.61

isoform 3
(CO2_
AAHCFR.D

HUMAN)

complement C2
P06681
R.PICLPCTMEAN
0.66

isoform 3
(CO2_
LALR.R

HUMAN)

complement C3
P01024
R.YYGGGYGSTQA
0.75

precursor
(CO3_
TFMVFQALAQYQK

HUMAN)
.D

complement C4-A
P0COL4
K.GLCVATPVQLR
0.74

isoform 1
(CO4A_
.V

HUMAN)

complement C4-A
P0COL4
K.M*RPSTDTITV
0.83

isoform 1
(CO4A_
M*VENSHGLR.V

HUMAN)

complement C4-A
P0COL4
K.MRPSTDTITVM
0.72

isoform 1
(CO4A_
*VENSHGLR.V

HUMAN)

complement C4-A
P0COL4
K.VGLSGM*AIAD
0.71

isoform 1
(CO4A_
VTLLSGFHALR.A

HUMAN)

complement C4-A
P0COL4
K.VLSLAQEQVGG
0.63

isoform 1
(CO4A_
SPEK.L

HUMAN)

complement C4-A
P0COL4
R.EMSGSPASGIP
0.65

isoform 1
(CO4A_
VK.V

HUMAN)

complement C4-A
P0COL4
R.GCGEQTM*IYL
0.75

isoform 1
(CO4A_
APTLAASR.Y

HUMAN)

complement C4-A
P0COL4
R.GLQDEDGYR.M
0.75

isoform 1
(CO4A_

HUMAN)

complement C4-A
P0COL4
R.GQIVFMNREP
0.93

isoform 1
(CO4A_
K.R

HUMAN)

complement C4-A
P0COL4
R.KKEVYM*PSSI
0.72

isoform 1
(CO4A_
FQDDFVIPDISEP

HUMAN)
GTWK.I

complement C4-A
P0COL4
R.LPMSVR.R
0.78

isoform 1
(CO4A_

HUMAN)

complement C4-A
P0COL4
R.LTVAAPPSGGP
0.84

isoform 1
(CO4A_
GFLSIER.P

HUMAN)

complement C4-A
P0COL4
R.NFLVR.A
0.75

isoform 1
(CO4A_

HUMAN)

complement C4-A
P0COL4
R.NGESVKLHLET
0.88

isoform 1
(CO4A_
DSLALVALGALDT

HUMAN)
ALYAAGSK.S

complement C4-A
P0COL4
R.QGSFQ.GGFR.
0.60

isoform 1
(CO4A_
S

HUMAN)

complement C4-A
P0COL4
R.TLEIPGNSDPN
0.69

isoform 1
(CO4A_
MIPDGDFNSYVR.

HUMAN)
V

complement C4-A
P0COL4
R.VTASDPLDTLG
0.63

isoform 1
(CO4A_
SEGALSPGGVASL

HUMAN)
LR.L

complement C4-A
P0COL4
R.YLDKTEQWSTL
0.67

isoform 1
(CO4A_
PPETK.D

HUMAN)

complement C5
P01031
K.ADNFLLENTLP
0.63

preproprotein
(CO5_
AQSTFTLAISAYA

HUMAN)
LSLGDK.T

complement C5
P01031
K.ALVEGVDQLFT
0.63

preproprotein
(CO5_
DYQIK.D

HUMAN)

complement C5
P01031
K.DGHVILQLNSI
0.62

preproprotein
(CO5_
PSSDFLCVR.F

HUMAN)

complement C5
P01031
K.DVFLEMNIPYS
0.63

preproprotein
(CO5_
VVR.G

HUMAN)

complement C5
P01031
K.EFPYRIPLDLV
0.60

preproprotein
(CO5_
PK.T

HUMAN)

complement C5
P01031
K.FQNSAILTIQP
0.67

preproprotein
(CO5_
K.Q

HUMAN)

complement C5
P01031
K.VFKDVFLEMNI
0.63

preproprotein
(CO5_
PYSVVR.G

HUMAN)

complement C5
P01031
R.VFQFLEK.S
0.61

preproprotein
(CO5_

HUMAN)

complement
P13671
K.DLHLSDVFLK.
0.60

component C6
(CO6_
A

precursor
HUMAN)

complement
P13671
R.TECIKPVVQEV
0.62

component C6
(CO6_
LTITPFQR.L

precursor
HUMAN)

complement
P10643
K.SSGWHFVVK.F
0.61

component C7
(CO7_

precursor
HUMAN)

complement
P10643
R.ILPLTVCK.M
0.75

component C7
(CO7_

precursor
HUMAN)

complement
P07357
R.ALDQYLMEFNA
0.65

component
(CO8A_
CR.C

C8 alpha
HUMAN)

chain

precursor

complement
P07360
K.YGFCEAADQFH
0.60

component C8
(CO8G_
VLDEVR.R

gamma chain
HUMAN)

precursor

complement
P02748
R.AIEDYINEFSV
0.69

component C9
(CO9_
RK.C

precursor
HUMAN)

complement
P02748
R.TAGYGINILGM
0.69

component C9
(CO9_
DPLSTPFDNEFYN

precursor
HUMAN)
GLCNR.D

complement
P00751
K.ALFVSEEEKK.
0.64

factor B
(CFAB_
L

preproprotein
HUMAN)

complement
P00751
K.CLVNLIEK.V
0.70

factor B
(CFAB_

HUMAN)

preproprotein

complement
P00751
K.EAGIPEFYDYD
0.66

factor B
(CFAB_
VALIK.L

preproprotein
HUMAN)

complement
P00751
K.VSEADSSNADW
0.73

factor B
(CFAB_
VTK.Q

preproprotein
HUMAN)

complement
P00751
K.YGQTIRPICLP
0.67

factor B
(CFAB_
CTEGTTR.A

preproprotein
HUMAN)

complement
P00751
R.DLEIEVVLFHP
0.71

factor B
(CFAB_
NYNINGK.K

preproprotein
HUMAN)

complement
P00751
R.FLCTGGVSPYA
0.64

factor B
(CFAB_
DPNTCR.G

preproprotein
HUMAN)

complement
P08603
K.DGWSAQPTCI
0.80

factor H
(CFAH_
K.S

isoform a
HUMAN)

precursor

complement
P08603
K.EGWIHTVCING
0.67

factor H
(CFAH_
R.W

isoform a
HUMAN)

precursor

complement
P08603
K.TDCLSLPSFEN
0.61

factor H
(CFAH_
AIPMGEK.K

isoform a
HUMAN)

precursor

complement
P08603
R.DTSCVNPPTVQ
0.60

factor H
(CFAH_
NAYIVSR.Q

isoform a
HUMAN)

precursor

complement
P08603
K.CTSTGWIPAP
0.68

factor H
(CFAH_
R.C

isoform b
HUMAN)

precursor

complement
P08603
K.IIYKENER.F
0.76

factor H
(CFAH_

isoform b
HUMAN)

precursor

complement
P08603
K.IVSSAM*EPDR
0.75

factor H
(CFAH_
EYHFGQAVR.F

isoform b
HUMAN)

precursor

complement
P08603
K.IVSSAMEPDRE
0.68

factor H
(CFAH_
YHFGQAVR.F

isoform b
HUMAN)

precursor

complement
P08603
R.CTLKPCDYPDI
0.81

factor H
(CFAH_
K.H

isoform b
HUMAN)

precursor

complement
P08603
R.KGEWVALNPL
0.60

factor H
(CFAH_
R.K

isoform b
HUMAN)

precursor

complement
P08603
R.KGEWVALNPLR
0.69

factor H
(CFAH_
K.C

isoform b
HUMAN)

precursor

complement
P08603
R.RPYFPVAVGK.
0.68

factor H
(CFAH_
Y

isoform b
HUMAN)

precursor

complement
Q03591
R.EIMENYNIAL
0.64

factor
(FHR1_
R.W

H-related
HUMAN)

protein 1

precursor

complement
P05156
K.DASGITCGGIY
0.71

factor 1
(CFAI_
IGGCWILTAAHCL

preproprotein
HUMAN)
R.A

complement
P05156
K.VANYFDWISYH
0.72

factor 1
(CFAI_
VGR.P

preproprotein
HUMAN)

complement
P05156
R.IIFHENYNAGT
0.63

factor 1
(CFAI_
YQNDIALIEMK.K

preproprotein
HUMAN)

complement
P05156
R.YQIWTTVVDWI
0.63

factor 1
(CFAI_
HPDLK.R

preproprotein
HUMAN)

conserved
Q9Y2V7
K.ISNLLK.F
0.65

oligomeric
(COG6_

Golgi complex
HUMAN)

subunit 6

isoform

corticosteroid-
P08185
R.WSAGLTSSQVD
0.62

binding
(CBG_
LYIPK.V

globulin
HUMAN)

precursor

C-reactive
P02741
K.YEVQGEVFTKP
0.60

protein
(CRP_
QLWP.-

precursor
HUMAN)

dopamine
P09172
R.HVLAAWALG
0.88

beta-
(DOPO_
AK.A

hydroxylase
HUMAN)

precursor

double-
Q9INS39
R.AGLRYVCLAEP
0.75

stranded
(RED2_
AER.R

RNA-specific
HUMAN)

editase

B2

dual
Q9NRD8
R.FTQLCVKGGGG
0.65

oxidase 2
(DUOX2_
GGNGIR.D

precursor
HUMAN)

FERM domain-
Q9BZ67
R.VQLGPYQPGRP
0.65

containing
(FRMD8_
AACDLR.E

protein 8
HUMAN)

fetuin-B
Q9UGM5
R.GGLGSLFYLTL
0.83

precursor
(FETUB_
DVLETDCHVLR.K

HUMAN)

ficolin-3
075636
R.ELLSQGATLSG
0.69

isoform 1
(FCN3_
WYHLCLPEGR.A

precursor
HUMAN)

gastric
P27352
K.KTTDM*ILNEI
0.60

intrinsic
(IF_
KQGK.F

factor
HUMAN)

precursor

gelsolin
P06396
K.NWRDPDQTDGL
0.72

isoform d
(GELS_
GLSYLSSHIANVE

HUMAN)
R.V

gelsolin
P06396
K.TPSAAYLWVGT
0.80

isoform d
(GELS_
GASEAEK.T

HUMAN)

gelsolin
P06396
R.VEKFDLVPVPT
0.60

isoform d
(GELS_
NLYGDFFTGDAYV

HUMAN)
ILK.T

gelsolin
P06396
R.VPFDAATLHT
0.67

isoform d
(GELS_
STAM AAQHGM D

HUMAN)
DDGTGQK.Q

glutathione
P22352
K.FYTFLK.N
0.63

peroxidase 3
(GPX3_

precursor
HUMAN)

hemopexin
P02790
K.GDKVWVYPPEK
0.65

precursor
(HEMO_
K.E

HUMAN)

hemopexin
P02790
K.LLQDEFPGIPS
0.71

precursor
(HEMO_
PLDAAVECHR.G

HUMAN)

hemopexin
P02790
K.SGAQATWTELP
0.64

precursor
(HEMO_
WPHEK.V

HUMAN)

hemopexin
P02790
K.SGAQATWTELP
0.61

precursor
(HEMO_
WPHEKVDGALCME

HUMAN)
K.S

hemopexin
P02790
K.VDGALCMEK.S
0.66

precursor
(HEMO_

HUMAN)

hemopexin
P02790
R.DYFMPCPGR.G
0.68

precursor
(HEMO_

HUMAN)

hemopexin
P02790
R.EWFWDLATGTM
0.64

precursor
(HEMO_
*K.E

HUMAN)

hemopexin
P02790
R.QGHNSVFLIK.
0.71

precursor
(HEMO_
G

HUMAN)

heparin
P05546
K.HQGTITVN E
0.60

cofactor 2
(HEP2_
EGTQATTVTTVG

precursor
HUMAN)
FMPLSTQVR.F

heparin
P05546
K.YEITTIHNLF
0.62

cofactor 2
(HEP2_
R.K

precursor
HUMAN)

heparin cofactor 2
P05546
R.LNILNAK.F
0.68

precursor
(HEP2_

HUMAN)

heparin cofactor 2
P05546
R.NFGYTLR.S
0.64

precursor
(HEP2_

HUMAN)

heparin cofactor 2
P05546
R.VLKDQVNTFDN
0.63

precursor
(HEP2_
IFIAPVGISTAMG

HUMAN)
M*ISLGLK.G

hepatocyte cell
Q14CZ8
K.PLLNDSRMLLS
0.61

adhesion molecule
(HECAM_
PDQK.V

precursor
HUMAN)

hepatocyte growth
Q04756
R.VQLSPDLLATL
0.82

factor activator
(HGFA_
PEPASPGR.Q

preproprotein
HUMAN)

histidine-rich
P04196
R.DGYLFQLLR.I
0.63

glycoprotein
(HRG_

precursor
HUMAN)

hyaluronan-binding
Q14520
K.FLNWIK.A
0.82

protein 2 isoform 1
(HABP2_

preproprotein
HUMAN)

hyaluronan-binding
Q14520
K.LKPVDGHCALE
0.61

protein 2 isoform 1
(HABP2_
SK.Y

preproprotein
HUMAN)

hyaluronan-binding
Q14520
K.RPGVYTQVTK.
0.74

protein 2 isoform 1
(HABP2_
F

preproprotein
HUMAN)

inactive caspase-12
Q6UXS9
K.AGADTHGRLLQ
0.74

(CASPC_
GNICNDAVTK.A

HUMAN)

insulin-degrading
P14735
K.KIIEKM*ATFE
0.85

enzyme isoform 1
(IDE_
IDEK.R

HUMAN)

insulin-like growth
P35858
R.SFEGLGQLEVL
0.62

factor-binding
(ALS_
TLDHNQ.LQEVK.

protein
HUMAN)
A

complex acid

labile subunit

isoform

2 precursor

inter-alpha-
P19827
K.ELAAQTIKK.S
0.81

trypsin
(ITIH1_

inhibitor
HUMAN)

heavy chain

HI isoform a

precursor

inter-alpha-trypsin
P19827
K.GSLVQASEANL
0.71

inhibitor heavy chain
(IT1H1__
QAAQDFVR.G

HI isoform a
HUMAN)

precursor

inter-alpha-trypsin
P19827
K.QLVHHFEIDVD
0.70

inhibitor heavy chain
(ITIH1_
IFEPQGISK.L

HI isoform a
HUMAN)

precursor

inter-alpha-
P19827
K.QYYEGSEIVVA
0.83

trypsin
(ITIH1_
GR.I

inhibitor
HUMAN)

heavy chain

heparin cofactor 2
P05546
R.LNILNAK.F
0.68

precursor
(HEP2_

HUMAN)

heparin cofactor 2
P05546
R.NFGYTLR.S
0.64

precursor
(HEP2_

HUMAN)

heparin cofactor 2
P05546
R.VLKDQVNTFDN
0.63

precursor
(HEP2_
IFIAPVGISTAMG

HUMAN)
M*ISLGLK.G

hepatocyte cell
Q14CZ8
K.PLLNDSRMLLS
0.61

adhesion molecule
(HECAM_
PDQK.V

precursor
HUMAN)

hepatocyte growth
Q04756
R.VQLSPDLLATL
0.82

factor activator
(HGFA_
PEPASPGR.Q

preproprotein
HUMAN)

histidine-rich
P04196
R.DGYLFQLLR.I
0.63

glycoprotein
(HRG_

precursor
HUMAN)

hyaluronan-binding
Q14520
K.FLNWIK.A
0.82

protein 2 isoform 1
(HABP2_

preproprotein
HUMAN)

hyaluronan-binding
Q14520
K.LKPVDGHCALE
0.61

protein 2 isoform 1
(HABP2_
SK.Y

preproprotein
HUMAN)

hyaluronan-binding
Q14520
K.RPGVYTQVTK.
0.74

protein 2 isoform 1
(HABP2_
F

preproprotein
HUMAN)

inactive caspase-12
Q6UXS9
K.AGADTHGRLLQ
0.74

(CASPC_
GNICNDAVTK.A

HUMAN)

insulin-degrading
P14735
K.KIIEKM*ATFE
0.85

enzyme isoform 1
(IDE_
IDEK.R

HUMAN)

insulin-like
P35858
R.SFEGLGQLEVL
0.62

growth
(ALS_
TLDHNQ.LQEVK.

factor-binding
HUMAN)
A

protein complex

acid

labile subunit

isoform

2 precursor

inter-alpha-
P19827
K.ELAAQTIKK.S
0.81

trypsin
(ITIH1_

inhibitor
HUMAN)

heavy chain

HI isoform a

precursor

inter-alpha-
P19827
K.GSLVQASEANL
0.71

trypsin
(ITIH1_
QAAQDFVR.G

inhibitor
HUMAN)

heavy chain

HI isoform a

precursor

inter-alpha-
P19827
K.QLVHHFEIDVD
0.70

trypsin
(ITIH1_
IFEPQGISK.L

inhibitor
HUMAN)

heavy chain

HI isoform a

precursor

inter-alpha-
P19827
K.QYYEGSEIVVA
0.83

trypsin
(ITIH1_
GR.I

inhibitor
HUMAN)

heavy chain

H1 isoform a

precursor

inter-alpha-trypsin
P19827
R.EVAFDLEIPKT
0.70

inhibitor heavy chain
(ITIH1_
AFISDFAVTADGN

HI isoform a
HUMAN)
AFIGDIK.D

precursor

inter-alpha-trypsin
P19827
R.GMADQDGLKPT
0.63

inhibitor heavy chain
(ITIH1_
IDKPSEDSPPLEM

HI isoform a
HUMAN)
*LGPR.R

precursor

inter-alpha-trypsin
P19827
R.GMADQDGLKPT
0.60

inhibitor heavy chain
(ITIH1_
IDKPSEDSPPLEM

HI isoform a
HUMAN)
LGPR.R

precursor

inter-alpha-trypsin
P19823
K.FDPAKLDQIES
0.80

inhibitor heavy chain
(ITIH2_
VITATSANTQLVL

H2 precursor
HUMAN)
ETLAQM*DDLQDF

LSK.D

inter-alpha-trypsin
P19823
K.KFYNQVSTPLL
0.76

inhibitor heavy chain
(ITIH2_
R.N

H2 precursor
HUMAN)

inter-alpha-trypsin
P19823
K.NILFVIDVSGS
0.68

inhibitor heavy chain
(ITIH2_
M*WGVK.M

H2 precursor
HUMAN)

inter-alpha-trypsin
P19823
K.NILFVIDVSGS
0.62

inhibitor heavy chain
(ITIH2_
MWGVK.M

H2 precursor
HUMAN)

inter-alpha-trypsin
P19823
R.KLGSYEHR.I
0.72

inhibitor heavy chain
(ITIH2_

H2 precursor
HUMAN)

inter-alpha-trypsin
P19823
R.LSNENHGIAQ
0.66

inhibitor heavy chain
(ITIH2_
R.I

H2 precursor
HUMAN)

inter-alpha-trypsin
P19823
R.MATTMIQSK.V
0.60

inhibitor heavy chain
(ITIH2_

H2 precursor
HUMAN)

inter-alpha-trypsin
P19823
R.SILQ.M*SLDH
0.63

inhibitor heavy chain
(ITIH2_
HIVTPLTSLVIEN

H2 precursor
HUMAN)
EAGDER.M

inter-alpha-trypsin
P19823
R.SILQMSLDHHI
0.65

inhibitor heavy chain
(ITIH2_
VTPLTSLVIENEA

H2 precursor
HUMAN)
GDER.M

inter-alpha-trypsin
P19823
R.TEVNVLPGAK.
0.69

inhibitor heavy chain
(ITIH2_
V

H2 precursor
HUMAN)

inter-alpha-trypsin
Q14624
K.NWFVIDK.S
0.68

inhibitor heavy chain
(ITIH4_

H4 isoform 1
HUMAN)

precursor

inter-alpha-trypsin
Q14624
K.WKETLFSVMPG
0.65

inhibitor heavy chain
(ITIH4_
LK.M

H4 isoform 1
HUMAN)

precursor

inter-alpha-trypsin
Q14624
K.YIFHNFM*ER.
0.67

inhibitor heavy chain
(ITIH4_
L

H4 isoform 1
HUMAN)

precursor

inter-alpha-trypsin
Q14624
R.FAHTVVTSR.V
0.63

inhibitor heavy chain
(ITIH4_

H4 isoform 1
HUMAN)

precursor

inter-alpha-trypsin
Q14624
R.FKPTLSQQQK.
0.60

inhibitor heavy chain
(ITIH4_
S

H4 isoform 1
HUMAN)

precursor

inter-alpha-trypsin
Q14624
R.IHEDSDSALQL
0.64

inhibitor heavy chain
(ITIH4_
QDFYQEVANPLLT

H4 isoform 1
HUMAN)
AVTFEYPSNAVEE

precursor

VTQNNFR.L

inter-alpha-trypsin
Q14624
R.MNFRPGVLSS
0.63

inhibitor heavy chain
(ITIH4_
R.Q

H4 isoform 1
HUMAN)

precursor

inter-alpha-trypsin
Q14624
R.NVHSAGAAGS
0.62

inhibitor heavy chain
(ITIH4_
R.M

H4 isoform 1
HUMAN)

precursor

inter-alpha-trypsin
Q14624
R.NVHSGSTFFK.
0.75

inhibitor heavy chain
(IT1H4_
Y

H4 isoform 1
HUMAN)

precursor

inter-alpha-trypsin
Q14624
R.RLGVYELLLK.
0.66

inhibitor heavy chain
(ITIH4_
V

H4 isoform 1
HUMAN)

precursor

kallistatin
P29622
K.KLELHLPK.F
0.78

precursor
(KAIN_

HUMAN)

kallistatin
P29622
R.EIEEVLTPEML
0.60

precursor
(KAIN_
MR.W

HUMAN)

kininogen-1 isoform 2
P01042
K.AATGECTATVG
0.67

precursor
(KNG1_
KR.S

HUMAN)

kininogen-1 isoform 2
P01042
K.LGQSLDCNAEV
0.72

precursor
(KNG1_
YWPWEK.K

HUMAN)

kininogen-1 isoform 2
P01042
K.YNSQNQSNNQF
0.62

precursor
(KNG1_
VLYR.I

HUMAN)

kininogen-1 isoform 2
P01042
R.QVVAGLNFR.I
0.64

precursor
(KNG1_

HUMAN)

leucine-rich alpha-2-
P02750
K.DLLLPQPDLR.
0.64

glycoprotein
(A2GL_
Y

precursor
HUMAN)

leucine-rich alpha-2-
P02750
R.LHLEGNKLQVL
0.76

glycoprotein
(A2GL_
GK.D

precursor
HUMAN)

leucine-rich alpha-2-
P02750
R.TLDLGENQLET
0.61

glycoprotein
(A2GL_
LPPDLLR.G

precursor
HUMAN)

lipopolysaccharide-
P18428
K.GLQYAAQEGLL
0.82

binding protein
(LBP_
ALQSELLR.I

precursor
HUMAN)

lipopolysaccharide-
P18428
K.LAEGFPLPLL
0.66

binding protein
(LBP_
K.R

precursor
HUMAN)

lumican precursor
P51884
K.SLEYLDLSFN
0.65

(LUM_
Q.IAR.L

HUMAN)

lumican precursor
P51884
R.LKEDAVSAAF
0.74

(LUM_
K.G

HUMAN)

m7GpppX
Q96C86
R.IVFENPDPSDG
0.62

diphosphatase
(DCPS_
FVLIPDLK.W

HUMAN)

matrix
Q99542
R.VYFFK.G
0.63

metalloproteinase-19
(MMP19_

isoform 1
HUMAN)

preproprotein

MBT domain-
Q05BQ5
K.WFDYLR.E
0.65

containing protein 1
(MBTD1_

HUMAN)

monocyte
P08571
R.LTVGAAQVPAQ
0.66

differentiation
(CD14_
LLVGALR.V

antigen CD14
HUMAN)

precursor

pappalysin-1
Q13219
R.VSFSSPLVAIS
0.66

preproprotein
(PAPP1_
GVALR.S

HUMAN)

phosphatidylinositol-
P80108
K.GIVAAFYSGPS
0.71

glycan-specific
(PHLD_
LSDKEK.L

phospholipase D
HUMAN)

precursor

phosphatidylinositol-
P80108
R.WYVPVKDLLGI
0.71

glycan-specific
(PHLD_
YEK.L

phospholipase D
HUMAN)

precursor

pigment epithelium-
P36955
K.LQSLFDSPDFS
0.61

derived factor
(PEDF_
K.I

precursor
HUMAN)

pigment epithelium-
P36955
R.ALYYDLISSPD
0.72

derived factor
(PEDF_
IHGTYK.E

precursor
HUMAN)

plasma kallikrein
P03952
R.CLLFSFLPASS
0.60

preproprotein
(KLKB1_
INDMEKR.F

HUMAN)

plasma protease Cl
P05155
K.FQPTLLTLPR.
0.70

inhibitor precursor
(IC1_
I

HUMAN)

plasma protease Cl
P05155
K.GVTSVSQ.IFH
0.66

inhibitor precursor
(IC1_
SPDLAIR.D

HUMAN)

plasminogen isoform
P00747
K.VIPACLPSPNY
0.63

1 precursor
(PLMN_
VVADR.T

HUMAN)

plasminogen isoform
P00747
R.FVTWIEGVMR.
0.60

1 precursor
(PLMN_
N

HUMAN)

plasminogen isoform
P00747
R.HSIFTPETNP
0.63

1 precursor
(PLMN_
R.A

HUMAN)

platelet basic
P02775
K.GKEESLDSDLY
0.70

protein
(CXCL7_
AELR.C

preproprotein
HUMAN)

platelet
P40197
K.MVLLEQLFLDH
0.66

glycoprotein
(GPV_
NALR.G

V precursor
HUMAN)

platelet
P40197
R.LVSLDSGLLNS
0.88

glycoprotein
(GPV_
LGALTELQFHR.N

V precursor
HUMAN)

pregnancy zone
P20742
K.ALLAYAFSLLG
0.66

protein precursor
(PZP_
K.Q

HUMAN)

pregnancy zone
P20742
K.DLFHCVSFTLP
0.86

protein precursor
(PZP_
R.I

HUMAN)

pregnancy zone
P20742
K.MLQ.ITNTGFE
0.84

protein precursor
(PZP_
MK.L

HUMAN)

pregnancy zone
P20742
R.NELIPLIYLEN
0.65

protein precursor
(PZP_
PRR.N

HUMAN)

pregnancy zone
P20742
R.SYIFIDEAHIT
0.68

protein precursor
(PZP_
QSLTWLSQMQK.D

HUMAN)

pregnancy-specific
P11465
R.SDPVTLNLLHG
0.66

beta-l-glycoprotein 2
(PSG2_
PDLPR.I

precursor
HUMAN)

pregnancy-specific
Q16557
R.TLFLFGVTK.Y
0.62

beta-l-glycoprotein 3
(PSG3_

precursor
HUMAN)

pregnancy-specific
Q15238
R.ILILPSVTR.N
0.76

beta-l-glycoprotein 5
(PSG5_

precursor
HUMAN)

pregnancy-specific
Q00889
R.SDPVTLNLLP
0.63

beta-l-glycoprotein 6
(PSG6_
K.L

isoform a
HUMAN)

progesterone-
Q8WXW3
R.VLQLEK.Q
0.71

induced-blocking
(PIBF1_

factor 1
HUMAN)

protein AMBP
P02760
R.VVAQGVGIPED
0.60

preproprotein
(AMBP_
SIFTMADR.G

HUMAN)

protein CBFA2T2
043439
R.LTEREWADEWK
0.70

isoform MTGRlb
(MTG8R _
HLDHALNCIMEMV

HUMAN)
EK.T

protein FAM98C
Q17RN3
R.ALCGGDGAAAL
0.75

(FA98C_
REPGAGLR.L

HUMAN)

protein NLRC3
Q7RTR2
K.ALM*DLLAGKG
0.92

(NLRC3_
SQGSQAPQALDR.

HUMAN)
T

protein Z-dependent
Q9UK55
K.MGDHLALEDYL
0.60

protease inhibitor
(ZPI_
TTDLVETWLR.N

precursor
HUMAN)

prothrombin
P00734
K.SPQELLCGASL
0.84

preproprotein
(THRB_
ISDR.W

HUMAN)

prothrombin
P00734
R.LAVTTHGLPCL
0.62

preproprotein
(THRB_
AWASAQAK.A

HUMAN)

prothrombin
P00734
R.SEGSSVNLSPP
0.70

preproprotein
(THRB_
LEQCVPDR.G

HUMAN)

prothrombin
P00734
R.SGIECQLWR.S
0.68

preproprotein
(THRB_

HUMAN)

prothrombin
P00734
R.TATSEYQTFFN
0.60

preproprotein
(THRB_
PR.T

HUMAN)

prothrombin
P00734
R.VTGWGNLKETW
0.69

preproprotein
(THRB_
TANVGK.G

HUMAN)

putative
Q5T013
R.IHLM*AGR.V
0.69

hydroxypyruvate
(HYI_

isomerase isoform 1
HUMAN)

putative
Q5T013
R.IHLMAGR.V
0.66

hydroxypyruvate
(HYI _

isomerase isoform 1
HUMAN)

ras-like
Q92737
R.PAHPALR.L
0.71

protein family
(RSLAA_

member 10A
HUMAN)

precursor

ras-related GTP-
Q7L523
K.ISNIIK.Q
0.82

binding protein A
(RRAGA_

HUMAN)

retinol-binding
P02753
K.M*KYWGVASFL
0.73

protein 4 precursor
(RET4_
QK.G

HUMAN)

retinol-binding
P02753
R.FSGTWYAM*AK
0.63

protein 4 precursor
(RET4_
.K

HUMAN)

retinol-binding
P02753
R.LLNLDGTCADS
0.79

protein 4 precursor
(RET4_
YSFVFSR.D

HUMAN)

retinol-binding
P02753
R.LLNNWDVCADM
0.77

protein 4 precursor
(RET4_
VGTFTDTEDPAKF

HUMAN)
K.M

sex hormone-binding
P04278
R.LFLGALPGEDS
0.66

globulin isoform 1
(SHBG _
STSFCLNGLWAQG

precursor
HUMAN)
QR.L

sex hormone-binding
P04278
K.DDWFMLGLR.D
0.60

globulin isoform 4
(SHBG _

precursor
HUMAN)

sex hormone-binding
P04278
R.SCDVESNPGIF
0.64

globulin isoform 4
(SHBG _
LPPGTQAEFNLR.

HUMAN)
G

precursor

sex hormone-binding
P04278
R.TWDPEGVIFYG
0.65

globulin isoform 4
(SHBG_
DTNPKDDWFM*LG

precursor
HUMAN)
LR.D

sex hormone-binding
P04278
R.TWDPEGVIFYG
0.66

globulin isoform 4
(SHBG_
DTNPKDDWFMLGL

precursor
HUMAN)
R.D

signal transducer
P52630
R.KFCRDIQDPTQ
0.73

and activator of
(STAT2_
LAEMIFNLLLEEK

transcription 2
HUMAN)
.R

spectrin beta chain,
Q13813
R.NELIRQEKLEQ
0.60

non-erythrocytic 1
(SPTN1_
LAR.R

HUMAN)

stabilin-1
Q9NY15
R.KNLSER.W
0.88

precursor
(STAB1_

HUMAN)

succinate-
P51649
R.KWYNLMIQNK.
0.88

semialdehyde
(SSDH_
D

dehydrogenase,
HUMAN)

mitochondrial

tetranectin
P05452
K.SRLDTLAQEVA
0.75

precursor
(TETN_
LLK.E

HUMAN)

THAP domain-
Q8TBB0
K.RLDVNAAGIWE
0.69

containing
(THAP6_
PKK.G

protein
HUMAN)

thyroxine-binding
P05543
R.SILFLGK.V
0.79

globulin precursor
(THBG_

HUMAN)

tripartite motif-
Q9C035
R.ELISDLEHRLQ
0.60

containing protein 5
(TRIM5_
GSVM*ELLQGVD

HUMAN)
GVIK.R

vitamin D-binding
P02774
K.EDFTSLSLVLY
0.66

protein isoform 1
(VTDB_
SR.K

precursor
HUMAN)

vitamin D-binding
P02774
K.ELSSFIDKGQE
0.67

protein isoform 1
(VTDB_
LCADYSENTFTEY

precursor
HUMAN)
K.K

vitamin D-binding
P02774
K.ELSSFIDKGQE
0.66

protein isoform 1
(VTDB_
LCADYSENTFTEY

precursor
HUMAN)
KK.K

vitamin D-binding
P02774
K.EVVSLTEACCA
0.65

protein isoform 1
(VTDB_
EGADPDCYDTR.T

precursor
HUMAN)

vitamin D-binding
P02774
K.TAMDVFVCTYF
0.84

protein isoform 1
(VTDB_
MPAAQLPELPDVE

precursor
HUMAN)
LPTNKDVCDPGNT

K.V

vitamin D-binding
P02774
R.RTHLPEVFLS
0.69

protein isoform 1
(VTDB_
.KV

precursor
HUMAN)

vitamin D-binding
P02774
R.VCSQYAAYGE
0.66

protein isoform 1
(VTDB_
K.K

precursor
HUMAN)

vitronectin precursor
P04004
K.LIRDVWGIEGP
0.61

(VTNC_
IDAAFTR.I

HUMAN)

vitronectin precursor
P04004
R.DVWGIEGPIDA
0.63

(VTNC_
AFTR.I

HUMAN)

vitronectin precursor
P04004
R.ERVYFFK.G
0.81

(VTNC_

HUMAN)

vitronectin precursor
P04004
R.FEDGVLDPDYP
0.64

(VTNC_
R.N

HUMAN)

vitronectin precursor
P04004
R.IYISGM*APRP
0.75

(VTNC_
SLAK.K

HUMAN)

zinc finger protein
P52746
K.TRFLLR.T
0.66

142
(ZN142_

HUMAN)

TABLE 9

Significant peptides (AUC > 0.6) for X!Tandem only

Protein description
Uniprot ID (name)
Peptide
XT_AUC

afamin precursor

P43652

K.HELTDEELQSLFTNFANVVDK.C
0.65

(AFAM_HUMAN)

afamin precursor

P43652

R.NPFVFAPTLLTVAVHFEEVAK.S
0.91

(AFAM_HUMAN)

alpha-1-

P01011

K.ADLSGITGAR.N
0.67

antichymotrypsin
(AACT_HUMAN)

precursor

alpha-1-

P01011

K.MEEVEAMLLPETLKR.W
0.60

antichymotrypsin
(AACT_HUMAN)

precursor

alpha-1-

P01011

K.WEMPFDPQDTHQSR.F
0.64

antichymotrypsin
(AACT_HUMAN)

precursor

alpha-1-

P01011

R.LYGSEAFATDFQDSAAAK.K
0.62

antichymotrypsin
(AACT_HUMAN)

precursor

alpha-1B-glycoprotein

P04217

K.HQFLLTGDTQGR.Y
0.72

precursor
(A1BG_HUMAN)

alpha-1B-glycoprotein

P04217

K.NGVAQEPVHLDSPAIK.H
0.63

precursor
(A1BG_HUMAN)

alpha-1B-glycoprotein

P04217

K.SLPAPWLSM*APVSWITPGLK.T
0.72

precursor
(A1BG_HUMAN)

alpha-1B-glycoprotein

P04217

K.VTLTCVAPLSGVDFQLRR.G
0.67

precursor
(A1BG_HUMAN)

alpha-1B-glycoprotein

P04217

R.C*EGPIPDVTFELLR.E
0.67

precursor
(A1BG_HUMAN)

alpha-1B-glycoprotein

P04217

R.C*LAPLEGAR.F
0.79

precursor
(A1BG_HUMAN)

alpha-1B-glycoprotein

P04217

R.CLAPLEGAR.F
0.63

precursor
(A1BG_HUMAN)

alpha-1B-glycoprotein

P04217

R.GVTFLLR.R
0.69

precursor
(A1BG_HUMAN)

alpha-1B-glycoprotein

P04217

R.LHDNQNGWSGDSAPVELILSDETL
0.60

precursor
(A1BG_HUMAN)
PAPEFSPEPESGR.A

alpha-1B-glycoprotein

P04217

R.TPGAAANLELIFVGPQHAGNYR.C
0.62

precursor
(A1BG_HUMAN)

alpha-2-antiplasmin

P08697

K.HQM*DLVATLSQLGLQELFQAPDL
0.61

isoform a precursor
(A2AP_HUMAN)
R.G

alpha-2-antiplasmin

P08697

R.LCQDLGPGAFR.L
0.68

isoform a precursor
(A2AP_HUMAN)

alpha-2-antiplasmin

P08697

R.WFLLEQPEIQVAHFPFK.N
0.60

isoform a precursor
(A2AP_HUMAN)

alpha-2-HS-glycoprotein

P02765

K.VWPQQPSGELFEIEIDTLETTCHVL
0.61

preproprotein
(FETUA_HUMAN)
DPTPVAR.C

alpha-2-HS-glycoprotein

P02765

R.HTFMGVVSLGSPSGEVSHPR.K
0.68

preproprotein
(FETUA_HUMAN)

alpha-2-HS-glycoprotein

P02765

R.Q*PNCDDPETEEAALVAIDYINQNL
0.69

preproprotein
(FETUA_HUMAN)
PWGYK.H

alpha-2-HS-glycoprotein

P02765

R.QPNCDDPETEEAALVAIDYINQNLP
0.64

preproprotein
(FETUA_HUMAN)
WGYK.H

alpha-2-HS-glycoprotein

P02765

R.TVVQPSVGAAAGPVVPPCPGR.I
0.64

preproprotein
(FETUA_HUMAN)

angiotensinogen

P01019

K.QPFVQGLALYTPVVLPR.S
0.73

preproprotein
(ANGT_HUMAN)

angiotensinogen

P01019

R.AAM*VGM*LANFLGFR.I
0.62

preproprotein
(ANGT_HUMAN)

apolipoprotein A-IV

P06727

K.LVPFATELHER.L
0.64

precursor
(APOA4_HUMAN)

apolipoprotein A-IV

P06727

R.LLPHANEVSQK.I
0.61

precursor
(APOA4_HUMAN)

apolipoprotein A-IV

P06727

R.SLAPYAQDTQEKLNHQLEGLTFQM
0.70

precursor
(APOA4_HUMAN)
K.K

apolipoprotein B-100

P04114

K.FPEVDVLTK.Y
0.61

precursor
(APOB_HUMAN)

apolipoprotein B-100

P04114

K.HINIDQFVR.K
0.70

precursor
(APOB_HUMAN)

apolipoprotein B-100

P04114

K.LLSGGNTLHLVSTTK.T
0.66

precursor
(APOB_HUMAN)

apolipoprotein B-100

P04114

K.Q*VFLYPEKDEPTYILNIKR.G
0.81

precursor
(APOB_HUMAN)

apolipoprotein B-100

P04114

K.QVFLYPEKDEPTYILNIKR.G
0.77

precursor
(APOB_HUMAN)

apolipoprotein B-100

P04114

K.SLHMYANR.L
0.83

precursor
(APOB_HUMAN)

apolipoprotein B-100

P04114

K.SVSDGIAALDLNAVANK.I
0.62

precursor
(APOB_HUMAN)

apolipoprotein B-100

P04114

K.SVSLPSLDPASAKIEGNLIFDPNNYL
0.67

precursor
(APOB_HUMAN)
PK.E

apolipoprotein B-100

P04114

K.TEVIPPLIENR.Q
0.63

precursor
(APOB_HUMAN)

apolipoprotein B-100

P04114

K.VLVDHFGYTK.D
0.76

precursor
(APOB_HUMAN)

apolipoprotein B-100

P04114

R.TSSFALNLPTLPEVKFPEVDVLTK.Y
0.62

precursor
(APOB_HUMAN)

apolipoprotein C-III

P02656

R.GWVTDGFSSLKDYWSTVK.D
0.66

precursor
(APOC3_HUMAN)

apolipoprotein E

P02649

R.GEVQAMLGQSTEELR.V
0.81

precursor
(APOE_HUMAN)

apolipoprotein E

P02649

R.LAVYQAGAR.E
0.63

precursor
(APOE_HUMAN)

apolipoprotein E

P02649

R.LGPLVEQGR.V
0.69

precursor
(APOE_HUMAN)

attractin isoform 2

O75882

K.LTLTPWVGLR.K
0.69

preproprotein
(ATRN_HUMAN)

beta-2-glycoprotein 1

P02749

K.FICPLTGLWPINTLK.C
0.63

precursor
(APOH_HUMAN)

beta-2-glycoprotein 1

P02749

K.TFYEPGEEITYSCKPGYVSR.G
0.62

precursor
(APOH_HUMAN)

beta-Ala-His

Q96KN2

K.MVVSMTLGLHPWIANIDDTQYLA
0.81

dipeptidase precursor
(CNDP1_HUMAN)
AK.R

beta-Ala-His

Q96KN2

K.VFQYIDLHQDEFVQTLK.E
0.65

dipeptidase precursor
(CNDP1_HUMAN)

biotinidase precursor

P43251

R.TSIYPFLDFM*PSPQVVR.W
0.79

(BTD_HUMAN)

carboxypeptidase N

P15169

R.ELMLQLSEFLCEEFR.N
0.61

catalytic chain
(CBPN_HUMAN)

precursor

ceruloplasmin

P00450

K.AEEEHLGILGPQLHADVGDKVK.I
0.73

precursor
(CERU_HUMAN)

ceruloplasmin

P00450

K.ALYLQYTDETFR.T
0.64

precursor
(CERU_HUMAN)

ceruloplasmin

P00450

K.DVDKEFYLFPTVFDENESLLLEDN
0.62

precursor
(CERU_HUMAN)
IR.M

ceruloplasmin

P00450

K.HYYIGIIETTWDYASDHGEK.K
0.61

precursor
(CERU_HUMAN)

ceruloplasmin

P00450

R.EYTDASFTNRK.E
0.67

precursor
(CERU_HUMAN)

ceruloplasmin

P00450

R.HYYIAAEEIIWNYAPSGIDIFTK.E
0.63

precursor
(CERU_HUMAN)

ceruloplasmin

P00450

R.IYHSHIDAPK.D
0.62

precursor
(CERU_HUMAN)

ceruloplasmin

P00450

R.Q*KDVDKEFYLFPTVFDENESLLLE
0.74

precursor
(CERU_HUMAN)
DNIR.M

ceruloplasmin

P00450

R.QKDVDKEFYLFPTVFDENESLLLED
0.65

precursor
(CERU_HUMAN)
NIR.M

ceruloplasmin

P00450

R.TYYIAAVEVEWDYSPQR.E
0.90

precursor
(CERU_HUMAN)

coagulation factor IX

P00740

R.SALVLQYLR.V
0.69

preproprotein
(FA9_HUMAN)

coagulation factor V

P12259

K.EFNPLVIVGLSK.D
0.61

precursor
(FA5_HUMAN)

coagulation factor XII

P00748

R.NPDNDIRPWCFVLNR.D
0.65

precursor
(FA12_HUMAN)

coagulation factor XII

P00748

R.VVGGLVALR.G
0.61

precursor
(FA12_HUMAN)

complement C1q

P02746

K.NSLLGMEGANSIFSGFLLFPDMEA.-
0.64

subcomponent subunit
(C1QB_HUMAN)

B precursor

complement C1q

P02746

K.VPGLYYFTYHASSR.G
0.63

subcomponent subunit
(C1QB_HUMAN)

B precursor

complement C1q

P02747

R.Q*THQPPAPNSLIR.F
0.60

subcomponent subunit
(C1QC_HUMAN)

C precursor

complement C1r

P00736

R.LPVANPQACENWLR.G
0.72

subcomponent
(C1R_HUMAN)

precursor

complement C2

P06681

K.NQGILEFYGDDIALLK.L
0.74

isoform 3
(CO2_HUMAN)

complement C2

P06681

K.RNDYLDIYAIGVGK.L
0.61

isoform 3
(CO2_HUMAN)

complement C2

P06681

R.QPYSYDFPEDVAPALGTSFSHMLG
0.78

isoform 3
(CO2_HUMAN)
ATNPTQK.T

complement C3

P01024

R.IHWESASLLR.S
0.69

precursor
(CO3_HUMAN)

complement C4-A

P0C0L4

K.FACYYPR.V
0.64

isoform 1
(CO4A_HUMAN)

complement C4-A

P0C0L4

K.LHLETDSLALVALGALDTALYAAGS
0.74

isoform 1
(CO4A_HUMAN)
K.S

complement C4-A

P0C0L4

K.LVNGQSHISLSK.A
0.64

isoform 1
(CO4A_HUMAN)

complement C4-A

P0C0L4

K.M*RPSTDTITVMVENSHGLR.V
0.60

isoform 1
(CO4A_HUMAN)

complement C4-A

P0C0L4

K.MRPSTDTITVMVENSHGLR.V
0.65

isoform 1
(CO4A_HUMAN)

complement C4-A

P0C0L4

K.SCGLHQLLR.G
0.74

isoform 1
(CO4A_HUMAN)

complement C4-A

P0C0L4

K.VGLSGMAIADVTLLSGFHALR.A
0.61

isoform 1
(CO4A_HUMAN)

complement C4-A

P0C0L4

K.YVLPNFEVK.I
0.64

isoform 1
(CO4A_HUMAN)

complement C4-A

P0C0L4

R.ALEILQEEDLIDEDDIPVR.S
0.64

isoform 1
(CO4A_HUMAN)

complement C4-A

P0C0L4

R.ECVGFEAVQEVPVGLVQPASATLY
0.62

isoform 1
(CO4A_HUMAN)
DYYNPER.R

complement C4-A

P0C0L4

R.EELVYELNPLDHR.G
0.66

isoform 1
(CO4A_HUMAN)

complement C4-A

P0C0L4

R.STQDTVIALDALSAYWIASHTTE
0.70

isoform 1
(CO4A_HUMAN)
ER.G

complement C4-A

P0C0L4

R.VGDTLNLNLR.A
0.79

isoform 1
(CO4A_HUMAN)

complement C4-A

P0C0L4

R.VHYTVCIWR.N
0.65

isoform 1
(CO4A_HUMAN)

complement C4-B-like

P0C0L5

K.GLCVATPVQLR.V
1.00

preproprotein
(CO4B_HUMAN)

complement C4-B-like

P0C0L5

K.KYVLPNFEVK.I
0.60

preproprotein
(CO4B_HUMAN)

complement C4-B-like

P0C0L5

K.VDFTLSSERDFALLSLQVPLKDAK.S
0.74

preproprotein
(CO4B_HUMAN)

complement C4-B-like

P0C0L5

R.EMSGSPASGIPVK.V
0.72

preproprotein
(CO4B_HUMAN)

complement C4-B-like

P0C0L5

R.GCGEQTM*IYLAPTLAASR.Y
0.75

preproprotein
(CO4B_HUMAN)

complement C4-B-like

P0C0L5

R.NGESVKLHLETDSLALVALGALDTA
0.85

preproprotein
(CO4B_HUMAN)
LYAAGSK.S

complement C5

P01031

R.IPLDLVPK.T
0.65

preproprotein
(CO5_HUMAN)

complement C5

P01031

R.SYFPESWLWEVHLVPR.R
0.63

preproprotein
(CO5_HUMAN)

complement C5

P01031

R.YGGGFYSTQDTINAIEGLTEYSLL
0.62

preproprotein
(CO5_HUMAN)
VK.Q

complement

P13671

K.ENPAVIDFELAPIVDLVR.N
0.63

component C6
(CO6_HUMAN)

precursor

complement

P07357

K.YNPVVIDFEMQPIHEVLR.H
0.61

component C8 alpha
(CO8A_HUMAN)

chain precursor

complement

P07357

R.HTSLGPLEAK.R
0.65

component C8 alpha
(CO8A_HUMAN)

chain precursor

complement

P07358

K.C*QHEMDQYWGIGSLASGINLFTN
0.61

component C8 beta
(CO8B_HUMAN)
SFEGPVLDHR.Y

chain preproprotein

complement

P07358

K.SGFSFGFK.I
0.64

component C8 beta
(CO8B_HUMAN)

chain preproprotein

complement

P07358

R.DTMVEDLVVLVR.G
0.77

component C8 beta
(CO8B_HUMAN)

chain preproprotein

complement

P07360

K.ANFDAQQFAGTWLLVAVGSACR.F
0.63

component C8 gamma
(CO8G_HUMAN)

chain precursor

complement

P07360

R.AEATTLHVAPQGTAMAVSTFR.K
0.61

component C8 gamma
(CO8G_HUMAN)

chain precursor

complement

P02748

R.DVVLTTTFVDDIK.A
0.73

component C9
(CO9_HUMAN)

precursor

complement

P02748

R.RPWNVASLIYETK.G
0.66

component C9
(CO9_HUMAN)

precursor

complement factor B

P00751

K.ISVIRPSK.G
0.70

preproprotein
(CFAB_HUMAN)

complement factor B

P00751

K.VASYGVKPR.Y
0.63

preproprotein
(CFAB_HUMAN)

complement factor B

P00751

R.DFHINLFQVLPWLK.E
0.68

preproprotein
(CFAB_HUMAN)

complement factor B

P00751

R.DLLYIGK.D
0.63

preproprotein
(CFAB_HUMAN)

complement factor B

P00751

R.GDSGGPLIVHK.R
0.63

preproprotein
(CFAB_HUMAN)

complement factor B

P00751

R.LEDSVTYHCSR.G
0.68

preproprotein
(CFAB_HUMAN)

complement factor B

P00751

R.LPPTTTCQQQK.E
0.68

preproprotein
(CFAB_HUMAN)

complement factor H

P08603

K.CLHPCVISR.E
0.62

isoform a precursor
(CFAH_HUMAN)

complement factor H

P08603

K.CTSTGWIPAPR.C
0.74

isoform a precursor
(CFAH_HUMAN)

complement factor H

P08603

K.IDVHLVPDR.K
0.66

isoform a precursor
(CFAH_HUMAN)

complement factor H

P08603

K.IVSSAMEPDREYHFGQAVR.F
0.67

isoform a precursor
(CFAH_HUMAN)

complement factor H

P08603

K.SIDVACHPGYALPK.A
0.67

isoform a precursor
(CFAH_HUMAN)

complement factor H

P08603

K.VSVLCQENYLIQEGEEITCKDGR.W
0.63

isoform a precursor
(CFAH_HUMAN)

complement factor H

P08603

K.WSSPPQCEGLPCK.S
0.60

isoform a precursor
(CFAH_HUMAN)

complement factor H

P08603

R.EIMENYNIALR.W
0.61

isoform a precursor
(CFAH_HUMAN)

complement factor H

P08603

R.RPYFPVAVGK.Y
0.83

isoform a precursor
(CFAH_HUMAN)

complement factor H

P08603

R.WQSIPLCVEK.I
0.63

isoform a precursor
(CFAH_HUMAN)

complement factor I

P05156

R.YQIWTTVVDWIHPDLKR.I
0.72

preproprotein
(CFAI_HUMAN)

corticosteroid-binding

P08185

K.AVLQLNEEGVDTAGSTGVTLNLTSK
0.61

globulin precursor
(CBG_HUMAN)
PIILR.F

corticosteroid-binding

P08185

R.GLASANVDFAFSLYK.H
0.66

globulin precursor
(CBG_HUMAN)

fibrinogen alpha chain

P02671

K.TFPGFFSPMLGEFVSETESR.G
0.62

isoform alpha-E
(FIBA_HUMAN)

preproprotein

gelsolin isoform b

P06396

K.FDLVPVPTNLYGDFFTGDAYVILK.T
0.66

(GELS_HUMAN)

gelsolin isoform b

P06396

K.QTQVSVLPEGGETPLFK.Q
0.66

(GELS_HUMAN)

gelsolin isoform b

P06396

K.TPSAAYLWVGTGASEAEK.T
0.71

(GELS_HUMAN)

gelsolin isoform b

P06396

R.AQPVQVAEGSEPDGFWEALGGK.A
0.67

(GELS_HUMAN)

gelsolin isoform b

P06396

R.IEGSNKVPVDPATYGQFYGGDSYIIL
0.60

(GELS_HUMAN)
YNYR.H

gelsolin isoform b

P06396

R.VEKFDLVPVPTNLYGDFFTGDAYVI
0.73

(GELS_HUMAN)
LK.T

gelsolin isoform b

P06396

R.VPFDAATLHTSTAMAAQHGMDD
0.63

(GELS_HUMAN)
DGTGQK.Q

glutathione peroxidase

P22352

K.FLVGPDGIPIMR.W
0.60

3 precursor
(GPX3_HUMAN)

hemopexin precursor

P02790

K.ALPQPQNVTSLLGCTH.-
0.63

(HEMO_HUMAN)

hemopexin precursor

P02790

K.SLGPNSCSANGPGLYLIHGPNLYCY
0.68

(HEMO_HUMAN)
SDVEK.L

hemopexin precursor

P02790

R.DGWHSWPIAHQWPQGPSAVDAA
0.63

(HEMO_HUMAN)
FSWEEK.L

hemopexin precursor

P02790

R.GECQAEGVLFFQGDR.E
0.67

(HEMO_HUMAN)

hemopexin precursor

P02790

R.GECQAEGVLFFQGDREWFWDLAT
0.67

(HEMO_HUMAN)
GTM*K.E

hemopexin precursor

P02790

R.LEKEVGTPHGIILDSVDAAFICPGSS
0.75

(HEMO_HUMAN)
R.L

hemopexin precursor

P02790

R.LWWLDLK.S
0.62

(HEMO_HUMAN)

hemopexin precursor

P02790

R.WKNFPSPVDAAFR.Q
0.68

(HEMO_HUMAN)

heparin cofactor 2

P05546

K.DQVNTFDNIFIAPVGISTAMGMISL
0.60

precursor
(HEP2_HUMAN)
GLK.G

insulin-like growth

P35858

K.ANVFVQLPR.L
0.71

factor-binding protein
(ALS_HUMAN)

complex acid labile

subunit isoform 2

precursor

insulin-like growth

P35858

R.LEALPNSLLAPLGR.L
0.61

factor-binding protein
(ALS_HUMAN)

complex acid labile

subunit isoform 2

precursor

insulin-like growth

P35858

R.LFQGLGK.L
0.68

factor-binding protein
(ALS_HUMAN)

complex acid labile

subunit isoform 2

precursor

insulin-like growth

P35858

R.NLIAAVAPGAFLGLK.A
0.76

factor-binding protein
(ALS_HUMAN)

complex acid labile

subunit isoform 2

precursor

insulin-like growth

P35858

R.TFTPQPPGLER.L
0.73

factor-binding protein
(ALS_HUMAN)

complex acid labile

subunit isoform 2

precursor

inter-alpha-trypsin

P19827

K.Q*LVHHFEIDVDIFEPQGISK.L
0.69

inhibitor heavy chain
(ITIH1_HUMAN)

H1 isoform a precursor

inter-alpha-trypsin

P19827

K.VTFQLTYEEVLK.R
0.61

inhibitor heavy chain
(ITIH1_HUMAN)

H1 isoform a precursor

inter-alpha-trypsin

P19827

K.VTFQLTYEEVLKR.N
0.70

inhibitor heavy chain
(ITIH1_HUMAN)

H1 isoform a precursor

inter-alpha-trypsin

P19827

R.GIEILNQVQESLPELSNHASILIMLT
0.62

inhibitor heavy chain
(ITIH1_HUMAN)
DGDPTEGVTDR.S

H1 isoform a precursor

inter-alpha-trypsin

P19827

R.GM*ADQDGLKPTIDKPSEDSPPLE
0.79

inhibitor heavy chain
(ITIH1_HUMAN)
M*LGPR.R

H1 isoform a precursor

inter-alpha-trypsin

P19827

R.KAAISGENAGLVR.A
0.78

inhibitor heavy chain
(ITIH1_HUMAN)

H1 isoform a precursor

inter-alpha-trypsin

P19823

K.AGELEVFNGYFVHFFAPDNLDPI
0.64

inhibitor heavy chain
(ITIH2_HUMAN)
PK.N

H2 precursor

inter-alpha-trypsin

P19823

K.FYNQVSTPLLR.N
0.68

inhibitor heavy chain
(ITIH2_HUMAN)

H2 precursor

inter-alpha-trypsin

P19823

K.VQFELHYQEVK.W
0.68

inhibitor heavy chain
(ITIH2_HUMAN)

H2 precursor

inter-alpha-trypsin

P19823

R.ETAVDGELVVLYDVK.R
0.63

inhibitor heavy chain
(ITIH2_HUMAN)

H2 precursor

inter-alpha-trypsin

P19823

R.IYLQPGR.L
0.75

inhibitor heavy chain
(ITIH2_HUMAN)

H2 precursor

inter-alpha-trypsin

Q06033

R.LWAYLTIEQLLEK.R
0.60

inhibitor heavy chain
(ITIH3_HUMAN)

H3 preproprotein

inter-alpha-trypsin

Q14624

K.ITFELVYEELLK.R
0.60

inhibitor heavy chain
(ITIH4_HUMAN)

H4 isoform 1 precursor

inter-alpha-trypsin

Q14624

K.LQDRGPDVLTATVSGK.L
0.67

inhibitor heavy chain
(ITIH4_HUMAN)

H4 isoform 1 precursor

inter-alpha-trypsin

Q14624

K.TGLLLLSDPDKVTIGLLFWDGRGEG
0.63

inhibitor heavy chain
(ITIH4_HUMAN)
LR.L

H4 isoform 1 precursor

inter-alpha-trypsin

Q14624

K.WKETLFSVM*PGLK.M
0.79

inhibitor heavy chain
(ITIH4_HUMAN)

H4 isoform 1 precursor

inter-alpha-trypsin

Q14624

R.AISGGSIQIENGYFVHYFAPEGLTT
0.60

inhibitor heavy chain
(ITIH4_HUMAN)
M*PK.N

H4 isoform 1 precursor

inter-alpha-trypsin

Q14624

R.AISGGSIQIENGYFVHYFAPEGLTT
0.65

inhibitor heavy chain
(ITIH4_HUMAN)
MPK.N

H4 isoform 1 precursor

inter-alpha-trypsin

Q14624

R.ANTVQEATFQMELPK.K
0.68

inhibitor heavy chain
(ITIH4_HUMAN)

H4 isoform 1 precursor

inter-alpha-trypsin

Q14624

R.SFAAGIQALGGTNINDAMLMAVQ
0.64

inhibitor heavy chain
(ITIH4_HUMAN)
LLDSSNQEER.L

H4 isoform 1 precursor

inter-alpha-trypsin

Q14624

R.VQGNDHSATR.E
0.63

inhibitor heavy chain
(ITIH4_HUMAN)

H4 isoform 1 precursor

inter-alpha-trypsin

Q14624

K.ITFELVYEELLKR.R
0.60

inhibitor heavy chain
(ITIH4_HUMAN)

H4 isoform 2 precursor

inter-alpha-trypsin

Q14624

K.VTIGLLFWDGR.G
0.65

inhibitor heavy chain
(ITIH4_HUMAN)

H4 isoform 2 precursor

inter-alpha-trypsin

Q14624

R.LWAYLTIQQLLEQTVSASDADQQA
0.68

inhibitor heavy chain
(ITIH4_HUMAN)
LR.N

H4 isoform 2 precursor

kallistatin precursor

P29622

K.LFHTNFYDTVGTIQLINDHVK.K
0.73

(KAIN_HUMAN)

kininogen-1 isoform 2

P01042

K.ENFLFLTPDCK.S
0.64

precursor
(KNG1_HUMAN)

kininogen-1 isoform 2

P01042

K.IYPTVNCQPLGMISLMK.R
0.64

precursor
(KNG1_HUMAN)

kininogen-1 isoform 2

P01042

K.KIYPTVNCQPLGMISLMK.R
0.78

precursor
(KNG1_HUMAN)

kininogen-1 isoform 2

P01042

K.SLWNGDTGECTDNAYIDIQLR.I
0.67

precursor
(KNG1_HUMAN)

lumican precursor

P51884

K.ILGPLSYSK.I
0.60

(LUM_HUMAN)

N-acetylmuramoyl-L-

Q96PD5

K.EYGVVLAPDGSTVAVEPLLAGLEAG
0.61

alanine amidase
(PGRP2_HUMAN)
LQGR.R

precursor

N-acetylmuramoyl-L-

Q96PD5

R.EGKEYGVVLAPDGSTVAVEPLLAGL
0.69

alanine amidase
(PGRP2_HUMAN)
EAGLQGR.R

precursor

N-acetylmuramoyl-L-

Q96PD5

R.Q*NGAALTSASILAQQVWGTLVLL
0.60

alanine amidase
(PGRP2_HUMAN)
QR.L

precursor

pigment epithelium-

P36955

K.IAQLPLTGSMSIIFFLPLK.V
0.65

derived factor
(PEDF_HUMAN)

precursor

pigment epithelium-

P36955

R.SSTSPTTNVLLSPLSVATALSALSLG
0.79

derived factor
(PEDF_HUMAN)
AEQR.T

precursor

plasma kallikrein

P03952

K.VAEYMDWILEK.T
0.62

preproprotein
(KLKB1_HUMAN)

plasma kallikrein

P03952

R.C*LLFSFLPASSINDMEKR.F
0.60

preproprotein
(KLKB1_HUMAN)

plasma kallikrein

P03952

R.C*QFFSYATQTFHK.A
0.60

preproprotein
(KLKB1_HUMAN)

plasma kallikrein

P03952

R.CLLFSFLPASSINDMEK.R
0.76

preproprotein
(KLKB1_HUMAN)

plasma protease C1

P05155

R.LVLLNAIYLSAK.W
0.96

inhibitor precursor
(IC1_HUMAN)

pregnancy zone protein

P20742

R.NALFCLESAWNVAK.E
0.67

precursor
(PZP_HUMAN)

pregnancy zone protein

P20742

R.NQGNTWLTAFVLK.T
0.61

precursor
(PZP_HUMAN)

pregnancy-specific

Q00887

R.SNPVILNVLYGPDLPR.I
0.62

beta-1-glycoprotein 9
(PSG9_HUMAN)

precursor

prenylcysteine oxidase

Q9UHG3

K.IAIIGAGIGGTSAAYYLR.Q
0.71

1 precursor
(PCYOX_HUMAN)

protein AMBP

P02760

K.WYNLAIGSTCPWLK.K
0.77

preproprotein
(AMBP_HUMAN)

protein AMBP

P02760

R.TVAACNLPIVR.G
0.66

preproprotein
(AMBP_HUMAN)

prothrombin

P00734

R.IVEGSDAEIGMSPWQVMLFR.K
0.62

preproprotein
(THRB_HUMAN)

prothrombin

P00734

R.RQECSIPVCGQDQVTVAMTPR.S
0.69

preproprotein
(THRB_HUMAN)

prothrombin

P00734

R.TFGSGEADCGLRPLFEK.K
0.61

preproprotein
(THRB_HUMAN)

retinol-binding protein

P02753

R.FSGTWYAMAK.K
0.60

4 precursor
(RET4_HUMAN)

retinol-binding protein

P02753

R.LLNNWDVCADMVGTFTDTEDPAK.F
0.64

4 precursor
(RET4_HUMAN)

serum amyloid P-

P02743

R.GYVIIKPLVWV.-
0.62

component precursor
(SAMP_HUMAN)

sex hormone-binding

P04278

K.VVLSSGSGPGLDLPLVLGLPLQLK.L
0.60

globulin isoform 1
(SHBG_HUMAN)

precursor

sex hormone-binding

P04278

R.TWDPEGVIFYGDTNPKDDWFM*L
0.75

globulin isoform 1
(SHBG_HUMAN)
GLR.D

precursor

sex hormone-binding

P04278

R.TWDPEGVIFYGDTNPKDDWFMLG
0.74

globulin isoform 1
(SHBG_HUMAN)
LR.D

precursor

thrombospondin-1

P07996

K.GFLLLASLR.Q
0.70

precursor
(TSP1_HUMAN)

thyroxine-binding

P05543

K.AVLHIGEK.G
0.85

globulin precursor
(THBG_HUMAN)

thyroxine-binding

P05543

K.FSISATYDLGATLLK.M
0.65

globulin precursor
(THBG_HUMAN)

thyroxine-binding

P05543

K.KELELQIGNALFIGK.H
0.61

globulin precursor
(THBG_HUMAN)

thyroxine-binding

P05543

K.MSSINADFAFNLYR.R
0.67

globulin precursor
(THBG_HUMAN)

transforming growth

Q15582

R.LTLLAPLNSVFK.D
0.65

factor-beta-induced
(BGH3_HUMAN)

protein ig-h3 precursor

transthyretin precursor

P02766

R.GSPAINVAVHVFR.K
0.67

(TTHY_HUMAN)

uncharacterized

Q8ND61

K.MPSHLMLAR.K
0.64

protein C3orf20
(CC020_HUMAN)

isoform 1

vitamin D-binding

P02774

K.ELPEHTVK.L
0.75

protein isoform 1
(VTDB_HUMAN)

precursor

vitamin D-binding

P02774

K.EYANQFMWEYSTNYGQAPLSLLVS
0.69

protein isoform 1
(VTDB_HUMAN)
YTK.S

precursor

vitamin D-binding

P02774

K.HLSLLTTLSNR.V
0.65

protein isoform 1
(VTDB_HUMAN)

precursor

vitamin D-binding

P02774

K.HQPQEFPTYVEPTNDEICEAFR.K
0.64

protein isoform 1
(VTDB_HUMAN)

precursor

vitamin D-binding

P02774

K.LAQKVPTADLEDVLPLAEDITNIL
0.73

protein isoform 1
(VTDB_HUMAN)
SK.C

precursor

vitamin D-binding

P02774

K.LCDNLSTK.N
0.70

protein isoform 1
(VTDB_HUMAN)

precursor

vitamin D-binding

P02774

K.LCMAALK.H
0.63

protein isoform 1
(VTDB_HUMAN)

precursor

vitamin D-binding

P02774

K.SCESNSPFPVHPGTAECCTK.E
0.63

protein isoform 1
(VTDB_HUMAN)

precursor

vitamin D-binding

P02774

K.SYLSMVGSCCTSASPTVCFLK.E
0.61

protein isoform 1
(VTDB_HUMAN)

precursor

vitamin D-binding

P02774

K.TAMDVFVCTYFM*PAAQLPELPDV
0.61

protein isoform 1
(VTDB_HUMAN)
ELPTNK.D

precursor

vitamin D-binding

P02774

K.VLEPTLK.S
0.69

protein isoform 1
(VTDB_HUMAN)

precursor

vitamin D-binding

P02774

R.KFPSGTFEQVSQLVK.E
0.66

protein isoform 1
(VTDB_HUMAN)

precursor

vitamin D-binding

P02774

R.THLPEVFLSK.V
0.62

protein isoform 1
(VTDB_HUMAN)

precursor

vitamin D-binding

P02774

R.TSALSAK.S
0.74

protein isoform 1
(VTDB_HUMAN)

precursor

vitronectin precursor

P04004

R.GQYCYELDEK.A
0.73

(VTNC_HUMAN)

vitronectin precursor

P04004

R.M*DWLVPATCEPIQSVFFFSGDK.Y
0.64

(VTNC_HUMAN)

vitronectin precursor

P04004

R.Q*PQFISR.D
0.63

(VTNC_HUMAN)

TABLE 10

Significant peptides (AUC > 0.6) for both X!Tandem and Sequest

Uniprot ID

Protein description
(name)
Peptide
XT_AUC
S_AUC

afamin precursor
P43652
K.HFQNLGK.D
0.74
0.61

(AFAM_HUMAN)

afamin precursor
P43652
R.RHPDLSIPELL
0.67
0.63

(AFAM_HUMAN)
R.I

afamin precursor
P43652
R.TINPAVDHCC
0.66
0.86

(AFAM_HUMAN)
K.T

alpha-1-antichymotrypsin
P01011
KITDLIKDLDSQ
0.71
0.73

precursor
(AACT_HUMAN)
TMMVLVNYIFF

K.A

alpha-1-antichymotrypsin
P01011
R.DYNLNDILLQ
0.74
0.62

precursor
(AACT_HUMAN)
LGIEEAFTSK.A

alpha-1-antichymotrypsin
P01011
R.GTHVDLGLAS
0.76
0.61

precursor
(AACT_HUMAN)
ANVDFAFSLYK.Q

alpha-1B-glycoprotein
P04217
K.SLPAPWLSMA
0.71
0.65

precursor
(A1BG_HUMAN)
PVSWITPGLK.T

alpha-2-antiplasmin
P08697
K.GFPIKEDFLEQ
0.66
0.69

isoform a precursor
(A2AP_HUMAN)
SEQLFGAKPVSL

TGK.Q

alpha-2-antiplasmin
P08697
K.HQMDLVATL
0.67
0.60

isoform a precursor
(A2AP_HUMAN)
SQLGLQELFQAP

DLR.G

alpha-2-antiplasmin
P08697
R.QLTSGPNQEQ
0.66
0.61

isoform a precursor
(A2AP_HUMAN)
VSPLTLLK.L

alpha-2-HS-glycoprotein
P02765
R.AQLVPLPPST
0.64
0.63

preproprotein
(FETUA_HUMAN)
YVEFTVSGTDC

VAK.E

angiotensinogen
P01019
K.DPTFIPAPIQA
0.69
0.69

preproprotein
(ANGT_HUMAN)
K.T

angiotensinogen
P01019
R.FM*QAVTGW
0.65
0.65

preproprotein
(ANGT_HUMAN)
K.T

antithrombin-III
P01008
K.ANRPFLVFI
0.72
0.60

precursor
(ANT3_HUMAN)
R.E

antithrombin-III
P01008
K.GDDITMVLIL
0.69
0.68

precursor
(ANT3_HUMAN)
PKPEK.S

antithrombin-III
P01008
R.DIPMNPMCIY
0.63
0.78

precursor
(ANT3_HUMAN)
R.S

apolipoprotein A-IV
P06727
K.KLVPFATELH
0.65
0.77

precursor
(APOA4_HUMAN)
ER.L

apolipoprotein A-IV
P06727
K.SLAELGGHLD
0.60
0.75

precursor
(APOA4_HUMAN)
QQVEEFR.R

apolipoprotein B-100
P04114
K.ALYWVNGQV
0.61
0.63

precursor
(APOB_HUMAN)
PDGVSK.V

apolipoprotein B-100
P04114
K.FIIPGLK.L
0.64
0.68

precursor
(APOB_HUMAN)

apolipoprotein B-100
P04114
K.FSVPAGIVIPS
0.63
0.63

precursor
(APOB_HUMAN)
FQALTAR.F

apolipoprotein B-100
P04114
K.IEGNLIFDPNN
0.63
0.65

precursor
(APOB_HUMAN)
YLPK.E

apolipoprotein B-100
P04114
K.LNDLNSVLV
0.91
0.88

precursor
(APOB_HUMAN)
MPTFHVPFTDL

QVPSCK.L

apolipoprotein B-100
P04114
K.VELEVPQLCS
0.60
0.61

precursor
(APOB_HUMAN)
FILK.T

apolipoprotein B-100
P04114
K.VNWEEEAAS
0.60
0.73

precursor
(APOB_HUMAN)
GLLTSLK.D

apolipoprotein B-100
P04114
R.ATLYALSHAV
0.78
0.80

precursor
(APOB_HUMAN)
NNYHK.T

apolipoprotein B-100
P04114
R.TGISPLALIK.G
0.64
0.77

precursor
(APOB_HUMAN)

apolipoprotein B-100
P04114
R.TLQGIPQMIG
0.65
0.66

precursor
(APOB_HUMAN)
EVIR.K

apolipoprotein C-III
P02656
K.DALSSVQESQ
0.80
0.69

precursor
(APOC3_HUMAN)
VAQQAR.G

apolipoprotein C-IV
P55056
R.DGWQWFWSP
0.63
0.67

precursor
(APOC4_HUMAN)
STFR.G

apolipoprotein E
P02649
K.VQAAVGTSA
0.70
0.72

precursor
(APOE_HUMAN)
APVPSDNH.-

apolipoprotein E
P02649
R.WELALGR.F
0.88
0.60

precursor
(APOE_HUMAN)

beta-2-microglobulin
P61769
K.SNFLNCYVSG
0.60
0.70

precursor
(B2MG_HUMAN)
FHPSDIEVDLLK.N

bone marrow
P13727
R.GGHCVALCT
0.83
0.86

proteoglycan isoform 1
(PRG2_HUMAN)
R.G

preproprotein

carboxypeptidase B2
Q96IY4
R.LVDFYVMPV
0.61
0.65

preproprotein
(CBPB2_HUMAN)
VNVDGYDYSW

K.K

carboxypeptidase B2
Q96IY4
R.YTHGHGSETL
0.60
0.68

preproprotein
(CBPB2_HUMAN)
YLAPGGGDDWI

YDLGIK.Y

carboxypeptidase N
P22792
K.LSNNALSGLP
0.65
0.67

subunit 2 precursor
(CPN2_HUMAN)
QGVFGK.L

carboxypeptidase N
P22792
K.TLNLAQNLLA
0.67
0.69

subunit 2 precursor
(CPN2_HUMAN)
QLPEELFHPLTS

LQTLK.L

carboxypeptidase N
P22792
R.WLNVQLSP
0.74
0.67

subunit 2 precursor
(CPN2_HUMAN)
R.Q

ceruloplasmin precursor
P00450
K.GDSVVWYLF
0.90
0.72

(CERU_HUMAN)
SAGNEADVHGI

YFSGNTYLWR.G

ceruloplasmin precursor
P00450
K.MYYSAVDPT
0.70
0.82

(CERU_HUMAN)
K.D

ceruloplasmin precursor
P00450
R.GPEEEHLGIL
0.60
0.65

(CERU_HUMAN)
GPVIWAEVGDTI

R.V

ceruloplasmin precursor
P00450
R.IDTINLFPATL
0.66
0.70

(CERU_HUMAN)
FDAYMVAQNP

GEWMLSCQNL

NHLK.A

ceruloplasmin precursor
P00450
R.SGAGTEDSAC
0.88
0.92

(CERU_HUMAN)
IPWAYYSTVDQ

VKDLYSGLIGPL

IVCR.R

cholinesterase precursor
P06276
K.IFFPGVSEFG
0.70
0.63

(CHLE_HUMAN)
K.E

cholinesterase precursor
P06276
R.AILQSGSFNAP
0.75
0.77

(CHLE_HUMAN)
WAVTSLYEAR.N

chorionic gonadotropin,
P01233
R.VLQGVLPALP
0.60
0.75

beta polypeptide 8
(CGHB_HUMAN)
QVVCNYR.D

precursor

chorionic
P01243
R.ISLLLIESWLE
0.83
0.63

somatomammotropin
(CSH_HUMAN)
PVR.F

hormone 2 isoform 2

precursor

coagulation factor XII
P00748
R.LHEAFSPVSY
0.60
0.66

precursor
(FA12_HUMAN)
QHDLALLR.L

coagulation factor XII
P00748
R.TTLSGAPCQP
0.69
0.82

precursor
(FA12_HUMAN)
WASEATYR.N

complement C1q
P02745
K.GLFQVVSGG
0.65
0.60

subcomponent subunit A
(C1QA_HUMAN)
MVLQLQQGDQ

precursor

VWVEKDPK.K

complement C1r
P00736
K.VLNYVDWIK
0.80
0.76

subcomponent precursor
(C1R_HUMAN)
K.E

complement C1s
P09871
K.SNALDIIFQTD
0.62
0.77

subcomponent precursor
(C1S_HUMAN)
LTGQK.K

complement C4-A
P0C0L4
K.EGAIHREELV
0.76
0.75

isoform 1
(CO4A_HUMAN)
YELNPLDHR.G

complement C4-A
P0C0L4
K.ITQVLHFTK.D
0.63
0.62

isoform 1
(CO4A_HUMAN)

complement C4-A
P0C0L4
K.SHALQLNNR.Q
0.66
0.71

isoform 1
(CO4A_HUMAN)

complement C4-A
P0C0L4
R.AVGSGATFSH
0.65
0.60

isoform 1
(CO4A_HUMAN)
YYYM*ILSR.G

complement C4-A
P0C0L4
R.EPFLSCCQFA
0.64
0.72

isoform 1
(CO4A_HUMAN)
ESLR.K

complement C4-A
P0C0L4
R.GHLFLQTDQP
0.63
0.76

isoform 1
(CO4A_HUMAN)
IYNPGQR.V

complement C4-A
P0C0L4
R.GLEEELQFSL
0.68
0.68

isoform 1
(CO4A_HUMAN)
GSK.I

complement C4-A
P0C0L4
R.GSFEFPVGDA
0.67
0.70

isoform 1
(CO4A_HUMAN)
VSK.V

complement C4-A
P0C0L4
R.LLATLCSAEV
0.61
0.71

isoform 1
(CO4A_HUMAN)
CQCAEGK.C

complement C4-A
P0C0L4
R.VQQPDCREPF
0.65
0.83

isoform 1
(CO4A_HUMAN)
LSCCQFAESLRK.K

complement C4-A
P0C0L4
R.YIYGKPVQGV
0.82
0.76

isoform 1
(CO4A_HUMAN)
AYVR.F

complement C5
P01031
K.ITHYNYLILS
0.66
0.69

preproprotein
(CO5_HUMAN)
K.G

complement C5
P01031
R.ENSLYLTAFT
0.60
0.68

preproprotein
(CO5_HUMAN)
VIGIR.K

complement C5
P01031
R.KAFDICPLVK.I
0.77
0.65

preproprotein
(CO5_HUMAN)

complement C5
P01031
R.VDDGVASFVL
0.68
0.61

preproprotein
(CO5_HUMAN)
NLPSGVTVLEFN

VK.T

complement component
P13671
K.TFSEWLESVK
0.94
0.64

C6 precursor
(CO6_HUMAN)
ENPAVIDFELAP

IVDLVR.N

complement component
P13671
R.IFDDFGTHYF
0.78
0.75

C6 precursor
(CO6_HUMAN)
TSGSLGGVYDL

LYQFSSEELK.N

complement component
P10643
K.ELSHLPSLYD
0.69
0.71

C7 precursor
(CO7_HUMAN)
YSAYR.R

complement component
P10643
R.RYSAWAESV
0.71
0.70

C7 precursor
(CO7_HUMAN)
TNLPQVIK.Q

complement component
P07357
K.YNPVVIDFEM*
0.68
0.73

C8 alpha chain precursor
(CO8A_HUMAN)
QPIHEVLR.H

complement component
P07358
K.VEPLYELVTA
0.69
0.70

C8 beta chain
(CO8B_HUMAN)
TDFAYSSTVR.Q

preproprotein

complement component
P07358
R.SLM*LHYEFL
0.61
0.65

C8 beta chain
(CO8B_HUMAN)
QR.V

preproprotein

complement component
P07360
K.YGFCEAADQF
0.78
0.76

C8 gamma chain
(CO8G_HUMAN)
HVLDEVRR.-

precursor

complement component
P07360
R.FLQEQGHR.A
0.63
0.69

C8 gamma chain
(CO8G_HUMAN)

precursor

complement component
P07360
R.KLDGICWQV
0.75
0.70

C8 gamma chain
(CO8G_HUMAN)
R.Q

precursor

complement component
P07360
R.SLPVSDSVLS
0.70
0.60

C8 gamma chain
(CO8G_HUMAN)
GFEQR.V

precursor

complement component
P02748
R.GTVIDVTDFV
0.68
0.69

C9 precursor
(CO9_HUMAN)
NWASSINDAPV

LISQK.L

complement factor B
P00751
K.NPREDYLDV
0.72
0.77

preproprotein
(CFAB_HUMAN)
YVFGVGPLVNQ

VNINALASK.K

complement factor B
P00751
R.GDSGGPLIVH
0.60
0.76

preproprotein
(CFAB_HUMAN)
KR.S

complement factor B
P00751
R.HVIILMTDGL
0.60
0.64

preproprotein
(CFAB_HUMAN)
HNM*GGDPITVI

DEIR.D

complement factor B
P00751
R.KNPREDYLDV
0.63
0.63

preproprotein
(CFAB_HUMAN)
YVFGVGPLVNQ

VNINALASK.K

complement factor H
P08603
K.SCDIPVFMNA
0.62
0.71

isoform a precursor
(CFAH_HUMAN)
R.T

complement factor H
P08603
K.SPPEISHGVV
0.88
0.88

isoform a precursor
(CFAH_HUMAN)
AHMSDSYQYGE

EVTYK.C

complement factor H
P08603
K.TDCLSLPSFE
0.61
0.66

isoform a precursor
(CFAH_HUMAN)
NAIPMGEKK.D

complement factor I
P05156
K.RAQLGDLPW
0.71
0.74

preproprotein
(CFAI_HUMAN)
QVAIK.D

complement factor I
P05156
K.SLECLHPGT
0.64
0.81

preproprotein
(CFAI_HUMAN)
K.F

complement factor I
P05156
R.TMGYQDFAD
0.73
0.75

preproprotein
(CFAI_HUMAN)
VVCYTQK.A

extracellular matrix
Q16610
R.ELLALIQLE
0.69
0.65

protein 1 isoform 3
(ECM1_HUMAN)
R.E

precursor

gelsolin isoform a
P06396
R.VPEARPNSMV
0.76
0.62

precursor
(GELS_HUMAN)
VEHPEFLK.A

glutathione peroxidase 3
P22352
R.LFWEPMK.V
0.69
0.67

precursor
(GPX3_HUMAN)

hemopexin precursor
P02790
R.DVRDYFMPCP
0.70
0.72

(HEMO_HUMAN)
GR.G

heparin cofactor 2
P05546
K.DALENIDPAT
0.61
0.65

precursor
(HEP2_HUMAN)
QMMILNCIYFK.G

heparin cofactor 2
P05546
K.GLIKDALENI
0.64
0.64

precursor
(HEP2_HUMAN)
DPATQMMILNC

IYFK.G

heparin cofactor 2
P05546
K.QFPILLDFK.T
0.61
0.69

precursor
(HEP2_HUMAN)

heparin cofactor 2
P05546
R.VLKDQVNTF
0.88
0.75

precursor
(HEP2_HUMAN)
DNIFIAPVGISTA

MGMISLGLK.G

insulin-like growth
P35858
R.AFWLDVSHN
0.61
0.82

factor-binding protein
(ALS_HUMAN)
R.L

complex acid labile

subunit isoform 2

precursor

inter-alpha-trypsin
P19827
K.ADVQAHGEG
0.61
0.74

inhibitor heavy chain H1
(ITIH1_HUMAN)
QEFSITCLVDEE

isoform a precursor

EMKK.L

inter-alpha-trypsin
P19827
K.ILGDM*QPGD
0.71
0.63

inhibitor heavy chain H1
(ITIH1_HUMAN)
YFDLVLFGTR.V

isoform a precursor

inter-alpha-trypsin
P19827
K.ILGDMQPGDY
0.68
0.60

inhibitor heavy chain H1
(ITIH1_HUMAN)
FDLVLFGTR.V

isoform a precursor

inter-alpha-trypsin
P19827
K.NVVFVIDISGS
0.76
0.83

inhibitor heavy chain H1
(ITIH1_HUMAN)
MR.G

isoform a precursor

inter-alpha-trypsin
P19827
K.TAFISDFAVT
0.74
0.63

inhibitor heavy chain H1
(ITIH1_HUMAN)
ADGNAFIGDIKD

isoform a precursor

K.V

inter-alpha-trypsin
P19827
R.GHMLENHVE
0.78
0.80

inhibitor heavy chain H1
(ITIH1_HUMAN)
R.L

isoform a precursor

inter-alpha-trypsin
P19827
R.GM*ADQDGL
0.61
0.62

inhibitor heavy chain H1
(ITIH1_HUMAN)
KPTIDKPSEDSP

isoform a precursor

PLEMLGPR.R

inter-alpha-trypsin
P19827
R.LWAYLTIQEL
0.68
0.62

inhibitor heavy chain H1
(ITIH1_HUMAN)
LAK.R

isoform a precursor

inter-alpha-trypsin
P19827
R.NHM*QYEIVI
0.67
0.65

inhibitor heavy chain H1
(ITIH1_HUMAN)
K.V

isoform a precursor

inter-alpha-trypsin
P19823
K.AHVSFKPTVA
0.75
0.61

inhibitor heavy chain H2
(ITIH2_HUMAN)
QQR.I

precursor

inter-alpha-trypsin
P19823
K.ENIQDNISLFS
0.80
0.93

inhibitor heavy chain H2
(ITIH2_HUMAN)
LGM*GFDVDYD

precursor

FLKR.L

inter-alpha-trypsin
P19823
K.ENIQDNISLFS
0.63
0.80

inhibitor heavy chain H2
(ITIH2_HUMAN)
LGMGFDVDYDF

precursor

LKR.L

inter-alpha-trypsin
P19823
K.HLEVDVWVIE
0.61
0.61

inhibitor heavy chain H2
(ITIH2_HUMAN)
PQGLR.F

precursor

inter-alpha-trypsin
P19823
K.LWAYLTINQL
0.69
0.62

inhibitor heavy chain H2
(ITIH2_HUMAN)
LAER.S

precursor

inter-alpha-trypsin
P19823
R.AEDHFSVIDF
0.65
0.63

inhibitor heavy chain H2
(ITIH2_HUMAN)
NQNIR.T

precursor

inter-alpha-trypsin
P19823
R.FLHVPDTFEG
0.66
0.62

inhibitor heavy chain H2
(ITIH2_HUMAN)
HFDGVPVISK.G

precursor

inter-alpha-trypsin
Q14624
K.ILDDLSPR.D
0.67
0.65

inhibitor heavy chain H4
(ITIH4_HUMAN)

isoform 1 precursor

inter-alpha-trypsin
Q14624
K.IPKPEASFSP
0.69
0.77

inhibitor heavy chain H4
(ITIH4_HUMAN)
R.R

isoform 1 precursor

inter-alpha-trypsin
Q14624
K.SPEQQETVLD
0.63
0.69

inhibitor heavy chain H4
(ITIH4_HUMAN)
GNLIIR.Y

isoform 1 precursor

inter-alpha-trypsin
Q14624
K.YIFHNFMER.L
0.66
0.61

inhibitor heavy chain H4
(ITIH4_HUMAN)

isoform 1 precursor

inter-alpha-trypsin
Q14624
R.FSSHVGGTLG
0.69
0.71

inhibitor heavy chain H4
(ITIH4_HUMAN)
QFYQEVLWGSP

isoform 1 precursor

AASDDGRR.T

inter-alpha-trypsin
Q14624
R.GPDVLTATVS
0.63
0.82

inhibitor heavy chain H4
(ITIH4_HUMAN)
GK.L

isoform 1 precursor

inter-alpha-trypsin
Q14624
R.NMEQFQVSVS
0.78
0.60

inhibitor heavy chain H4
(ITIH4_HUMAN)
VAPNAK.I

isoform 1 precursor

inter-alpha-trypsin
Q14624
R.RLDYQEGPPG
0.68
0.62

inhibitor heavy chain H4
(ITIH4_HUMAN)
VEISCWSVEL.-

isoform 1 precursor

kallistatin precursor
P29622
K.IVDLVSELKK.D
0.75
0.67

(KAIN_HUMAN)

kallistatin precursor
P29622
R.VGSALFLSHN
0.70
0.74

(KAIN_HUMAN)
LK.F

kininogen-1 isoform 2
P01042
K.IYPTVNCQPL
0.89
0.62

precursor
(KNG1_HUMAN)
GM*ISLM*K.R

kininogen-1 isoform 2
P01042
K.TVGSDTFYSF
0.61
0.68

precursor
(KNG1_HUMAN)
K.Y

kininogen-1 isoform 2
P01042
R.DIPTNSPELEE
0.61
0.76

precursor
(KNG1_HUMAN)
TLTHTITK.L

kininogen-1 isoform 2
P01042
R.VQVVAGK.K
0.67
0.71

precursor
(KNG1_HUMAN)

lumican precursor
P51884
R.FNALQYLR.L
0.68
0.76

(LUM_HUMAN)

macrophage colony-
P09603
K.VIPGPPALTLV
0.68
0.60

stimulating factor 1
(CSF1_HUMAN)
PAELVR.I

receptor precursor

monocyte differentiation
P08571
K.ITGTMPPLPLE
0.80
0.67

antigen CD14 precursor
(CD14_HUMAN)
ATGLALSSLR.L

N-acetylmuramoyl-L-
Q96PD5
K.EFTEAFLGCP
0.62
0.64

alanine amidase
(PGRP2_HUMAN)
AIHPR.C

precursor

N-acetylmuramoyl-L-
Q96PD5
R.RVINLPLDSM
0.63
0.62

alanine amidase
(PGRP2_HUMAN)
AAPWETGDTFP

precursor

DVVAIAPDVR.A

phosphatidylinositol-
P80108
R.GVFFSVNSWT
0.67
0.78

glycan-specific
(PHLD_HUMAN)
PDSMSFIYK.A

phospholipase D

precursor

pigment epithelium-

P36955

K.EIPDEISILLLGVAHF
0.63
0.61

derived factor precursor
(PEDF_HUMAN)
K.G

pigment epithelium-

P36955

K.IAQLPLTGSM*SIIF
0.79
0.61

derived factor precursor
(PEDF_HUMAN)
FLPLK.V

pigment epithelium-

P36955

K.TVQAVLTVPK.L
0.75
0.79

derived factor precursor
(PEDF_HUMAN)

pigment epithelium-

P36955

R.ALYYDLISSPDIHGT
0.60
0.73

derived factor precursor
(PEDF_HUMAN)
YKELLDTVTAPQK.N

pigment epithelium-

P36955

R.DTDTGALLFIGK.I
0.85
0.62

derived factor precursor
(PEDF_HUMAN)

plasminogen isoform 1

P00747

R.ELRPWCFTTDPNK
0.70
0.68

precursor
(PLMN_HUMAN)
R.W

plasminogen isoform 1

P00747

R.TECFITGWGETQGT
0.63
0.68

precursor
(PLMN_HUMAN)
FGAGLLK.E

platelet basic protein

P02775

K.GTHCNQVEVIATL
0.60
0.61

preproprotein
(CXCL7_HUMAN)
K.D

pregnancy zone protein

P20742

K.AVGYLITGYQR.Q
0.87
0.73

precursor
(PZP_HUMAN)

pregnancy zone protein

P20742

R.AVDQSVLLM*KPE
0.64
0.62

precursor
(PZP_HUMAN)
AELSVSSVYNLLTVK.D

pregnancy zone protein

P20742

R.IQHPFTVEEFVLP
0.66
0.74

precursor
(PZP_HUMAN)
K.F

pregnancy zone protein

P20742

R.NELIPLIYLENPR.R
0.61
0.61

precursor
(PZP_HUMAN)

protein AMBP

P02760

R.AFIQLWAFDAVK.G
0.72
0.67

preproprotein
(AMBP_HUMAN)

proteoglycan 4 isoform B

Q92954

K.GFGGLTGQIVAALS
0.70
0.72

precursor
(PRG4_HUMAN)
TAK.Y

prothrombin preproprotein

P00734

K.YGFYTHVFR.L
0.70
0.63

(THRB_HUMAN)

prothrombin preproprotein

P00734

R.IVEGSDAEIGM*SP
0.63
0.71

(THRB_HUMAN)
WQVMLFR.K

retinol-binding protein 4

P02753

K.KDPEGLFLQDNIVA
0.67
0.67

precursor
(RET4_HUMAN)
EFSVDETGQMSATAK.G

thyroxine-binding globulin

P05543

K.AQWANPFDPSKTE
0.67
0.80

precursor
(THBG_HUMAN)
DSSSFLIDK.T

thyroxine-binding globulin

P05543

K.GWVDLFVPK.F
0.67
0.64

precursor
(THBG_HUMAN)

thyroxine-binding globulin

P05543

R.SFM*LLILER.S
0.65
0.68

precursor
(THBG_HUMAN)

thyroxine-binding globulin

P05543

R.SFMLLILER.S
0.64
0.62

precursor
(THBG_HUMAN)

vitamin D-binding protein

P02774

K.EFSHLGKEDFTSLSL
0.74
0.61

isoform 1 precursor
(VTDB_HUMAN)
VLYSR.K

vitamin D-binding protein

P02774

K.EYANQFM*WEYST
0.73
0.61

isoform 1 precursor
(VTDB_HUMAN)
NYGQAPLSLLVSYTK.S

vitamin D-binding protein

P02774

K.HQPQEFPTYVEPTN
0.67
0.69

isoform 1 precursor
(VTDB_HUMAN)
DEICEAFRK.D

vitamin D-binding protein

P02774

K.SYLSM*VGSCCTSA
0.63
0.62

isoform 1 precursor
(VTDB_HUMAN)
SPTVCFLK.E

vitamin D-binding protein

P02774

K.TAM*DVFVCTYFM
0.63
0.60

isoform 1 precursor
(VTDB_HUMAN)
PAAQLPELPDVELPT

NK.D

vitamin D-binding protein

P02774

K.VPTADLEDVLPLAE
0.70
0.71

isoform 1 precursor
(VTDB_HUMAN)
DITNILSK.C

vitronectin precursor

P04004

K.AVRPGYPK.L
0.68
0.77

(VTNC_HUMAN)

vitronectin precursor

P04004

R.MDWLVPATCEPIQ
0.67
0.65

(VTNC_HUMAN)
SVFFFSGDK.Y

zinc-alpha-2-glycoprotein

P25311

K.EIPAWVPFDPAAQI
0.63
0.67

precursor
(ZA2G_HUMAN)
TK.Q

The differentially expressed proteins identified by the hypothesis-independent strategy above, not already present in our MRM-MS assay, were candidates for incorporation into the MRM-MS assay. Two additional proteins (AFP, PGH1) of functional interest were also selected for MRM development. Candidates were prioritized by AUC and biological function, with preference give for new pathways. Sequences for each protein of interest, were imported into Skyline software which generated a list of tryptic peptides, m/z values for the parent ions and fragment ions, and an instrument-specific collision energy (McLean et al. Bioinformatics (2010) 26 (7): 966-968; McLean et al. Anal. Chem (2010) 82 (24): 10116-10124).

The list was refined by eliminating peptides containing cysteines and methionines, and by using the shotgun data to select the charge state(s) and a subset of potential fragment ions for each peptide that had already been observed on a mass spectrometer.

After prioritizing parent and fragment ions, a list of transitions was exported with a single predicted collision energy. Approximately 100 transitions were added to a single MRM run. For development, MRM data was collected on either a QTRAP 5500 (AB Sciex) or a 6490 QQQ (Agilent). Commercially available human female serum (from pregnant and non-pregnant donors), was depleted and processed to tryptic peptides, as described above, and used to “scan” for peptides of interest. In some cases, purified synthetic peptides were used for further optimization. For development, digested serum or purified synthetic peptides were separated with a 15 min acetonitrile gradient at 100 ul/min on a 2.1×50 mM Poroshell 120 EC-C18 column (Agilent) at 40° C.

The MS/MS data was imported back into Skyline, where all chromatograms for each peptide were overlayed and used to identify a consensus peak corresponding to the peptide of interest and the transitions with the highest intensities and the least noise. Table 11, contains a list of the most intensely observed candidate transitions and peptides for transfer to the MRM assay.

TABLE 11

Candidate peptides and transitions for transferring to the MRM assay

fragment ion, m/z,

Protein
Peptide
m/z, charge
charge, rank
area

alpha-1-antichymotrypsin
K.ADLSGITGAR.N
480.7591++
S[y7]-661.3628+[1]
1437602

G[y6]-574.3307+[2]
637584

T[y4]-404.2252+[3]
350392

L[y8]-774.4468+[4]
191870

G[y3]-303.1775+[5]
150575

I[y5]-517.3093+[6]
97828

alpha-1-antichymotrypsin
K.EQLSLLDR.F
487.2693++
S[y5]-603.3461[1]
345602

L[y6]-716.4301[2]
230046

L[y4]-516.3140[3]
143874

D[y2]-290.1459[4]
113381

D[y2]-290.1459[5]
113381

Q[b2]-258.1084[6]
78157

alpha-1-antichymotrypsin
K.ITLLSALVETR.T
608.3690++
S[y7]-775.4308+[1]
1059034

L[y8]-888.5149+[2]
541969

T[b2]-215.1390+[3]
408819

L[y9]-1001.5990+[4]
438441

V[y4]-504.2776+[5]
311293

L[y5]-617.3617+[6]
262544

L[b3]-328.2231+[7]
197526

T[y2]-276.1666+[8]
212816

E[y3]-405.2092+[9]
207163

alpha-1-antichymotrypsin
R.EIGELYLPK.F
531.2975++
G[y7]-819.4611+[2]
977307

L[y5]-633.3970+[3]
820582

Y[y4]-520.3130+[4]
400762

L[y3]-357.2496+[5]
498958

P[y2]-244.1656+[1]
1320591

I[b2]-243.1339+[6]
303268

G[b3]-300.1554+[7]
305120

alpha-1-antichymotrypsin
R.GTHVDLGLASA
742.3794+++
D[y8]-990.4931+[1]
154927

NVDFAFSLYK.Q

L[b8]-793.4203+[2]
51068

D[b5]-510.2307+[3]
45310

F[y7]-875.4662+[4]
42630

A[b9]-864.4574+[5]
43355

S[y4]-510.2922+[6]
45310

F[y5]-657.3606+[7]
37330

V[y9]-1089.5615+[8]
32491

G[b7]-680.3362+[9]
38185

Y[y2]-310.1761+[10]
36336

N[b12]-1136.5695+[11]
16389

S[b10]-951.4894+[12]
16365

L[b6]-623.3148+[13]
13687

L[y3]-423.2602+[14]
17156

V[b4]-395.2037+[15]
10964

alpha-1-antichymotrypsin
R.NLAVSQVVHK.A
547.8195++
A[y8]-867.5047+[1]
266203

365.5487+++
L[b2]-228.1343+[2]
314232

V[y7]-796.4676+[3]
165231

A[b3]-299.1714+[4]
173694

S[y6]-697.3991+[5]
158512

H[y2]-284.1717+[6]
136431

V[b4]-398.2398+[7]
36099

S[b5]-485.2718+[8]
23836

S[y6]-697.3991+[1]
223443

V[y3]-383.2401+[2]
112952

V[y4]-482.3085+[3]
84872

Q[y5]-610.3671+[4]
30835

inter-alpha-trypsin
K.AAISGENAGLVR.A
579.3173++
S[y9]-902.4690+[1]
518001

inhibitor heavy chain H1

G[y8]-815.4370+[2]
326256

N[y6]-629.3729+[3]
296670

S[b4]-343.1976+[4]
258172

inter-alpha-trypsin
K.GSLVQASEANL
668.6763+++
A[y7]-806.4155+[1]
304374

inhibitor heavy chain H1
QAAQDFVR.G

A[y6]-735.3784+[2]
193844

V[b4]-357.2132+[3]
294094

F[y3]-421.2558+[4]
167816

A[b6]-556.3089+[5]
149216

L[b11]-535.7775++[6]
156882

A[b13]-635.3253++[7]
249287

A[y14]-760.3786++[8]
123723

F[b17]-865.9208++[9]
23057

inter-alpha-trypsin
K.TAFISDFAVTAD
1087.0442++
G[y4]-432.2453+[1]
22362

inhibitor heavy chain H1
GNAFIGDIK.D

I[y5]-545.3293+[2]
8319

A[b8]-853.4090+[3]
7006

G[y9]-934.4993+[4]
6755

F[y6]-692.3978+[5]
6193

V[b9]-952.4775+[6]
9508

inter-alpha-trypsin
K.VTYDVSR.D
420.2165++
Y[y5]-639.3097+[1]
609348

inhibitor heavy chain H1

T[b2]-201.1234+[2]
792556

D[y4]-476.2463+[3]
169546

V[y3]-361.2194+[4]
256946

Y[y5]-320.1585++[5]
110608

S[y2]-262.1510+[6]
50268

Y[b3]-182.5970++[7]
10947

D[b4]-479.2136+[8]
13662

inter-alpha-trypsin
R.EVAFDLEIPK.T
580.8135++
P[y2]-244.1656+[1]
2032509

inhibitor heavy chain H1

D[y6]-714.4032+[2]
672749

A[y8]-932.5088+[3]
390837

L[y5]-599.3763+[4]
255527

F[y7]-861.4716+[5]
305087

inter-alpha-trypsin
R.LWAYLTIQELLAK.R
781.4531++
W[b2]-300.1707+[1]
602601

inhibitor heavy chain H1

A[b3]-371.2078+[2]
356967

T[y8]-915.5510+[3]
150419

Y[b4]-534.2711+[4]
103449

I[y7]-814.5033+[5]
72044

Q[y6]-701.4192+[6]
66989

L[b5]-647.3552+[7]
99820

E[y5]-573.3606+[8]
44843

inter-alpha-trypsin
K.FYNQVSTPLLR.N
669.3642++
S[y6]-686.4196+[1]
367330

inhibitor heavy chain H2

V[y7]-785.4880+[2]
182396

P[y4]-498.3398+[3]
103638

Y[b2]-311.1390+[4]
52172

Q[b4]-553.2405+[5]
54270

N[b3]-425.1819+[6]
34567

inter-alpha-trypsin
K.HLEVDVWVIEPQGLR.F
597.3247+++
I[y7]-812.4625+[1]
206996

inhibitor heavy chain H2

P[y5]-570.3358+[2]
303693

E[y6]-699.3784+[3]
126752

P[y5]-285.6715++[4]
79841

inter-alpha-trypsin
K.TAGLVR.S
308.6925++
A[b2]-173.0921+[1]
460019

inhibitor heavy chain H2

G[y4]-444.2929+[2]
789068

V[y2]-274.1874+[3]
34333

G[b3]-230.1135+[4]
15169

L[y3]-387.2714+[5]
29020

inter-alpha-trypsin
R.IYLQPGR.L
423.7452++
L[y5]-570.3358+[1]
638209

inhibitor heavy chain H2

P[y3]-329.1932+[2]
235194

Y[b2]-277.1547+[3]
266889

Q[y4]-457.2518+[4]
171389

inter-alpha-trypsin
R.LSNENHGIAQR.I
413.5461+++
N[y9]-519.7574++[1]
325409

inhibitor heavy chain H2

N[y7]-398.2146++[2]
39521

G[y5]-544.3202+[3]
139598

S[b2]-201.1234+[4]
54786

E[y8]-462.7359++[5]
30623

inter-alpha-trypsin
R.SLAPTAAAKR.R
415.2425++
A[y7]-629.3617+[1]
582421

inhibitor heavy chain H2

L[b2]-201.1234+[2]
430584

P[y6]-558.3246+[3]
463815

A[b3]-272.1605+[4]
204183

T[y5]-461.2718+[5]
47301

inter-alpha-trypsin
K.EVSFDVELPK.T
581.8032++
P[y2]-244.1656+[1]
132304

inhibitor heavy chain H3

V[b2]-229.1183+[2]
48895

L[y3]-357.2496+[3]
20685

inter-alpha-trypsin
K.IQENVR.N
379.7114++
E[y4]-517.2729+[1]
190296

inhibitor heavy chain H3

E[b3]-371.1925+[2]
51697

Q[b2]-242.1499+[3]
54241

N[y3]-388.2303+[4]
21156

V[y2]-274.1874+[5]
8309

inter-alpha-trypsin
R.ALDLSLK.Y
380.2342++
D[y5]-575.3399+[1]
687902

inhibitor heavy chain H3

L[b2]-185.1285+[2]
241010

L[y2]-260.1969+[3]
29365

inter-alpha-trypsin
R.LIQDAVTGLTVN
972.0258++
V[b6]-640.3665+[1]
139259

inhibitor heavy chain H3
GQITGDK.R

G[b8]-798.4356+[2]
53886

G[y7]-718.3730+[3]
12518

pigment epithelium-
K.SSFVAPLEK.S
489.2687++
A[y5]-557.3293+[1]
13436

derived factor precursor

V[y6]-656.3978+[2]
9350

F[y7]-803.4662+[3]
6672

P[y4]-486.2922+[4]
6753

pigment epithelium-
K.TVQAVLTVPK.L
528.3266++
Q[y8]-855.5298+[1]
26719

derived factor precursor

V[b2]-201.1234+[2]
21239

Q[y8]-428.2686++[3]
16900

A[y7]-727.4713+[4]
9518

L[y5]-557.3657+[5]
5108

Q[b3]-329.1819+[6]
5450

V[y6]-656.4341+[7]
4391

pigment epithelium-
R.ALYYDLISSPDIH
652.6632+++
Y[y15]-886.4305++[1]
78073

derived factor precursor
GTYK.E

Y[y14]-804.8988++[2]
26148

pigment epithelium-
R.DTDTGALLFIGK.I
625.8350++
G[y8]-818.5135+[1]
25553

derived factor precursor

T[b2]-217.0819+[2]
22716

T[b4]-217.0819++[3]
22716

L[y5]-577.3708+[4]
11600

I[y3]-317.2183+[5]
11089

A[b6]-561.2151+[6]
6956

pigment epithelium-
K.ELLDTVTAPQK.N
607.8350++
T[y5]-544.3089+[1]
17139

derived factor precursor

D[y8]-859.4520+[2]
17440

L[y9]-972.5360+[3]
14344

A[y4]-443.2613+[4]
11474

T[y7]-744.4250+[5]
10808

V[y6]-643.3774+[6]
9064

pregnancy-specific beta-
K.FQLPGQK.L
409.2320++
L[y5]-542.3297+[1]
116611

1-glycoprotein 1

P[y4]-429.2456+[2]
91769

Q[b2]-276.1343+[3]
93301

pregnancy-specific beta-
R.DLYHYITSYVVD
955.4762+++
G[y7]-707.3471+[1]
5376

1-glycoprotein 1
GEIIIYGPAYSGR.E

Y[y8]-870.4104+[2]
3610

P[y6]-650.3257+[3]
2770

I[y9]-983.4945+[4]
3361

pregnancy-specific beta-
K.LFIPQITPK.H
528.8262++
P[y6]-683.4087+[1]
39754

1-glycoprotein 11

F[b2]-261.1598+[2]
29966

I[y7]-796.4927+[3]
13162

pregnancy-specific beta-
NSATGEESSTSLTIR
776.8761++
E[b7]-689.2737+[1]
11009

1-glycoprotein 11

T[y6]-690.4145+[2]
11284

L[y4]-502.3348+[3]
2265

S[y7]-389.2269++[4]
1200

T[y3]-389.2507+[5]
1200

I[y2]-288.2030+[6]
2248

pregnancy-specific beta-
K.FQQSGQNLFIP
617.3317+++
F[y8]-474.2817++[1]
43682

1-glycoprotein 2
QITTK.H

G[y12]-680.3852++[2]
24166

S[b4]-491.2249+[3]
23548

Q[b3]-404.1928+[4]
17499

I[y4]-462.2922+[5]
17304

F[b9]-525.7538++[6]
17206

I[b10]-582.2958++[7]
16718

L[b8]-452.2196++[8]
16490

P[y6]-344.2054++[9]
16198

G[b5]-548.2463+[10]
15320

pregnancy-specific beta-
IHPSYTNYR
575.7856++
N[b7]-813.3890+[1]
16879

1-glycoprotein 2

Y[b5]-598.2984+[2]
18087

T[y4]-553.2729+[3]
2682

pregnancy-specific beta-
FQLSETNR
497.7513++
L[y6]-719.3682+[1]
358059

1-glycoprotein 2

S[y5]-606.2842+[2]
182330

Q[b2]-276.1343+[3]
292482

pregnancy-specific beta-
VSAPSGTGHLPGLNPL
506.2755+++
T[b7]-300.6530++[1]
25346

1-glycoprotein 3

H[y8]-860.4989+[2]
12159

H[y8]-430.7531++[3]
15522

pregnancy-specific beta-
EDAGSYTLHIVK
666.8433++
Y[b6]-623.2307+[1]
23965

1-glycoprotein 3

Y[y7]-873.5193+[2]
21686

L[b8]-837.3625+[3]
4104

A[b3]-316.1139+[4]
1987

pregnancy-specific beta-
R.TLFIFGVTK.Y
513.3051++
F[y7]-811.4713+[1]
62145

1-glycoprotein 4

L[b2]-215.1390+[2]
31687

F[y5]-551.3188+[3]
972

pregnancy-specific beta-
NYTYIWWLNGQS
1097.5576++
W[b6]-841.3879+[1]
25756

1-glycoprotein 4
LPVSPR

G[y9]-940.5211+[2]
25018

Y[b4]-542.2245+[3]
19778

Q[y8]-883.4996+[4]
6642

P[y2]-272.1717+[5]
5018

pregnancy-specific beta-
GVTGYFTFNLYLK
508.2695+++
L[y2]-260.1969+[1]
176797

1-glycoprotein 5

T[y11]-683.8557++[2]
136231

F[b6]-625.2980+[3]
47523

L[y4]-536.3443+[4]
23513

pregnancy-specific beta-
SNPVTLNVLYGPD
585.6527+++
Y[y7]-817.4203+[1]
14118

1-glycoprotein 6
LPR

G[y6]-654.3570+[2]
10433

P[b3]-299.1350+[3]
87138*

P[y5]-299.1714++[4]
77478*

P[y5]-597.3355+[5]
68089*

pregnancy-specific beta-
DVLLLVHNLPQNL
791.7741+++
L[y8]-1017.5516+[3]
141169

1-glycoprotein 7
TGHIWYK

G[y6]-803.4199+[5]
115905

W[y3]-496.2554+[6]
108565

P[y11]-678.8566++[7]
105493

V[b2]-215.1026+[1]
239492

L[b3]-328.1867+[2]
204413

N[b8]-904.5251+[4]
121880

pregnancy-specific beta-
YGPAYSGR
435.7089++
A[y5]-553.2729+[1]
25743*

1-glycoprotein 7

Y[y4]-482.2358+[2]
25580*

P[y6]-650.3257+[3]
10831*

S[y3]-319.1724+[4]
10559*

G[b2]-221.0921+[5]
7837*

pregnancy-specific beta-
LQLSETNR
480.7591++
S[b4]-442.2660+[1]
18766

1-glycoprotein 8

L[b3]-355.2340+[2]
12050

Q[b2]-242.1499+[3]
1339

T[b6]-672.3563+[4]
2489

pregnancy-specific beta-
K.LFIPQITR.N
494.3029++
P[y5]-614.3620+[1]
53829

1-glycoprotein 9

I[y6]-727.4461+[2]
13731

I[b3]-374.2438+[3]
4178

Q[y4]-517.3093+[4]
2984

pregnancy-specific beta-
K.LPIPYITINNLNPR.E
819.4723++
P[b2]-211.1441+[1]
18814*

1-glycoprotein 9

P[b4]-211.1441++[2]
18814*

T[b7]-798.4760+[3]
17287*

T[y8]-941.5163+[4]
10205*

Y[b5]-584.3443+[5]
10136*

N[y6]-727.3846+[6]
9511*

pregnancy-specific beta-
R.SNPVILNVLYGP
589.6648+++
P[y5]-597.3355+[1]
3994

1-glycoprotein 9
DLPR.I

Y[y7]-817.4203+[2]
3743

G[y6]-654.3570+[3]
3045

pregnancy-specific beta-
DVLLLVHNLPQNL
810.4387+++
P[y7]-960.4614+[1]
120212

1-glycoprotein 9
PGYFWYK

V[b2]-215.1026+[2]
65494

L[b3]-328.1867+[3]
54798

pregnancy-specific beta-
SENYTYIWWLNG
846.7603+++
W[y15]-834.4488++[1]
14788

1-glycoprotein 9
QSLPVSPGVK

P[y4]-200.6314++[2]
19000

Y[y17]-972.5225++[3]
4596

L[b10]-678.8166++[4]
2660

Y[b6]-758.2992+[5]
1705

P[y4]-400.2554+[6]
1847

Pan-PSG
ILILPSVTR
506.3317++
P[y5]-559.3198+[1]
484395

L[b2]-227.1754+[2]
102774

L[b4]-227.1754++[3]
102774

I[y7]-785.4880+[4]
90153

I[b3]-340.2595+[5]
45515

L[y6]-672.4039+[6]
40368

thyroxine-binding
K.AQWANPFDPSK.T
630.8040++
A[b4]-457.2194+[1]
30802

globulin precursor

S[y2]-234.1448+[2]
28255

D[y4]-446.2245+[3]
24933

thyroxine-binding
K.AVLHIGEK.G
289.5080+++
I[y4]-446.2609+[1]
220841

globulin precursor

H[y5]-292.1636++[2]
303815

H[y5]-583.3198+[3]
133795

V[b2]-171.1128+[4]
166139

L[y6]-348.7056++[5]
823533

thyroxine-binding
K.FLNDVK.T
368.2054++
N[y4]-475.2511+[1]
296859

globulin precursor

V[y2]-246.1812+[2]
219597

L[b2]-261.1598+[3]
87504

thyroxine-binding
K.FSISATYDLGATL
800.4351++
Y[y9]-993.5615+[1]
34111

globulin precursor
LK.M

G[y6]-602.3872+[2]
17012

D[y8]-830.4982+
45104

S[b2]-235.1077+[4]
15480

thyroxine-binding
K.GWVDLFVPK.F
530.7949++
W[b2]-244.1081+[1]
1261810

globulin precursor

P[y2]-244.1656+[2]
1261810

V[b7]-817.4243+[3]
517675

V[y7]-817.4818+[4]
517675

D[y6]-718.4134+[5]
306994

F[b6]-718.3559+[6]
306994

V[y3]-343.2340+[7]
112565

V[b3]-343.1765+[8]
112565

thyroxine-binding
K.NALALFVLPK.E
543.3395++
A[y7]-787.5076+[1]
198085

globulin precursor

L[b3]-299.1714+[2]
199857

P[y2]-244.1656+[3]
129799

L[y8]-900.5917+[4]
111572

L[y6]-716.4705+[5]
88773

F[y5]-603.3865+[6]
54020

L[y3]-357.2496+[7]
43353

thyroxine-binding
R.SILFLGK.V
389.2471++
L[y5]-577.3708+[1]
1878736

globulin precursor

I[b2]-201.1234+[2]
946031

G[y2]-204.1343+[3]
424248

L[y3]-317.2183+[4]
291162

F[y4]-464.2867+[5]
391171

AFP
R.DFNQFSSGEK.N
386.8402+++
N[b3]-189.0764++[1]
42543

S[y4]-210.6081++[2]
21340

G[y3]-333.1769+[3]
53766

N[b3]-377.1456+[4]
58644

F[b2]-263.1026+[5]
5301

AFP
K.GYQELLEK.C
490.2584++
E[y5]-631.3661+[1]
110518

L[y4]-502.3235+[2]
74844

E[y2]-276.1554+[3]
42924

E[b4]-478.1932+[4]
20953

AFP
K.GEEELQK.Y
416.7060++
E[b2]-187.0713+[1]
37843

E[y4]-517.2980+[2]
56988

AFP
K.FIYEIAR.R
456.2529++
I[y3]-359.2401+[1]
34880

I[b2]-261.1598+[2]
7931

AFP
R.HPFLYAPTILL
590.3348+++
I[y7]-421.7660++[1]
11471

WAAR.Y

L[y6]-365.2239++[2]
5001

A[b6]-365.1896++[3]
5001

L[y6]-729.4406+[4]
3218

F[b3]-382.1874+[5]
6536

A[b6]-729.3719+[6]
3218

AFP
R.TFQAITVTK.L
504.7898++
T[b6]-662.3508+[1]
11241

T[y4]-448.2766+[2]
7541

A[b4]-448.2191+[3]
7541

AFP
K.LTTLER.G
366.7162++
T[y4]-518.2933+[1]
7836

L[b4]-215.1390++[2]
4205

T[b2]-215.1390+[3]
4205

AFP
R.HPQLAVSVILR.V

L[y2]-288.2030+[1]
3781

I[y3]-401.2871+[2]
2924

L[b4]-476.2616+[3]
2647

AFP
K.LGEYYLQNAFLV
631.6646+++
G[b2]-171.1128+[1]
10790

AYTK.K

Y[y3]-411.2238+[2]
2303

F[b10]-600.2902++[3]
1780

Y[b4]-463.2187+[4]
2214

F[y7]-421.2445++[6]
3072

PGH1
R.ILPSVPK.D
377.2471++
P[y5]-527.3188+[1]
5340492

S[y4]-430.2660+[5]
419777

P[y2]-244.1656+[2]
4198508

P[y5]-264.1630++[3]
2771328

L[b2]-227.1754+[4]
2331263

PGH1
K.AEHPTWGDEQL
639.3026+++
E[b9]-512.2120++[1]
64350

FQTTR.L

P[b4]-218.1030++[2]
38282

L[b11]-632.7833++[3]
129128

G[y10]-597.7911++[4]
19406

G[b7]-779.3471+[5]
51467

T[y3]-189.1108++[6]
10590

D[y9]-569.2804++[7]
12460

L[y6]-765.4254+[8]
6704

D[b8]-447.6907++[9]
4893

P[b4]-435.1987+[10]
8858

Q[y7]-893.4839+[11]
6101

T[b5]-268.6268++[12]
5456

T[b5]-536.2463+[13]
5549

PGH1
R.LILIGETIK.I
500.3261++
G[y5]-547.3086+[1]
7649

T[y3]-361.2445+[2]
6680

E[y4]-490.2871+[3]
5234

L[y7]-773.4767+[4]
3342

PGH1
R.LQPFNEYR.K
533.7694++
N[b5]-600.3140+[1]
25963

F[b4]-486.2711+[2]
6915

E[y3]-467.2249+[3]
15079

*QTRAP5500 data, all other peak areas are from Agilent 6490

Next, the top 2-10 transitions per peptide and up to 7 peptides per protein were selected for collision energy (CE) optimization on the Agilent 6490. Using Skyline or MassHunter Qual software, the optimized CE value for each transition was determined based on the peak area or signal to noise. The two transitions with the largest peak areas per peptide and at least two peptides per protein were chosen for the final MRM method. Substitutions of transitions with lower peak areas were made when a transition with a larger peak area had a high background level or had a low m/z value that has more potential for interference.

Lastly, the retention times of selected peptides were mapped using the same column and gradient as our established sMRM assay. The newly discovered analytes were subsequently added to the sMRM method and used in a further hypothesis-dependent discovery study described in Example 5 below.

The above method was typical for most proteins. However, in some cases, the differentially expressed peptide identified in the shotgun method did not uniquely identify a protein, for example, in protein families with high sequence identity. In these cases, a MRM method was developed for each family member. Also, let it be noted that, for any given protein, peptides in addition to those found to be significant and fragment ions not observed on the Orbitrap may have been included in MRM optimization and added to the final sMRM method if those yielded the best signal intensities.

Example 5. Study IV to Identify and Confirm Preterm Birth Biomarkers

A further hypothesis-dependent discovery study was performed with the scheduled MRM assay used in Examples 3 but now augmented with newly discovered analytes from the Example 4. Less robust transitions (from the original 1708 described in Example 1) were removed to improve analytical performance and make room for the newly discovered analytes. Samples included approximately 30 cases and 60 matched controls from each of three gestational periods (early, 17-22 weeks, middle, 23-25 weeks and late, 26-28 weeks). Log transformed peak areas for each transition were corrected for run order and batch effects by regression. The ability of each analyte to separate cases and controls was determined by calculating univariate AUC values from ROC curves. Ranked univariate AUC values (0.6 or greater) are reported for individual gestational age window sample sets (Tables 12, 13, 15) and a combination of the middle and late window (Table 14). Multivariate classifiers were built using different subsets of analytes (described below) by Lasso and Random Forest methods. Lasso significant transitions correspond to those with non-zero coefficients and Random Forest analyze ranking was determined by the Gini importance values (mean decrease in model accuracy if that variable is removed). We report all analytes with non-zero Lasso coefficients (Tables 16-32) and the top 30 analytes from each Random Forest analysis (Tables 33-49). Models were built considering the top univariate 32 or 100 analytes, the single best univariate analyte for the top 50 proteins or all analytes. Lastly 1000 rounds of bootstrap resampling were performed and the nonzero Lasso coefficients or Random Forest Gini importance values were summed for each analyte amongst panels with AUCs of 0.85 or greater.

TABLE 12

Early Window Individual Stats

Transition
Protein
AUC

ELIEELVNITQNQK_557.6_517.3
IL13_HUMAN
0.834

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN
0.822

FLNWIK_410.7_560.3
HABP2_HUMAN
0.820

ITLPDFTGDLR_624.3_920.5
LBP_HUMAN
0.808

SFRPFVPR_335.9_635.3
LBP_HUMAN
0.800

LIQDAVTGLTVNGQITGDK_972.0_
ITIH3_HUMAN
0.800

798.4

FSVVYAK_407.2_579.4
FETUA_HUMAN
0.796

ITGFLKPGK_320.9_429.3
LBP_HUMAN
0.796

AHYDLR_387.7_288.2
FETUA_HUMAN
0.796

FSVVYAK_407.2_381.2
FETUA_HUMAN
0.795

SFRPFVPR_335.9_272.2
LBP_HUMAN
0.795

DVLLLVHNLPQNLPGYFWYK_810.4_
PSG9_HUMAN
0.794

967.5

ELIEELVNITQNQK_557.6_618.3
IL13_HUMAN
0.794

QALEEFQK_496.8_680.3
CO8B_HUMAN
0.792

DAGLSWGSAR_510.3_390.2
NEUR4_HUMAN
0.792

AHYDLR_387.7_566.3
FETUA_HUMAN
0.791

VFQFLEK_455.8_811.4
CO5_HUMAN
0.786

ITGFLKPGK_320.9_301.2
LBP_HUMAN
0.783

VFQFLEK_455.8_276.2
CO5_HUMAN
0.782

SLLQPNK_400.2_599.4
CO8A_HUMAN
0.781

VQTAHFK_277.5_431.2
CO8A_HUMAN
0.780

SDLEVAHYK_531.3_617.3
CO8B_HUMAN
0.777

SLLQPNK_400.2_358.2
CO8A_HUMAN
0.776

TLLPVSKPEIR_418.3_288.2
CO5_HUMAN
0.776

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.774

DISEVVTPR_508.3_787.4
CFAB_HUMAN
0.774

VSEADSSNADWVTK_754.9_533.3
CFAB_HUMAN
0.773

LSSPAVITDK_515.8_743.4
PLMN_HUMAN
0.773

VQEAHLTEDQIFYFPK_655.7_701.4
CO8G_HUMAN
0.772

DVLLLVHNLPQNLPGYFWYK_810.4_
PSG9_HUMAN
0.771

594.3

ALVLELAK_428.8_672.4
INHBE_HUMAN
0.770

FLNWIK_410.7_561.3
HABP2_HUMAN
0.770

LSSPAVITDK_515.8_830.5
PLMN_HUMAN
0.769

LPNNVLQEK_527.8_844.5
AFAM_HUMAN
0.769

VSEADSSNADWVTK_754.9_347.2
CFAB_HUMAN
0.768

HTLNQIDEVK_598.8_951.5
FETUA_HUMAN
0.767

TTSDGGYSFK_531.7_860.4
INHA_HUMAN
0.761

YENYTSSFFIR_713.8_756.4
IL12B_HUMAN
0.760

HTLNQIDEVK_598.8_958.5
FETUA_HUMAN
0.760

DISEVVTPR_508.3_472.3
CFAB_HUMAN
0.760

LIQDAVTGLTVNGQITGDK_972.0_
ITIH3_HUMAN
0.759

640.4

EAQLPVIENK_570.8_699.4
PLMN_HUMAN
0.759

SLPVSDSVLSGFEQR_810.9_836.4
CO8G_HUMAN
0.757

AVLHIGEK_289.5_348.7
THBG_HUMAN
0.755

GLQYAAQEGLLALQSELLR_1037.1_
LBP_HUMAN
0.752

929.5

FLQEQGHR_338.8_497.3
CO8G_HUMAN
0.750

LPNNVLQEK_527.8_730.4
AFAM_HUMAN
0.750

AVLHIGEK_289.5_292.2
THBG_HUMAN
0.749

QLYGDTGVLGR_589.8_501.3
CO8G_HUMAN
0.748

WWGGQPLWITATK_772.4_929.5
ENPP2_HUMAN
0.747

NADYSYSVWK_616.8_769.4
CO5_HUMAN
0.746

GLQYAAQEGLLALQSELLR_1037.1_
LBP_HUMAN
0.746

858.5

SLPVSDSVLSGFEQR_810.9_723.3
CO8G_HUMAN
0.745

IEEIAAK_387.2_531.3
CO5_HUMAN
0.743

TYLHTYESEI_628.3_908.4
ENPP2_HUMAN
0.742

WWGGQPLWITATK_772.4_373.2
ENPP2_HUMAN
0.742

FQLSETNR_497.8_605.3
PSG2_HUMAN
0.741

NIQSVNVK_451.3_674.4
GROA_HUMAN
0.741

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
0.740

LQGTLPVEAR_542.3_571.3
CO5_HUMAN
0.740

SGFSFGFK_438.7_732.4
CO8B_HUMAN
0.740

HELTDEELQSLFTNFANVVDK_817.1_
AFAM_HUMAN
0.740

906.5

VQTAHFK_277.5_502.3
CO8A_HUMAN
0.739

YENYTSSFFIR_713.8_293.1
IL12B_HUMAN
0.739

AFTECCVVASQLR_770.9_574.3
CO5_HUMAN
0.736

EAQLPVIENK_570.8_329.2
PLMN_HUMAN
0.734

QALEEFQK_496.8_551.3
CO8B_HUMAN
0.734

DAQYAPGYDK_564.3_813.4
CFAB_HUMAN
0.734

TEFLSNYLTNVDDITLVPGTLGR_
ENPP2_HUMAN
0.734

846.8_600.3

IAIDLFK_410.3_635.4
HEP2_HUMAN
0.733

TASDFITK_441.7_781.4
GELS_HUMAN
0.731

YEFLNGR_449.7_606.3
PLMN_HUMAN
0.731

TVQAVLTVPK_528.3_428.3
PEDF_HUMAN
0.731

LIENGYFHPVK_439.6_627.4
F13B_HUMAN
0.730

DALSSVQESQVAQQAR_573.0_672.4
APOC3_HUMAN
0.730

TVQAVLTVPK_528.3_855.5
PEDF_HUMAN
0.730

ALQDQLVLVAAK_634.9_289.2
ANGT_HUMAN
0.727

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.727

SDLEVAHYK_531.3_746.4
CO8B_HUMAN
0.726

FLPCENK_454.2_550.2
IL10_HUMAN
0.725

HPWIVHWDQLPQYQLNR_744.0_
KS6A3_HUMAN
0.725

1047.0

AFTECCVVASQLR_770.9_673.4
CO5_HUMAN
0.725

YGLVTYATYPK_638.3_843.4
CFAB_HUMAN
0.724

TLEAQLTPR_514.8_685.4
HEP2_HUMAN
0.724

DAQYAPGYDK_564.3_315.1
CFAB_HUMAN
0.724

QGHNSVFLIK_381.6_260.2
HEMO_HUMAN
0.722

HELTDEELQSLFTNFANVVDK_817.1_
AFAM_HUMAN
0.722

854.4

TLEAQLTPR_514.8_814.4
HEP2_HUMAN
0.721

IEEIAAK_387.2_660.4
CO5_HUMAN
0.721

HFQNLGK_422.2_527.2
AFAM_HUMAN
0.721

IAPQLSTEELVSLGEK_857.5_333.2
AFAM_HUMAN
0.721

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
0.720

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
0.719

IAIDLFK_410.3_706.4
HEP2_HUMAN
0.719

FLQEQGHR_338.8_369.2
CO8G_HUMAN
0.719

ALQDQLVLVAAK_634.9_956.6
ANGT_HUMAN
0.718

IEGNLIFDPNNYLPK_874.0_414.2
APOB_HUMAN
0.717

YEFLNGR_449.7_293.1
PLMN_HUMAN
0.717

TASDFITK_441.7_710.4
GELS_HUMAN
0.716

DADPDTFFAK_563.8_825.4
AFAM_HUMAN
0.716

TLLPVSKPEIR_418.3_514.3
CO5_HUMAN
0.716

NADYSYSVWK_616.8_333.2
CO5_HUMAN
0.715

YGLVTYATYPK_638.3_334.2
CFAB_HUMAN
0.715

VNHVTLSQPK_374.9_459.3
B2MG_HUMAN
0.715

HYGGLTGLNK_530.3_759.4
PGAM1_HUMAN
0.714

DFHINLFQVLPWLK_885.5_400.2
CFAB_HUMAN
0.714

NCSFSIIYPVVIK_770.4_555.4
CRHBP_HUMAN
0.714

HPWIVHWDQLPQYQLNR_744.0_
KS6A3_HUMAN
0.712

918.5

AQPVQVAEGSEPDGFWEALGGK_
GELS_HUMAN
0.711

758.0_574.3

ALDLSLK_380.2_185.1
ITIH3_HUMAN
0.711

ALDLSLK_380.2_575.3
ITIH3_HUMAN
0.710

LDFHFSSDR_375.2_611.3
INHBC_HUMAN
0.709

TLNAYDHR_330.5_312.2
PAR3_HUMAN
0.707

EVFSKPISWEELLQ_852.9_260.2
FA40A_HUMAN
0.706

IAPQLSTEELVSLGEK_857.5_533.3
AFAM_HUMAN
0.704

LIENGYFHPVK_439.6_343.2
F13B_HUMAN
0.703

NFPSPVDAAFR_610.8_775.4
HEMO_HUMAN
0.703

QLYGDTGVLGR_589.8_345.2
CO8G_HUMAN
0.702

LYYGDDEK_501.7_563.2
CO8A_HUMAN
0.702

FQLSETNR_497.8_476.3
PSG2_HUMAN
0.701

TGVAVNKPAEFTVDAK_549.6_977.5
FLNA_HUMAN
0.700

IPGIFELGISSQSDR_809.9_679.3
CO8B_HUMAN
0.700

TLFIFGVTK_513.3_215.1
PSG4_HUMAN
0.699

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
0.699

QVFAVQR_424.2_473.3
ELNE_HUMAN
0.699

AQPVQVAEGSEPDGFWEALGGK_
GELS_HUMAN
0.699

758.0_623.4

DFNQFSSGEK_386.8_189.1
FETA_HUMAN
0.699

SVSLPSLDPASAK_636.4_473.3
APOB_HUMAN
0.699

GNGLTWAEK_488.3_634.3
C163B_HUMAN
0.698

LYYGDDEK_501.7_726.3
CO8A_HUMAN
0.698

NFPSPVDAAFR_610.8_959.5
HEMO_HUMAN
0.698

FAFNLYR_465.8_565.3
HEP2_HUMAN
0.697

SGFSFGFK_438.7_585.3
CO8B_HUMAN
0.696

DFHINLFQVLPWLK_885.5_543.3
CFAB_HUMAN
0.696

LQGTLPVEAR_542.3_842.5
CO5_HUMAN
0.694

GAVHVVVAETDYQSFAVLYLER_
CO8G_HUMAN
0.694

822.8_863.5

TSESTGSLPSPFLR_739.9_716.4
PSMG1_HUMAN
0.694

YISPDQLADLYK_713.4_277.2
ENOA_HUMAN
0.694

ESDTSYVSLK_564.8_347.2
CRP_HUMAN
0.693

ILDDLSPR_464.8_587.3
ITIH4_HUMAN
0.693

VQEAHLTEDQIFYFPK_655.7_
CO8G_HUMAN
0.692

391.2

SGVDLADSNQK_567.3_662.3
VGFR3_HUMAN
0.692

DTDTGALLFIGK_625.8_217.1
PEDF_HUMAN
0.692

HFQNLGK_422.2_285.1
AFAM_HUMAN
0.691

NNQLVAGYLQGPNVNLEEK_700.7_
IL1RA_HUMAN
0.691

999.5

IPGIFELGISSQSDR_809.9_849.4
CO8B_HUMAN
0.691

ESDTSYVSLK_564.8_696.4
CRP_HUMAN
0.690

GAVHVVVAETDYQSFAVLYLER_
CO8G_HUMAN
0.690

822.8_580.3

DADPDTFFAK_563.8_302.1
AFAM_HUMAN
0.690

LDFHFSSDR_375.2_464.2
INHBC_HUMAN
0.689

TLFIFGVTK_513.3_811.5
PSG4_HUMAN
0.688

DFNQFSSGEK_386.8_333.2
FETA_HUMAN
0.687

IQTHSTTYR_369.5_627.3
F13B_HUMAN
0.686

HYFIAAVER_553.3_658.4
FA8_HUMAN
0.686

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
0.686

DLHLSDVFLK_396.2_366.2
CO6_HUMAN
0.685

DPTFIPAPIQAK_433.2_556.3
ANGT_HUMAN
0.684

AGITIPR_364.2_272.2
IL17_HUMAN
0.684

IAQYYYTFK_598.8_884.4
F13B_HUMAN
0.684

SGVDLADSNQK_567.3_591.3
VGFR3_HUMAN
0.683

VEPLYELVTATDFAYSSTVR_754.4_
CO8B_HUMAN
0.682

549.3

AGITIPR_364.2_486.3
IL17_HUMAN
0.682

YEVQGEVFTKPQLWP_911.0_293.1
CRP_HUMAN
0.681

APLTKPLK_289.9_357.2
CRP_HUMAN
0.681

YNSQLLSFVR_613.8_508.3
TFR1_HUMAN
0.681

ANDQYLTAAALHNLDEAVK_686.4_
IL1A_HUMAN
0.681

301.1

IQTHSTTYR_369.5_540.3
F13B_HUMAN
0.681

IHPSYTNYR_575.8_598.3
PSG2_HUMAN
0.681

TEFLSNYLTNVDDITLVPGTLGR_
ENPP2_HUMAN
0.681

846.8_699.4

DPTFIPAPIQAK_433.2_461.2
ANGT_HUMAN
0.679

FQSVFTVTR_542.8_623.4
C1QC_HUMAN
0.679

LQVNTPLVGASLLR_741.0_925.6
BPIA1_HUMAN
0.679

DEIPHNDIALLK_459.9_510.8
HABP2_HUMAN
0.678

HATLSLSIPR_365.6_272.2
VGFR3_HUMAN
0.678

EDTPNSVWEPAK_686.8_315.2
C1S_HUMAN
0.678

TGISPLALIK_506.8_741.5
APOB_HUMAN
0.678

ILPSVPK_377.2_244.2
PGH1_HUMAN
0.676

HATLSLSIPR_365.6_472.3
VGFR3_HUMAN
0.676

QGHNSVFLIK_381.6_520.4
HEMO_HUMAN
0.676

LPATEKPVLLSK_432.6_460.3
HYOU1_HUMAN
0.675

APLTKPLK_289.9_398.8
CRP_HUMAN
0.674

GVTGYFTFNLYLK_508.3_683.9
PSG5_HUMAN
0.673

TFLTVYWTPER_706.9_401.2
ICAM1_HUMAN
0.673

GDTYPAELYITGSILR_885.0_274.1
F13B_HUMAN
0.672

EDTPNSVWEPAK_686.8_630.3
C1S_HUMAN
0.672

SLDFTELDVAAEK_719.4_316.2
ANGT_HUMAN
0.672

VELAPLPSWQPVGK_760.9_342.2
ICAM1_HUMAN
0.671

GPGEDFR_389.2_322.2
PTGDS_HUMAN
0.670

TDAPDLPEENQAR_728.3_843.4
CO5_HUMAN
0.670

GVTGYFTFNLYLK_508.3_260.2
PSG5_HUMAN
0.669

FAFNLYR_465.8_712.4
HEP2_HUMAN
0.669

ITENDIQIALDDAK_779.9_873.5
APOB_HUMAN
0.669

ILNIFGVIK_508.8_790.5
TFR1_HUMAN
0.669

ISQGEADINIAFYQR_575.6_684.4
MMP8_HUMAN
0.668

GDTYPAELYITGSILR_885.0_
F13B_HUMAN
0.668

1332.8

ELLESYIDGR_597.8_710.4
THRB_HUMAN
0.668

FTITAGSK_412.7_576.3
FABPL_HUMAN
0.667

ILDGGNK_358.7_490.2
CXCL5_HUMAN
0.667

GWVTDGFSSLK_598.8_854.4
APOC3_HUMAN
0.667

FSLVSGWGQLLDR_493.3_403.2
FA7_HUMAN
0.665

IHPSYTNYR_575.8_813.4
PSG2_HUMAN
0.665

ELLESYIDGR_597.8_839.4
THRB_HUMAN
0.665

SDGAKPGPR_442.7_213.6
COLI_HUMAN
0.664

IAQYYYTFK_598.8_395.2
F13B_HUMAN
0.664

SILFLGK_389.2_201.1
THBG_HUMAN
0.664

IEVNESGTVASSSTAVIVSAR_693.0_
PAI1_HUMAN
0.664

545.3

VSAPSGTGHLPGLNPL_506.3_300.7
PSG3_HUMAN
0.664

LLAPSDSPEWLSFDVTGVVR_730.1_
TGFB1_HUMAN
0.664

430.3

YYGYTGAFR_549.3_771.4
TRFL_HUMAN
0.663

TDAPDLPEENQAR_728.3_613.3
CO5_HUMAN
0.663

IEVIITLK_464.8_815.5
CXL11_HUMAN
0.662

ILPSVPK_377.2_227.2
PGH1_HUMAN
0.662

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
0.661

DYWSTVK_449.7_347.2
APOC3_HUMAN
0.661

IEGNLIFDPNNYLPK_874.0_845.5
APOB_HUMAN
0.661

WILTAAHTLYPK_471.9_407.2
C1R_HUMAN
0.661

WNFAYWAAHQPWSR_607.3_545.3
PRG2_HUMAN
0.661

SILFLGK_389.2_577.4
THBG_HUMAN
0.661

FSLVSGWGQLLDR_493.3_516.3
FA7_HUMAN
0.661

DTDTGALLFIGK_625.8_818.5
PEDF_HUMAN
0.661

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
0.660

LWAYLTIQELLAK_781.5_371.2
ITIH1_HUMAN
0.660

LLEVPEGR_456.8_356.2
C1S_HUMAN
0.659

ITENDIQIALDDAK_779.9_632.3
APOB_HUMAN
0.659

LTTVDIVTLR_565.8_716.4
IL2RB_HUMAN
0.658

IEVIITLK_464.8_587.4
CXL11_HUMAN
0.658

QLGLPGPPDVPDHAAYHPF_676.7_
ITIH4_HUMAN
0.658

299.2

TLAFVR_353.7_492.3
FA7_HUMAN
0.656

NSDQEIDFK_548.3_294.2
S10A5_HUMAN
0.656

YHFEALADTGISSEFYDNANDLLSK_
CO8A_HUMAN
0.656

940.8_874.5

SEPRPGVLLR_375.2_454.3
FA7_HUMAN
0.655

FLPCENK_454.2_390.2
IL10_HUMAN
0.654

NCSFSIIYPVVIK_770.4_831.5
CRHBP_HUMAN
0.654

SLDFTELDVAAEK_719.4_874.5
ANGT_HUMAN
0.654

ILLLGTAVESAWGDEQSAFR_721.7_
CXA1_HUMAN
0.653

909.4

SVSLPSLDPASAK_636.4_885.5
APOB_HUMAN
0.653

TGISPLALIK_506.8_654.5
APOB_HUMAN
0.653

YNQLLR_403.7_288.2
ENOA_HUMAN
0.653

YEVQGEVFTKPQLWP_911.0_392.2
CRP_HUMAN
0.652

VPGLYYFTYHASSR_554.3_720.3
C1QB_HUMAN
0.650

SLQNASAIESILK_687.4_589.4
IL3_HUMAN
0.650

WILTAAHTLYPK_471.9_621.4
C1R_HUMAN
0.650

GWVTDGFSSLK_598.8_953.5
APOC3_HUMAN
0.650

YGIEEHGK_311.5_599.3
CXA1_HUMAN
0.649

QDLGWK_373.7_503.3
TGFB3_HUMAN
0.649

DYWSTVK_449.7_620.3
APOC3_HUMAN
0.648

ALVLELAK_428.8_331.2
INHBE_HUMAN
0.647

QLGLPGPPDVPDHAAYHPF_676.7_
ITIH4_HUMAN
0.646

263.1

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
0.645

TFLTVYWTPER_706.9_502.3
ICAM1_HUMAN
0.644

FQSVFTVTR_542.8_722.4
C1QC_HUMAN
0.643

DPNGLPPEAQK_583.3_669.4
RET4_HUMAN
0.642

ETLLQDFR_511.3_322.2
AMBP_HUMAN
0.642

IIEVEEEQEDPYLNDR_996.0_777.4
FBLN1_HUMAN
0.641

ELCLDPK_437.7_359.2
IL8_HUMAN
0.641

TPSAAYLWVGTGASEAEK_919.5_
GELS_HUMAN
0.641

849.4

NQSPVLEPVGR_598.3_866.5
KS6A3_HUMAN
0.641

FNAVLTNPQGDYDTSTGK_964.5_
C1QC_HUMAN
0.641

333.2

LLEVPEGR_456.8_686.4
C1S_HUMAN
0.641

FFQYDTWK_567.8_840.4
IGF2_HUMAN
0.640

SPEAEDPLGVER_649.8_670.4
Z512B_HUMAN
0.639

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
0.639

SGAQATWTELPWPHEK_613.3_793.4
HEMO_HUMAN
0.638

YSHYNER_323.5_581.3
HABP2_HUMAN
0.638

YHFEALADTGISSEFYDNANDLLSK_
CO8A_HUMAN
0.637

940.8_301.1

DLHLSDVFLK_396.2_260.2
CO6_HUMAN
0.637

YSHYNER_323.5_418.2
HABP2_HUMAN
0.637

YYLQGAK_421.7_327.1
ITIH4_HUMAN
0.636

EVPLSALTNILSAQLISHWK_740.8_
PAI1_HUMAN
0.636

996.6

VPGLYYFTYHASSR_554.3_420.2
C1QB_HUMAN
0.636

AALAAFNAQNNGSNFQLEEISR_789.1_
FETUA_HUMAN
0.636

746.4

ETLLQDFR_511.3_565.3
AMBP_HUMAN
0.635

IVLSLDVPIGLLQILLEQAR_735.1_
UCN2_HUMAN
0.635

503.3

ENPAVIDFELAPIVDLVR_670.7_
CO6_HUMAN
0.635

811.5

LQLSETNR_480.8_355.2
PSG8_HUMAN
0.635

DPDQTDGLGLSYLSSHIANVER_796.4_
GELS_HUMAN
0.635

456.2

NVNQSLLELHK_432.2_656.3
FRIH_HUMAN
0.634

EIGELYLPK_531.3_633.4
AACT_HUMAN
0.634

SPEQQETVLDGNLIIR_906.5_699.3
ITIH4_HUMAN
0.634

NKPGVYTDVAYYLAWIR_677.0_
FA12_HUMAN
0.632

545.3

QNYHQDSEAAINR_515.9_544.3
FRIH_HUMAN
0.632

EKPAGGIPVLGSLVNTVLK_631.4_
BPIB1_HUMAN
0.632

930.6

VTFEYR_407.7_614.3
CRHBP_HUMAN
0.630

DLPHITVDR_533.3_490.3
MMP7_HUMAN
0.630

VEHSDLSFSK_383.5_234.1
B2MG_HUMAN
0.630

ENPAVIDFELAPIVDLVR_670.7_
CO6_HUMAN
0.630

601.4

YGFYTHVFR_397.2_659.4
THRB_HUMAN
0.629

ILDDLSPR_464.8_702.3
ITIH4_HUMAN
0.629

DPNGLPPEAQK_583.3_497.2
RET4_HUMAN
0.629

GSLVQASEANLQAAQDFVR_668.7_
ITIH1_HUMAN
0.629

806.4

FLYHK_354.2_447.2
AMBP_HUMAN
0.627

FNAVLTNPQGDYDTSTGK_964.5_
C1QC_HUMAN
0.627

262.1

LQDAGVYR_461.2_680.3
PD1L1_HUMAN
0.627

INPASLDK_429.2_630.4
C163A_HUMAN
0.626

LEEHYELR_363.5_580.3
PAI2_HUMAN
0.625

VEHSDLSFSK_383.5_468.2
B2MG_HUMAN
0.624

TSDQIHFFFAK_447.6_659.4
ANT3_HUMAN
0.624

ATLSAAPSNPR_542.8_570.3
CXCL2_HUMAN
0.624

YGFYTHVFR_397.2_421.3
THRB_HUMAN
0.624

EANQSTLENFLER_775.9_678.4
IL4_HUMAN
0.623

GQQPADVTGTALPR_705.9_314.2
CSF1_HUMAN
0.623

VELAPLPSWQPVGK_760.9_400.3
ICAM1_HUMAN
0.622

GEVTYTTSQVSK_650.3_750.4
EGLN_HUMAN
0.622

SLQAFVAVAAR_566.8_487.3
IL23A_HUMAN
0.622

HYGGLTGLNK_530.3_301.1
PGAM1_HUMAN
0.622

GPEDQDISISFAWDK_854.4_753.4
DEF4_HUMAN
0.622

YVVISQGLDKPR_458.9_400.3
LRP1_HUMAN
0.621

LWAYLTIQELLAK_781.5_300.2
ITIH1_HUMAN
0.621

SGAQATWTELPWPHEK_613.3_510.3
HEMO_HUMAN
0.621

GTAEWLSFDVTDTVR_848.9_952.5
TGFB3_HUMAN
0.621

FFQYDTWK_567.8_712.3
IGF2_HUMAN
0.621

AHQLAIDTYQEFEETYIPK_766.0_
CSH_HUMAN
0.620

634.4

LPATEKPVLLSK_432.6_347.2
HYOU1_HUMAN
0.620

NIQSVNVK_451.3_546.3
GROA_HUMAN
0.620

TAVTANLDIR_537.3_288.2
CHL1_HUMAN
0.619

WSAGLTSSQVDLYIPK_883.0_515.3
CBG_HUMAN
0.616

QINSYVK_426.2_496.3
CBG_HUMAN
0.616

GFQALGDAADIR_617.3_288.2
TIMP1_HUMAN
0.615

WNFAYWAAHQPWSR_607.3_673.3
PRG2_HUMAN
0.615

NEIWYR_440.7_357.2
FA12_HUMAN
0.615

VLEPTLK_400.3_587.3
VTDB_HUMAN
0.614

YYLQGAK_421.7_516.3
ITIH4_HUMAN
0.614

ALNSIIDVYHK_424.9_774.4
S10A8_HUMAN
0.614

ETPEGAEAKPWYEPIYLGGVFQLEK_
TNFA_HUMAN
0.614

951.1_877.5

LNIGYIEDLK_589.3_837.4
PAI2_HUMAN
0.614

NVNQSLLELHK_432.2_543.3
FRIH_HUMAN
0.613

ILLLGTAVESAWGDEQSAFR_721.7_
CXA1_HUMAN
0.613

910.6

AALAAFNAQNNGSNFQLEEISR_789.1_
FETUA_HUMAN
0.613

633.3

VLEPTLK_400.3_458.3
VTDB_HUMAN
0.613

VGEYSLYIGR_578.8_708.4
SAMP_HUMAN
0.613

DIPHWLNPTR_416.9_373.2
PAPP1_HUMAN
0.612

NEIVFPAGILQAPFYTR_968.5_
ECE1_HUMAN
0.612

357.2

AEHPTWGDEQLFQTTR_639.3_765.4
PGH1_HUMAN
0.612

VEPLYELVTATDFAYSSTVR_754.4_
CO8B_HUMAN
0.611

712.4

DEIPHNDIALLK_459.9_260.2
HABP2_HUMAN
0.611

QINSYVK_426.2_610.3
CBG_HUMAN
0.610

SWNEPLYHLVTEVR_581.6_614.3
PRL_HUMAN
0.610

YGIEEHGK_311.5_341.2
CXA1_HUMAN
0.610

FGFGGSTDSGPIR_649.3_946.5
ADA12_HUMAN
0.610

ANDQYLTAAALHNLDEAVK_686.4_
IL1A_HUMAN
0.610

317.2

VRPQQLVK_484.3_609.4
ITIH4_HUMAN
0.609

IPKPEASFSPR_410.2_506.3
ITIH4_HUMAN
0.609

SPEQQETVLDGNLIIR_906.5_685.4
ITIH4_HUMAN
0.609

DDLYVSDAFHK_655.3_704.3
ANT3_HUMAN
0.609

ELPEHTVK_476.8_347.2
VTDB_HUMAN
0.609

FLYHK_354.2_284.2
AMBP_HUMAN
0.608

QRPPDLDTSSNAVDLLFFTDESGDSR_
C1R_HUMAN
0.608

961.5_262.2

DPDQTDGLGLSYLSSHIANVER_796.4_
GELS_HUMAN
0.608

328.1

NEIWYR_440.7_637.4
FA12_HUMAN
0.607

LQLSETNR_480.8_672.4
PSG8_HUMAN
0.606

GQVPENEANVVITTLK_571.3_462.3
CADH1_HUMAN
0.606

FTGSQPFGQGVEHATANK_626.0_
TSP1_HUMAN
0.605

521.2

LEPLYSASGPGLRPLVIK_637.4_
CAA60698
0.605

260.2

QRPPDLDTSSNAVDLLFFTDESGDSR_
C1R_HUMAN
0.604

961.5_866.3

LTTVDIVTLR_565.8_815.5
IL2RB_HUMAN
0.604

TSDQIHFFFAK_447.6_512.3
ANT3_HUMAN
0.604

IQHPFTVEEFVLPK_562.0_861.5
PZP_HUMAN
0.603

NKPGVYTDVAYYLAWIR_677.0_
FA12_HUMAN
0.603

821.5

TEQAAVAR_423.2_615.4
FA12_HUMAN
0.603

EIGELYLPK_531.3_819.5
AACT_HUMAN
0.602

LFYADHPFIFLVR_546.6_647.4
SERPH_HUMAN
0.602

AEHPTWGDEQLFQTTR_639.3_569.3
PGH1_HUMAN
0.601

TSYQVYSK_488.2_787.4
C163A_HUMAN
0.601

YTTEIIK_434.2_704.4
C1R_HUMAN
0.601

NVIQISNDLENLR_509.9_402.3
LEP_HUMAN
0.600

AFLEVNEEGSEAAASTAVVIAGR_
ANT3_HUMAN
0.600

764.4_685.4

TABLE 13

Middle Window Individual Stats

Transition
Protein
AUC

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
0.738

VFQFLEK_455.8_811.4
CO5_HUMAN
0.709

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
0.705

SLDFTELDVAAEK_719.4_316.2
ANGT_HUMAN
0.692

VEHSDLSFSK_383.5_234.1
B2MG_HUMAN
0.686

LLAPSDSPEWLSFDVTGVVR_730.1_
TGFB1_HUMAN
0.683

430.3

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.683

VLEPTLK_400.3_458.3
VTDB_HUMAN
0.681

LHEAFSPVSYQHDLALLR_699.4_
FA12_HUMAN
0.681

251.2

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
0.679

YGIEEHGK_311.5_599.3
CXA1_HUMAN
0.677

ALQDQLVLVAAK_634.9_289.2
ANGT_HUMAN
0.675

VLEPTLK_400.3_587.3
VTDB_HUMAN
0.667

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
0.665

IEEIAAK_387.2_660.4
CO5_HUMAN
0.664

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
0.664

TLLPVSKPEIR_418.3_514.3
CO5_HUMAN
0.662

ALQDQLVLVAAK_634.9_956.6
ANGT_HUMAN
0.661

TLAFVR_353.7_492.3
FA7_HUMAN
0.661

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
0.658

VEHSDLSFSK_383.5_468.2
B2MG_HUMAN
0.653

DPTFIPAPIQAK_433.2_461.2
ANGT_HUMAN
0.653

QGHNSVFLIK_381.6_260.2
HEMO_HUMAN
0.650

SLDFTELDVAAEK_719.4_874.5
ANGT_HUMAN
0.650

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
0.649

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.647

SLQAFVAVAAR_566.8_804.5
IL23A_HUMAN
0.646

AQPVQVAEGSEPDGFWEALGGK_
GELS_HUMAN
0.644

758.0_574.3

QGHNSVFLIK_381.6_520.4
HEMO_HUMAN
0.644

VNHVTLSQPK_374.9_459.3
B2MG_HUMAN
0.643

DLHLSDVFLK_396.2_260.2
CO6_HUMAN
0.643

TEQAAVAR_423.2_615.4
FA12_HUMAN
0.643

GPITSAAELNDPQSILLR_632.4_
EGLN_HUMAN
0.643

826.5

HFQNLGK_422.2_527.2
AFAM_HUMAN
0.642

TEQAAVAR_423.2_487.3
FA12_HUMAN
0.642

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
0.642

TLFIFGVTK_513.3_811.5
PSG4_HUMAN
0.642

DLHLSDVFLK_396.2_366.2
CO6_HUMAN
0.641

AFTECCVVASQLR_770.9_574.3
CO5_HUMAN
0.640

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
0.639

DPTFIPAPIQAK_433.2_556.3
ANGT_HUMAN
0.639

FSLVSGWGQLLDR_493.3_403.2
FA7_HUMAN
0.638

HYINLITR_515.3_301.1
NPY_HUMAN
0.637

HFQNLGK_422.2_285.1
AFAM_HUMAN
0.637

VPLALFALNR_557.3_620.4
PEPD_HUMAN
0.636

IHPSYTNYR_575.8_813.4
PSG2_HUMAN
0.635

IEEIAAK_387.2_531.3
CO5_HUMAN
0.635

GEVTYTTSQVSK_650.3_750.4
EGLN_HUMAN
0.634

DFNQFSSGEK_386.8_333.2
FETA_HUMAN
0.634

VVGGLVALR_442.3_784.5
FA12_HUMAN
0.634

SDGAKPGPR_442.7_459.2
COLI_HUMAN
0.634

DVLLLVHNLPQNLTGHIWYK_791.8_
PSG7_HUMAN
0.634

310.2

TLLPVSKPEIR_418.3_288.2
CO5_HUMAN
0.633

NKPGVYTDVAYYLAWIR_677.0_
FA12_HUMAN
0.630

821.5

QVFAVQR_424.2_473.3
ELNE_HUMAN
0.630

NHYTESISVAK_624.8_415.2
NEUR1_HUMAN
0.630

IAPQLSTEELVSLGEK_857.5_333.2
AFAM_HUMAN
0.629

IHPSYTNYR_575.8_598.3
PSG2_HUMAN
0.627

EVFSKPISWEELLQ_852.9_260.2
FA40A_HUMAN
0.627

SILFLGK_389.2_201.1
THBG_HUMAN
0.626

IEVIITLK_464.8_587.4
CXL11_HUMAN
0.625

VVGGLVALR_442.3_685.4
FA12_HUMAN
0.624

VVLSSGSGPGLDLPLVLGLPLQLK_
SHBG_HUMAN
0.624

791.5_598.4

FGFGGSTDSGPIR_649.3_946.5
ADA12_HUMAN
0.623

VVLSSGSGPGLDLPLVLGLPLQLK_
SHBG_HUMAN
0.622

791.5_768.5

YGIEEHGK_311.5_341.2
CXA1_HUMAN
0.621

LHEAFSPVSYQHDLALLR_699.4_
FA12_HUMAN
0.621

380.2

AHYDLR_387.7_566.3
FETUA_HUMAN
0.620

FSVVYAK_407.2_381.2
FETUA_HUMAN
0.618

ALALPPLGLAPLLNLWAKPQGR_
SHBG_HUMAN
0.618

770.5_256.2

YENYTSSFFIR_713.8_293.1
IL12B_HUMAN
0.617

VELAPLPSWQPVGK_760.9_342.2
ICAM1_HUMAN
0.617

SILFLGK_389.2_577.4
THBG_HUMAN
0.616

ILPSVPK_377.2_227.2
PGH1_HUMAN
0.615

IPSNPSHR_303.2_496.3
FBLN3_HUMAN
0.615

HYFIAAVER_553.3_301.1
FA8_HUMAN
0.615

FSVVYAK_407.2_579.4
FETUA_HUMAN
0.613

VFQFLEK_455.8_276.2
CO5_HUMAN
0.613

IAPQLSTEELVSLGEK_857.5_533.3
AFAM_HUMAN
0.613

ILPSVPK_377.2_244.2
PGH1_HUMAN
0.613

NKPGVYTDVAYYLAWIR_677.0_
FA12_HUMAN
0.613

545.3

WSAGLTSSQVDLYIPK_883.0_515.3
CBG_HUMAN
0.612

TPSAAYLWVGTGASEAEK_919.5_
GELS_HUMAN
0.612

849.4

ALALPPLGLAPLLNLWAKPQGR_
SHBG_HUMAN
0.612

770.5_457.3

QLGLPGPPDVPDHAAYHPF_676.7_
ITIH4_HUMAN
0.612

299.2

ILDDLSPR_464.8_587.3
ITIH4_HUMAN
0.611

VELAPLPSWQPVGK_760.9_400.3
ICAM1_HUMAN
0.611

DADPDTFFAK_563.8_825.4
AFAM_HUMAN
0.611

NHYTESISVAK_624.8_252.1
NEUR1_HUMAN
0.611

SEPRPGVLLR_375.2_454.3
FA7_HUMAN
0.611

LNIGYIEDLK_589.3_950.5
PAI2_HUMAN
0.611

ANLINNIFELAGLGK_793.9_299.2
LCAP_HUMAN
0.609

LTTVDIVTLR_565.8_716.4
IL2RB_HUMAN
0.608

TQILEWAAER_608.8_761.4
EGLN_HUMAN
0.608

NEPEETPSIEK_636.8_573.3
SOX5_HUMAN
0.608

AQPVQVAEGSEPDGFWEALGGK_
GELS_HUMAN
0.607

758.0_623.4

LQVNTPLVGASLLR_741.0_925.6
BPIA1_HUMAN
0.607

VPSHAVVAR_312.5_345.2
TRFL_HUMAN
0.607

SLCINASAIESILK_687.4_860.5
IL3_HUMAN
0.607

GVTGYFTFNLYLK_508.3_260.2
PSG5_HUMAN
0.605

DFNQFSSGEK_386.8_189.1
FETA_HUMAN
0.605

QLGLPGPPDVPDHAAYHPF_676.7_
ITIH4_HUMAN
0.605

263.1

TLEAQLTPR_514.8_814.4
HEP2_HUMAN
0.604

AFTECCVVASQLR_770.9_673.4
CO5_HUMAN
0.604

LTTVDIVTLR_565.8_815.5
IL2RB_HUMAN
0.604

TLNAYDHR_330.5_312.2
PAR3_HUMAN
0.603

LWAYLTIQELLAK_781.5_300.2
ITIH1_HUMAN
0.603

GGLFADIASHPWQAAIFAK_667.4_
TPA_HUMAN
0.603

375.2

IPSNPSHR_303.2_610.3
FBLN3_HUMAN
0.603

TDAPDLPEENQAR_728.3_843.4
CO5_HUMAN
0.603

SPQAFYR_434.7_684.4
REL3_HUMAN
0.602

SSNNPHSPIVEEFQVPYNK_729.4_
C1S_HUMAN
0.601

261.2

AHYDLR_387.7_288.2
FETUA_HUMAN
0.600

DGSPDVTTADIGANTPDATK_973.5_
PGRP2_HUMAN
0.600

844.4

SPQAFYR_434.7_556.3
REL3_HUMAN
0.600

TABLE 14

Middle Late Individual Stats

Transition
Protein
AUC

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
0.656

VPLALFALNR_557.3_620.4
PEPD_HUMAN
0.655

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.652

AVYEAVLR_460.8_587.4
PEPD_HUMAN
0.649

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
0.644

VFQFLEK_455.8_811.4
CO5_HUMAN
0.643

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
0.640

TLAFVR_353.7_492.3
FA7_HUMAN
0.639

TEQAAVAR_423.2_615.4
FA12_HUMAN
0.637

YGIEEHGK_311.5_599.3
CXA1_HUMAN
0.637

TEQAAVAR_423.2_487.3
FA12_HUMAN
0.633

QINSYVK_426.2_496.3
CBG_HUMAN
0.633

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
0.633

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
0.633

ALQDQLVLVAAK_634.9_956.6
ANGT_HUMAN
0.628

VLEPTLK_400.3_587.3
VTDB_HUMAN
0.628

DFNQFSSGEK_386.8_333.2
FETA_HUMAN
0.628

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.628

LIEIANHVDK_384.6_498.3
ADA12_HUMAN
0.626

QINSYVK_426.2_610.3
CBG_HUMAN
0.625

SLDFTELDVAAEK_719.4_316.2
ANGT_HUMAN
0.625

DPTFIPAPIQAK_433.2_461.2
ANGT_HUMAN
0.625

AVYEAVLR_460.8_750.4
PEPD_HUMAN
0.623

YENYTSSFFIR_713.8_756.4
IL12B_HUMAN
0.623

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
0.623

WSAGLTSSQVDLYIPK_883.0_515.3
CBG_HUMAN
0.622

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
0.622

ALQDQLVLVAAK_634.9_289.2
ANGT_HUMAN
0.621

SLQAFVAVAAR_566.8_804.5
IL23A_HUMAN
0.621

DPTFIPAPIQAK_433.2_556.3
ANGT_HUMAN
0.620

FGFGGSTDSGPIR_649.3_946.5
ADA12_HUMAN
0.619

VLEPTLK_400.3_458.3
VTDB_HUMAN
0.619

SLDFTELDVAAEK_719.4_874.5
ANGT_HUMAN
0.618

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
0.618

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
0.618

TPSAAYLWVGTGASEAEK_919.5_849.4
GELS_HUMAN
0.615

LHEAFSPVSYQHDLALLR_699.4_251.2
FA12_HUMAN
0.615

TLEAQLTPR_514.8_685.4
HEP2_HUMAN
0.613

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
0.612

GYQELLEK_490.3_631.4
FETA_HUMAN
0.612

VPLALFALNR_557.3_917.6
PEPD_HUMAN
0.611

DLHLSDVFLK_396.2_260.2
CO6_HUMAN
0.611

LTTVDIVTLR_565.8_815.5
IL2RB_HUMAN
0.608

WSAGLTSSQVDLYIPK_883.0_357.2
CBG_HUMAN
0.608

ITQDAQLK_458.8_702.4
CBG_HUMAN
0.608

NIQSVNVK_451.3_674.4
GROA_HUMAN
0.607

ALEQDLPVNIK_620.4_570.4
CNDP1_HUMAN
0.607

TLNAYDHR_330.5_312.2
PAR3_HUMAN
0.606

LWAYLTIQELLAK_781.5_300.2
ITIH1_HUMAN
0.606

VVGGLVALR_442.3_784.5
FA12_HUMAN
0.605

AQPVQVAEGSEPDGFWEALGGK_758.0_623.4
GELS_HUMAN
0.603

SVVLIPLGAVDDGEHSCINEK_703.0_798.4
CNDP1_HUMAN
0.603

SETEIHQGFQHLHQLFAK_717.4_318.1
CBG_HUMAN
0.603

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
0.603

IEVIITLK_464.8_587.4
CXL11_HUMAN
0.602

ITQDAQLK_458.8_803.4
CBG_HUMAN
0.602

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
0.601

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
0.601

LTTVDIVTLR_565.8_716.4
IL2RB_HUMAN
0.600

WWGGQPLWITATK_772.4_929.5
ENPP2_HUMAN
0.600

TABLE 15

Late Window Individual Stats

Transition
Protein
AUC

AVYEAVLR_460.8_587.4
PEPD_HUMAN
0.724

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
0.703

QINSYVK_426.2_496.3
CBG_HUMAN
0.695

AVYEAVLR_460.8_750.4
PEPD_HUMAN
0.693

AALAAFNAQNNGSNFQLEEISR_
FETUA_HUMAN
0.684

789.1_746.4

QINSYVK_426.2_610.3
CBG_HUMAN
0.681

VPLALFALNR_557.3_620.4
PEPD_HUMAN
0.678

VGVISFAQK_474.8_580.3
TFR2_HUMAN
0.674

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
0.670

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
0.670

LIEIANHVDK_384.6_498.3
ADA12_HUMAN
0.660

SGVDLADSNQK_567.3_662.3
VGFR3_HUMAN
0.660

TSYQVYSK_488.2_787.4
C163A_HUMAN
0.657

ITQDAQLK_458.8_702.4
CBG_HUMAN
0.652

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
0.650

ALEQDLPVNIK_620.4_798.5
CNDP1_HUMAN
0.650

VFQYIDLHQDEFVQTLK_708.4_375.2
CNDP1_HUMAN
0.650

SGVDLADSNQK_567.3_591.3
VGFR3_HUMAN
0.648

YENYTSSFFIR_713.8_756.4
IL12B_HUMAN
0.647

VLSSIEQK_452.3_691.4
1433S_HUMAN
0.647

YSHYNER_323.5_418.2
HABP2_HUMAN
0.646

ILDGGNK_358.7_603.3
CXCL5_HUMAN
0.645

GTYLYNDCPGPGQDTDCR_697.0_666.3
TNR1A_HUMAN
0.645

AEIEYLEK_497.8_389.2
LYAM1_HUMAN
0.645

TLPFSR_360.7_506.3
LYAM1_HUMAN
0.645

DEIPHNDIALLK_459.9_510.8
HABP2_HUMAN
0.644

ALEQDLPVNIK_620.4_570.4
CNDP1_HUMAN
0.644

SPEAEDPLGVER_649.8_314.1
Z512B_HUMAN
0.644

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
0.642

TASDFITK_441.7_781.4
GELS_HUMAN
0.641

SETEIHQGFQHLHQLFAK_717.4_447.2
CBG_HUMAN
0.640

SPQAFYR_434.7_556.3
REL3_HUMAN
0.639

TAVTANLDIR_537.3_288.2
CHL1_HUMAN
0.636

VPLALFALNR_557.3_917.6
PEPD_HUMAN
0.636

YISPDQLADLYK_713.4_277.2
ENOA_HUMAN
0.633

SETEIHQGFQHLHQLFAK_717.4_318.1
CBG_HUMAN
0.633

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
0.633

GYQELLEK_490.3_631.4
FETA_HUMAN
0.633

AYSDLSR_406.2_375.2
SAMP_HUMAN
0.633

SVVLIPLGAVDDGEHSCINEK_
CNDP1_HUMAN
0.632

703.0_798.4

TLEAQLTPR_514.8_685.4
HEP2_HUMAN
0.631

WSAGLTSSQVDLYIPK_883.0_515.3
CBG_HUMAN
0.631

TEQAAVAR_423.2_615.4
FA12_HUMAN
0.628

AQPVQVAEGSEPDGFWEALGGK_
GELS_HUMAN
0.626

758.0_574.3

AGITIPR_364.2_486.3
IL17_HUMAN
0.626

AEVIWTSSDHQVLSGK_586.3_300.2
PD1L1_HUMAN
0.625

TEQAAVAR_423.2_487.3
FA12_HUMAN
0.625

NHYTESISVAK_624.8_415.2
NEUR1_HUMAN
0.625

WSAGLTSSQVDLYIPK_883.0_357.2
CBG_HUMAN
0.623

YSHYNER_323.5_581.3
HABP2_HUMAN
0.623

DFNQFSSGEK_386.8_333.2
FETA_HUMAN
0.621

NIQSVNVK_451.3_674.4
GROA_HUMAN
0.620

SVVLIPLGAVDDGEHSCINEK_
CNDP1_HUMAN
0.620

703.0_286.2

TLAFVR_353.7_492.3
FA7_HUMAN
0.619

AVDIPGLEAATPYR_736.9_286.1
TENA_HUMAN
0.619

TEFLSNYLTNVDDITLVPGTLGR_
ENPP2_HUMAN
0.618

846.8_600.3

YWGVASFLQK_599.8_849.5
RET4_HUMAN
0.618

TPSAAYLWVGTGASEAEK_919.5_428.2
GELS_HUMAN
0.618

DPNGLPPEAQK_583.3_669.4
RET4_HUMAN
0.617

TYLHTYESEI_628.3_908.4
ENPP2_HUMAN
0.616

SPQAFYR_434.7_684.4
REL3_HUMAN
0.616

TPSAAYLWVGTGASEAEK_919.5_849.4
GELS_HUMAN
0.615

ALNHLPLEYNSALYSR_621.0_538.3
C06_HUMAN
0.615

IEVNESGTVASSSTAVIVSAR_
PAI1_HUMAN
0.615

693.0_545.3

LTTVDIVTLR_565.8_815.5
IL2RB_HUMAN
0.615

LWAYLTIQELLAK_781.5_371.2
ITIH1_HUMAN
0.613

SYTITGLQPGTDYK_772.4_352.2
FINC_HUMAN
0.612

GAVHVVVAETDYQSFAVLYLER_
CO8G_HUMAN
0.612

822.8_863.5

FQLPGQK_409.2_276.1
PSG1_HUMAN
0.612

ILDGGNK_358.7_490.2
CXCL5_HUMAN
0.611

DYWSTVK_449.7_620.3
APOC3_HUMAN
0.611

AGLLRPDYALLGHR_518.0_595.4
PGRP2_HUMAN
0.611

ALNFGGIGVVVGHELTHAFDDQGR_
ECE1_HUMAN
0.611

837.1_360.2

GYQELLEK_490.3_502.3
FETA_HUMAN
0.611

HATLSLSIPR_365.6_472.3
VGFR3_HUMAN
0.610

SVPVTKPVPVTKPITVTK_631.1_658.4
Z512B_HUMAN
0.610

FQLPGQK_409.2_429.2
PSG1_HUMAN
0.610

IYLQPGR_423.7_329.2
ITIH2_HUMAN
0.610

TLNAYDHR_330.5_312.2
PAR3_HUMAN
0.609

DPNGLPPEAQK_583.3_497.2
RET4_HUMAN
0.609

FGFGGSTDSGPIR_649.3_946.5
ADA12_HUMAN
0.609

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.608

GAVHVVVAETDYQSFAVLYLER_
CO8G_HUMAN
0.608

822.8_580.3

VPSHAVVAR_312.5_515.3
TRFL_HUMAN
0.608

YWGVASFLQK_599.8_350.2
RET4_HUMAN
0.608

EWVAIESDSVQPVPR_856.4_468.3
CNDP1_HUMAN
0.607

LQDAGVYR_461.2_680.3
PD1L1_HUMAN
0.607

DLYHYITSYVVDGEIIIYGPAYSGR_
PSG1_HUMAN
0.607

955.5_650.3

LWAYLTIQELLAK_781.5_300.2
ITIH1_HUMAN
0.606

ITENDIQIALDDAK_779.9_632.3
APOB_HUMAN
0.606

SYTITGLQPGTDYK_772.4_680.3
FINC_HUMAN
0.606

FFQYDTWK_567.8_712.3
IGF2_HUMAN
0.605

IYLQPGR_423.7_570.3
ITIH2_HUMAN
0.605

YNCILLR_403.7_529.4
ENOA_HUMAN
0.605

WWGGQPLWITATK_772.4_929.5
ENPP2_HUMAN
0.605

WWGGQPLWITATK_772.4_373.2
ENPP2_HUMAN
0.605

TASDFITK_441.7_710.4
GELS_HUMAN
0.605

EWVAIESDSVQPVPR_856.4_486.2
CNDP1_HUMAN
0.605

YEFLNGR_449.7_606.3
PLMN_HUMAN
0.604

SNPVTLNVLYGPDLPR_585.7_654.4
PSG6_HUMAN
0.604

ITQDAQLK_458.8_803.4
CBG_HUMAN
0.603

LTTVDIVTLR_565.8_716.4
IL2RB_HUMAN
0.602

FNAVLTNPQGDYDTSTGK_
C1QC_HUMAN
0.602

964.5_262.1

ITGFLKPGK_320.9_301.2
LBP_HUMAN
0.601

DYWSTVK_449.7_347.2
APOC3_HUMAN
0.601

DPTFIPAPIQAK_433.2_556.3
ANGT_HUMAN
0.601

GWVTDGFSSLK_598.8_953.5
APOC3_HUMAN
0.601

YYGYTGAFR_549.3_771.4
TRFL_HUMAN
0.601

ELPEHTVK_476.8_347.2
VTDB_HUMAN
0.601

FTFTLHLETPKPSISSSNLNPR_
PSG1_HUMAN
0.601

829.4_874.4

DLYHYITSYVVDGEIIIYGPAYSGR_
PSG1_HUMAN
0.601

955.5_707.3

SPQAFYR_434.7_684.4
REL3_HUMAN
0.616

TPSAAYLWVGTGASEAEK_
GELS_HUMAN
0.615

919.5_849.4

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.615

IEVNESGTVASSSTAVIVSAR_
PAI1_HUMAN
0.615

693.0_545.3

LTTVDIVTLR_565.8_815.5
IL2RB_HUMAN
0.615

LWAYLTIQELLAK_781.5_371.2
ITIH1_HUMAN
0.613

SYTITGLQPGTDYK_772.4_352.2
FINC_HUMAN
0.612

GAVHVVVAETDYQSFAVLYLER_
CO8G_HUMAN
0.612

822.8_863.5

FQLPGQK_409.2_276.1
PSG1_HUMAN
0.612

DLYHYITSYVVDGEIIIYGPAYSGR_
PSG1_HUMAN
0.601

955.5_707.3

TABLE 16

Lasso Early 32

Coef-

Variable
Protein
ficient

LIQDAVTGLTVNGQITGDK_972.0_798.4
ITIH3_HUMAN
9.53

VQTAHFK_277.5_431.2
CO8A_HUMAN
9.09

FLNWIK_410.7_560.3
HABP2_HUMAN
6.15

ITGFLKPGK_320.9_429.3
LBP_HUMAN
5.29

ELIEELVNITQNQK_557.6_517.3
IL13_HUMAN
3.83

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
3.41

DISEVVTPR_508.3_787.4
CFAB_HUMAN
0.44

AHYDLR_387.7_288.2
FETUA_HUMAN
0.1

TABLE 17

Lasso Early 100

Coef-

Variable
Protein
ficient

LIQDAVTGLTVNGQITGDK_
ITIH3_HUMAN
6.56

972.0_798.4

ALNHLPLEYNSALYSR_
CO6_HUMAN
6.51

621.0_538.3

VQTAHFK_277.5_431.2
CO8A_HUMAN
4.51

NIQSVNVK_451.3_674.4
GROA_HUMAN
3.12

TYLHTYESEI_628.3_908.4
ENPP2_HUMAN
2.68

LIENGYFHPVK_439.6_627.4
F13B_HUMAN
2.56

AVLHIGEK_289.5_292.2
THBG_HUMAN
2.11

FLNWIK_410.7_560.3
HABP2_HUMAN
1.85

ITGFLKPGK_320.9_429.3
LBP_HUMAN
1.36

DALSSVQESQVAQQAR_
APOC3_HUMAN
1.3

573.0_672.4

DALSSVQESQVAQQAR_
APOC3_HUMAN
0.83

573.0_502.3

FLPCENK_454.2_550.2
IL10_HUMAN
0.39

ELIEELVNITQNQK_557.6_517.3
IL13_HUMAN
0.3

TEFLSNYLTNVDDITLVPGTLGR_
ENPP2_HUMAN
0.29

846.8_600.3

VSEADSSNADWVTK_754.9_347.2
CFAB_HUMAN
0.27

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN
0.13

TGVAVNKPAEFTVDAK_
FLNA_HUMAN
0.04

549.6_258.1

TASDFITK_441.7_781.4
GELS_HUMAN
−5.91

LIQDAVTGLTVNGQITGDK_
ITIH3_HUMAN
6.56

972.0_798.4

TABLE 18

Lasso Protein Early Window

Coef-

Variable
Protein
ficient

ALNHLPLEYNSALYSR_
CO6_HUMAN
7.17

621.0_538.3

LIQDAVTGLTVNGQITGDK_
ITIH3_HUMAN
6.06

972.0_798.4

LIENGYFHPVK_439.6_627.4
F13B_HUMAN
3.23

WWGGQPLWITATK_772.4_929.5
ENPP2_HUMAN
2.8

QALEEFQK_496.8_680.3
CO8B_HUMAN
2.73

NIQSVNVK_451.3_674.4
GROA_HUMAN
2.53

DALSSVQESQVAQQAR_
APOC3_HUMAN
2.51

573.0_672.4

AVLHIGEK_289.5_348.7
THBG_HUMAN
2.33

FLNWIK_410.7_560.3
HABP2_HUMAN
1.05

FLPCENK_454.2_550.2
IL10_HUMAN
0.74

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN
0.7

DISEVVTPR_508.3_787.4
CFAB_HUMAN
0.45

EVFSKPISWEELLQ_852.9_260.2
FA40A_HUMAN
0.17

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
0.06

TASDFITK_441.7_781.4
GELS_HUMAN
−7.65

TABLE 19

Lasso All Early Window

Coef-

Variable
Protein
ficient

FLNWIK_410.7_560.3
HABP2_HUMAN
3.74

AHYDLR_387.7_288.2
FETUA_HUMAN
0.07

ALNHLPLEYNSALYSR_
CO6_HUMAN
6.07

621.0_538.3

LIQDAVTGLTVNGQITGDK_
ITIH3_HUMAN
8.85

972.0_798.4

TYLHTYESEI_628.3_908.4
ENPP2_HUMAN
2.97

VQTAHFK_277.5_431.2
CO8A_HUMAN
3.36

ELIEELVNITQNQK_557.6_618.3
IL13_HUMAN
11.24

VSEADSSNADWVTK_754.9_347.2
CFAB_HUMAN
0.63

AVLHIGEK_289.5_292.2
THBG_HUMAN
0.51

TGVAVNKPAEFTVDAK_
FLNA_HUMAN
0.17

549.6_977.5

LIENGYFHPVK_439.6_343.2
F13B_HUMAN
1.7

AQPVQVAEGSEPDGFWEALGGK_
GELS_HUMAN
−0.93

758.0_574.3

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
1.4

TASDFITK_441.7_781.4
GELS_HUMAN
−0.07

NIQSVNVK_451.3_674.4
GROA_HUMAN
2.12

DALSSVQESQVAQQAR_
APOC3_HUMAN
1.15

573.0_672.4

DALSSVQESQVAQQAR_
APOC3_HUMAN
0.09

573.0_502.3

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
2.45

ALDLSLK_380.2_575.3
ITIH3_HUMAN
2.51

TLFIFGVTK_513.3_811.5
PSG4_HUMAN
4.12

ISQGEADINIAFYQR_575.6_684.4
MMP8_HUMAN
1.29

SGVDLADSNQK_567.3_591.3
VGFR3_HUMAN
0.55

GPGEDFR_389.2_322.2
PTGDS_HUMAN
0.07

DPNGLPPEAQK_583.3_669.4
RET4_HUMAN
1.36

WNFAYWAAHQPWSR_607.3_545.3
PRG2_HUMAN
−1.27

ELCLDPK_437.7_359.2
IL8_HUMAN
0.3

FFQYDTWK_567.8_840.4
IGF2_HUMAN
1.83

IIEVEEEQEDPYLNDR_
FBLN1_HUMAN
1.14

996.0_777.4

ECEELEEK_533.2_405.2
IL15_HUMAN
1.78

LEEHYELR_363.5_580.3
PAI2_HUMAN
0.15

LNIGYIEDLK_589.3_837.4
PAI2_HUMAN
0.32

TAVTANLDIR_537.3_288.2
CHL1_HUMAN
−0.98

SWNEPLYHLVTEVR_581.6_716.4
PRL_HUMAN
1.88

ILNIFGVIK_508.8_790.5
TFR1_HUMAN
0.05

TPSAAYLWVGTGASEAEK_
GELS_HUMAN
−2.69

919.5_849.4

VGVISFAQK_474.8_693.4
TFR2_HUMAN
−5.68

LNIGYIEDLK_589.3_950.5
PAI2_HUMAN
−1.43

GQVPENEANVVITTLK_571.3_462.3
CADH1_HUMAN
−0.55

STPSLTTK_417.7_549.3
IL6RA_HUMAN
−0.59

ALLLGWVPTR_563.3_373.2
PAR4_HUMAN
−0.97

TABLE 20

Lasso SummedCoef Early Window

SumBest

Transition
Protein
Coefs

LIQDAVTGLTVNGQITGDK_
ITIH3_HUMAN
1173.723955

972.0_798.4

ALNHLPLEYNSALYSR_
CO6_HUMAN
811.0150364

621.0_538.3

ELIEELVNITQNQK_
IL13_HUMAN
621.9659363

557.6_618.3

VQTAHFK_277.5_431.2
CO8A_HUMAN
454.178544

NIQSVNVK_451.3_674.4
GROA_HUMAN
355.9550674

TLFIFGVTK_
PSG4_HUMAN
331.8629189

513.3_811.5

GPGEDFR_389.2_322.2
PTGDS_HUMAN
305.9079494

FLPCENK_454.2_550.2
IL10_HUMAN
296.9473975

FLNWIK_410.7_560.3
HABP2_HUMAN
282.9841332

LIENGYFHPVK_
F13B_HUMAN
237.5320227

439.6_627.4

ECEELEEK_533.2_405.2
IL15_HUMAN
200.38281

FGFGGSTDSGPIR_
ADA12_HUMAN
194.6252869

649.3_745.4

QALEEFQK_496.8_680.3
CO8B_HUMAN
179.2518843

IIEVEEEQEDPYLNDR_
FBLN1_HUMAN
177.7534111

996.0_777.4

TYLHTYESEI_
ENPP2_HUMAN
164.9735228

628.3_908.4

ELIEELVNITQNQK_
IL13_HUMAN
162.2414693

557.6_517.3

LEEHYELR_363.5_580.3
PAI2_HUMAN
152.9262386

ISQGEADINIAFYQR_
MMP8_HUMAN
144.2445011

575.6_684.4

HPWIVHWDQLPQYQLNR_
KS6A3_HUMAN
140.2287926

744.0_918.5

AHYDLR_387.7_288.2
FETUA_HUMAN
137.9737525

GFQALGDAADIR_
TIMP1_HUMAN
130.4945567

617.3_288.2

SWNEPLYHLVTEVR_
PRL_HUMAN
127.442646

581.6_716.4

SGVDLADSNQK_
VGFR3_HUMAN
120.5149446

567.3_591.3

YENYTSSFFIR_
IL12B_HUMAN
117.0947487

713.8_293.1

FFQYDTWK_567.8_840.4
IGF2_HUMAN
109.8569617

HYFIAAVER_
FA8_HUMAN
106.9426543

553.3_658.4

ITGFLKPGK_
LBP_HUMAN
103.8056505

320.9_429.3

DALSSVQESQVAQQAR_
APOC3_HUMAN
98.50490812

573.0_502.3

SGVDLADSNQK_
VGFR3_HUMAN
97.19989285

567.3_662.3

ALDLSLK_380.2_575.3
ITIH3_HUMAN
94.84900337

TGVAVNKPAEFTVDAK_
FLNA_HUMAN
92.52335783

549.6_258.1

HPWIVHWDQLPQYQLNR_
KS6A3_HUMAN
91.77547608

744.0_1047.0

LIQDAVTGLTVNGQITGDK_
ITIH3_HUMAN
83.6483639

972.0_640.4

LNIGYIEDLK_
PAI2_HUMAN
83.50221521

589.3_837.4

IALGGLLFPASNLR_
SHBG_HUMAN
79.33146741

481.3_657.4

LPATEKPVLLSK_
HYOU1_HUMAN
78.89429168

432.6_460.3

FQLSETNR_497.8_605.3
PSG2_HUMAN
78.13445824

NEIVFPAGILQAPFYTR_
ECE1_HUMAN
75.12145257

968.5_357.2

ALDLSLK_380.2_185.1
ITIH3_HUMAN
63.05454715

DLHLSDVFLK_
CO6_HUMAN
58.26831142

396.2_366.2

TQILEWAAER_
EGLN_HUMAN
57.29461621

608.8_761.4

FSVVYAK_407.2_381.2
FETUA_HUMAN
54.78436389

VSEADSSNADWVTK_
CFAB_HUMAN
54.40003244

754.9_347.2

DPNGLPPEAQK_
RET4_HUMAN
53.89169348

583.3_669.4

VQEAHLTEDQIFYFPK_
CO8G_HUMAN
53.33747599

655.7_701.4

LSSPAVITDK_
PLMN_HUMAN
53.22513181

515.8_830.5

ITLPDFTGDLR_
LBP_HUMAN
51.5477235

624.3_288.2

AVLHIGEK_
THBG_HUMAN
49.73092632

289.5_292.2

GEVTYTTSQVSK_
EGLN_HUMAN
45.14743629

650.3_750.4

GYVIIKPLVWV_
SAMP_HUMAN
44.05164273

643.9_854.6

TGVAVNKPAEFTVDAK_
FLNA_HUMAN
42.99898046

549.6_977.5

YYGYTGAFR_
TRFL_HUMAN
42.90897411

549.3_450.3

ILDGGNK_358.7_490.2
CXCL5_HUMAN
42.60771281

FLPCENK_454.2_390.2
IL10_HUMAN
42.56799651

GFQALGDAADIR_
TIMP1_HUMAN
38.68456017

617.3_717.4

SDGAKPGPR_
COLI_HUMAN
38.47800265

442.7_213.6

NTGVISVVTTGLDR_
CADH1_HUMAN
32.62953675

716.4_662.4

SERPPIFEIR_
LRP1_HUMAN
31.48248968

415.2_288.2

DFHINLFQVLPWLK_
CFAB_HUMAN
31.27286268

885.5_400.2

DALSSVQESQVAQQAR_
APOC3_HUMAN
31.26972354

573.0_672.4

ELCLDPK_
IL8_HUMAN
29.91108737

437.7_359.2

ILNIFGVIK_
TFR1_HUMAN
29.88784921

508.8_790.5

TEFLSNYLTNVDDITLVPGT
ENPP2_HUMAN
29.42327998

LGR_846.8_600.3

GAVHVVVAETDYQSFAVLYL
CO8G_HUMAN
26.70286929

ER_822.8_863.5

AVLHIGEK_289.5_348.7
THBG_HUMAN
25.78703299

TFLTVYWTPER_
ICAM1_HUMAN
24.73090242

706.9_401.2

AGITIPR_364.2_486.3
IL17_HUMAN
23.84580477

GAVHVVVAETDYQSFAVLYL
CO8G_HUMAN
23.81167843

ER_822.8_580.3

SLQAFVAVAAR_
IL23A_HUMAN
23.61468839

566.8_487.3

SWNEPLYHLVTEVR_
PRL_HUMAN
23.2538221

581.6_614.3

TYLHTYESEI_
ENPP2_HUMAN
22.70115313

628.3_515.3

TAHISGLPPSTDFIVYLSGL
TENA_HUMAN
22.42695892

APSIR_871.5_800.5

QNYHQDSEAAINR_
FRIH_HUMAN
21.96827269

515.9_544.3

AHQLAIDTYQEFEETYIPK_
CSH_HUMAN
21.75765717

766.0_634.4

GDTYPAELYITGSILR_
F13B_HUMAN
20.89751398

885.0_274.1

AHYDLR_387.7_566.3
FETUA_HUMAN
20.67629529

IALGGLLFPASNLR_
SHBG_HUMAN
19.28973033

481.3_412.3

ATNATLDPR_
PAR1_HUMAN
18.77604574

479.8_272.2

FSVVYAK_407.2_579.4
FETUA_HUMAN
17.81136564

HTLNQIDEVK_
FETUA_HUMAN
17.29763288

598.8_951.5

DIPHWLNPTR_
PAPP1_HUMAN
17.00562521

416.9_373.2

LYYGDDEK_
CO8A_HUMAN
16.78897272

501.7_563.2

AALAAFNAQNNGSNFQLEE
FETUA_HUMAN
16.41986569

ISR_789.1_633.3

IQTHSTTYR_
F13B_HUMAN
15.78335174

369.5_627.3

GPITSAAELNDPQSILLR_
EGLN_HUMAN
15.3936876

632.4_826.5

QTLSWTVTPK_
PZP_HUMAN
14.92509259

580.8_818.4

AVGYLITGYQR_
PZP_HUMAN
13.9795325

620.8_737.4

DIIKPDPPK_
IL12B_HUMAN
13.76508282

511.8_342.2

YNCILLR_403.7_288.2
ENOA_HUMAN
12.61733711

GNGLTWAEK_
C163B_HUMAN
12.5891421

488.3_634.3

QVFAVQR_424.2_473.3
ELNE_HUMAN
12.57709327

FLQEQGHR_
CO8G_HUMAN
12.51843475

338.8_497.3

HVVQLR_376.2_515.3
IL6RA_HUMAN
11.83747559

DVLLLVHNLPQNLTGHIW
PSG7_HUMAN
11.69074708

YK_791.8_883.0

TFLTVYWTPER_
ICAM1_HUMAN
11.63709776

706.9_502.3

VELAPLPSWQPVGK_
ICAM1_HUMAN
10.79897269

760.9_400.3

TLFIFGVTK_
PSG4_HUMAN
10.2831751

513.3_215.1

AYSDLSR_406.2_375.2
SAMP_HUMAN
10.00461148

HATLSLSIPR_
VGFR3_HUMAN
9.967933028

365.6_472.3

LQGTLPVEAR_
CO5_HUMAN
9.963760572

542.3_571.3

NTVISVNPSTK_
VCAM1_HUMAN
9.124228658

580.3_732.4

EVFSKPISWEELLQ_
FA40A-HUMAN
8.527980294

852.9_260.2

SLCINASAIESILK_
IL3_HUMAN
8.429061621

687.4_860.5

IQHPFTVEEFVLPK_
PZP_HUMAN
7.996504258

562.0_861.5

GVTGYFTFNLYLK_
PSG5_HUMAN
7.94396229

508.3_683.9

VFQYIDLHQDEFVQTLK_
CNDP1_HUMAN
7.860590049

708.4_361.2

ILDDLSPR_464.8_587.3
ITIH4_HUMAN
7.593889262

LIENGYFHPVK_
F13B_HUMAN
7.05838337

439.6_343.2

VFQFLEK_455.8_811.4
CO5_HUMAN
6.976884759

AFTECCVVASQLR_
CO5_HUMAN
6.847474286

770.9_574.3

WWGGQPLWITATK_
ENPP2_HUMAN
6.744837357

772.4_929.5

IQTHSTTYR_
F13B_HUMAN
6.71464509

369.5_540.3

IAQYYYTFK_
F13B_HUMAN
6.540497911

598.8_395.2

YGFYTHVFR_
THRB_HUMAN
6.326347548

397.2_421.3

YHFEALADTGISSEFYDNAN
CO8A_HUMAN
6.261787525

DLLSK_940.8_874.5

ANDQYLTAAALHNLDEAVK_
IL1A_HUMAN
6.217191651

686.4_301.1

FSLVSGWGQLLDR_
FA7-HUMAN
6.1038295

493.3_403.2

GWVTDGFSSLK_
APOC3_HUMAN
6.053494609

598.8_854.4

TLEAQLTPR_514.8_814.4
HEP2_HUMAN
5.855967278

VSAPSGTGHLPGLNPL_
PSG3_HUMAN
5.625944609

506.3_300.7

EAQLPVIENK_
PLMN_HUMAN
5.407703773

570.8_699.4

SPEAEDPLGVER_
Z512B_HUMAN
5.341420139

649.8_670.4

IAIDLFK_410.3_635.4
HEP2_HUMAN
4.698739039

YEFLNGR_449.7_293.1
PLMN_HUMAN
4.658286706

VQTAHFK_277.5_502.3
CO8A_HUMAN
4.628247194

IEVIITLK_464.8_815.5
CXL11_HUMAN
4.57198762

ILTPEVR_414.3_601.3
GDF15_HUMAN
4.452884608

LEEHYELR_363.5_288.2
PAI2_HUMAN
4.411983862

HATLSLSIPR_
VGFR3_HUMAN
4.334242077

365.6_272.2

NSDQEIDFK_
S10A5_HUMAN
4.25302369

548.3_294.2

LPNNVLQEK_
AFAM-HUMAN
4.183602548

527.8_844.5

ELANTIK_394.7_475.3
S10AC_HUMAN
4.13558153

LSIPQITTK_
PSG5_HUMAN
3.966238797

500.8_687.4

TLNAYDHR_
PAR3_HUMAN
3.961140111

330.5_312.2

WWGGQPLWITATK_
ENPP2_HUMAN
3.941476057

772.4_373.2

ELLESYIDGR_
THRB_HUMAN
3.832723338

597.8_710.4

ATLSAAPSNPR_
CXCL2_HUMAN
3.82834767

542.8_570.3

VVLSSGSGPGLDLPLVLGLP
SHBG_HUMAN
3.80737887

LQLK_791.5_598.4

NADYSYSVWK_
CO5_HUMAN
3.56404167

616.8_333.2

ILILPSVTR_
PSGx_HUMAN
3.526998593

506.3_559.3

ALEQDLPVNIK_
CNDP1_HUMAN
3.410412424

620.4_798.5

QVCADPSEEWVQK_
CCL3_HUMAN
3.30795151

788.4_275.2

SVCINDSQAIAEVLNQLK_
DESP_HUMAN
3.259270741

619.7_914.5

QVFAVQR_424.2_620.4
ELNE_HUMAN
3.211482663

ALPGEQQPLHALTR_
IBP1_HUMAN
3.211207158

511.0_807.5

LEPLYSASGPGLRPLVIK_
CAA60698
3.203088951

637.4_260.2

GTYLYNDCPGPGQDTDCR_
TNR1A_HUMAN
3.139418139

697.0_666.3

DAGLSWGSAR_
NEUR4_HUMAN
3.005197927

510.2_576.3

YGFYTHVFR_
THRB_HUMAN
2.985663918

397.2_659.4

NNQLVAGYLQGPNVNLEEK_
IL1RA_HUMAN
2.866983196

700.7_357.2

EKPAGGIPVLGSLVNTVLK_
BPIB1_HUMAN
2.798965142

631.4_930.6

FGSDDEGR_441.7_735.3
PTHR_HUMAN
2.743283546

IEVNESGTVASSSTAVIVSAR_
PAI1_HUMAN
2.699725572

693.0_545.3

FATTFYQHLADSK_
ANT3_HUMAN
2.615073729

510.3_533.3

DYWSTVK_449.7_347.2
APOC3_HUMAN
2.525459346

QLGLPGPPDVPDHAAYHPF_
ITIH4_HUMAN
2.525383799

676.7_263.1

LSSPAVITDK_
PLMN_HUMAN
2.522306831

515.8_743.4

TEFLSNYLTNVDDITLVPG
ENPP2_HUMAN
2.473366805

TLGR_846.8_699.4

SILFLGK_389.2_201.1
THBG_HUMAN
2.472413913

VTFEYR_407.7_614.3
CRHBP_HUMAN
2.425338167

SVVLIPLGAVDDGEHSCIN
CNDP1_HUMAN
2.421340244

EK_703.0_798.4

HTLNQIDEVK_
FETUA_HUMAN
2.419851187

598.8_958.5

ALNSIIDVYHK_
S10A8_HUMAN
2.367904596

424.9_661.3

ETLALLSTHR_
IL5_HUMAN
2.230076769

570.8_500.3

GLQYAAQEGLLALQSELLR_
LBP_HUMAN
2.205949216

1037.1_858.5

TYNVDK_370.2_262.1
PPB1_HUMAN
2.11849772

FTITAGSK_412.7_576.3
FABPL_HUMAN
2.098589805

GIVEECCFR_
IGF2_HUMAN
2.059942995

585.3_900.3

YGIEEHGK_
CXA1_HUMAN
2.033828589

311.5_599.3

ALVLELAK_
INHBE_HUMAN
1.993820617

428.8_331.2

ITLPDFTGDLR_
LBP_HUMAN
1.968753183

624.3_920.5

HELTDEELQSLFTNFANVV
AFAM_HUMAN
1.916438806

DK_817.1_906.5

EANQSTLENFLER_
IL4_HUMAN
1.902033355

775.9_678.4

DADPDTFFAK_
AFAM_HUMAN
1.882254674

563.8_825.4

LFIPQITR_
PSG9_HUMAN
1.860649392

494.3_727.4

DPNGLPPEAQK_
RET4_HUMAN
1.847702127

583.3_497.2

VEPLYELVTATDFAYSSTV
CO8B_HUMAN
1.842159131

R_754.4_549.3

FQLSETNR_497.8_476.3
PSG2_HUMAN
1.834693717

FSLVSGWGQLLDR_
FA7_HUMAN
1.790582748

493.3_516.3

NKPGVYTDVAYYLAWIR_
FA12_HUMAN
1.777303353

677.0_545.3

FTGSQPFGQGVEHATANK_
TSP1_HUMAN
1.736517431

626.0_521.2

DDLYVSDAFHK_
ANT3_HUMAN
1.717534082

655.3_704.3

AFLEVNEEGSEAAASTAVVI
ANT3_HUMAN
1.679420475

AGR_764.4_685.4

LPNNVLQEK_
AFAM_HUMAN
1.66321148

527.8_730.4

IVLSLDVPIGLLQILLEQA
UCN2_HUMAN
1.644983604

R_735.1_503.3

DPTFIPAPIQAK_
ANGT_HUMAN
1.625411496

433.2_556.3

SDLEVAHYK_
CO8B_HUMAN
1.543640117

531.3_617.3

QLYGDTGVLGR_
CO8G_HUMAN
1.505242962

589.8_501.3

VNHVTLSQPK_
B2MG_HUMAN
1.48233058

374.9_459.3

TLLPVSKPEIR_
CO5_HUMAN
1.439531341

418.3_288.2

SEYGAALAWEK_
CO6_HUMAN
1.424401638

612.8_845.5

YGIEEHGK_311.5_341.2
CXA1_HUMAN
1.379872204

DAGLSWGSAR_
NEUR4_HUMAN
1.334272677

510.3_390.2

AEHPTWGDEQLFQTTR_
PGH1_HUMAN
1.30549273

639.3_569.3

FQSVFTVTR_
C1QC_HUMAN
1.302847429

542.8_623.4

VPGLYYFTYHASSR_
C1QB_HUMAN
1.245565877

554.3_420.2

AYSDLSR_406.2_577.3
SAMP_HUMAN
1.220777002

ALEQDLPVNIK_
CNDP1_HUMAN
1.216612522

620.4_570.4

NAVVQGLEQPHGLVVHPLR_
LRP1_HUMAN
1.212935735

688.4_890.6

TSDQIHFFFAK_
ANT3_HUMAN
1.176238265

447.6_659.4

GTYLYNDCPGPGQDTDCR_
TNR1A_HUMAN
1.1455649

697.0_335.2

TSYQVYSK_488.2_787.4
C163A_HUMAN
1.048896429

ALNSIIDVYHK_
S10A8_HUMAN
1.028522516

424.9_774.4

VELAPLPSWQPVGK_
ICAM1_HUMAN
0.995831393

760.9_342.2

LSETNR_360.2_330.2
PSG1_HUMAN
0.976094717

HFQNLGK_422.2_527.2
AFAM_HUMAN
0.956286531

ELPQSIVYK_
FBLN3_HUMAN
0.947931674

538.8_417.7

LPATEKPVLLSK_
HYOU1_HUMAN
0.932537153

432.6_347.2

SPEAEDPLGVER_
Z512B_HUMAN
0.905955419

649.8_314.1

DEIPHNDIALLK_
HABP2_HUMAN
0.9032484

459.9_510.8

FFQYDTWK_567.8_712.3
IGF2_HUMAN
0.884340285

LIEIANHVDK_
ADA12_HUMAN
0.881493383

384.6_498.3

AGFAGDDAPR_
ACTB_HUMAN
0.814836556

488.7_701.3

YEFLNGR_449.7_606.3
PLMN_HUMAN
0.767373087

VIAVNEVGR_
CHL1_HUMAN
0.721519592

478.8_284.2

SLSQQIENIR_
CO1A1_HUMAN
0.712051082

594.3_531.3

EWVAIESDSVQPVPR_
CNDP1_HUMAN
0.647712421

856.4_486.2

YGLVTYATYPK_
CFAB_HUMAN
0.618499569

638.3_843.4

SVVLIPLGAVDDGEHSCINE
CNDP1_HUMAN
0.606626346

K_703.0_286.2

NSDQEIDFK_
S10A5_HUMAN
0.601928175

548.3_409.2

NVNQSLLELHK_
FRIH_HUMAN
0.572008792

432.2_543.3

IAQYYYTFK_5
F13B_HUMAN
0.495062844

98.8_884.4

GPITSAAELNDPQSILLR_
EGLN_HUMAN
0.47565795

632.4_601.4

YTTEIIK_434.2_704.4
C1R_HUMAN
0.433318952

GYVIIKPLVWV_
SAMP_HUMAN
0.427905264

643.9_304.2

LDFHFSSDR_
INHBC_HUMAN
0.411898116

375.2_464.2

IPSNPSHR_
FBLN3_HUMAN
0.390037291

303.2_496.3

APLTKPLK_
CRP_HUMAN
0.38859469

289.9_357.2

EVFSKPISWEELLQ_
FA40A_HUMAN
0.371359974

852.9_376.2

YENYTSSFFIR_
IL12B_HUMAN
0.346336267

713.8_756.4

SPQAFYR_434.7_556.3
REL3_HUMAN
0.345901234

SVDEALR_395.2_488.3
PRDX2_HUMAN
0.307518869

FVFGTTPEDILR_
TSP1_HUMAN
0.302313589

697.9_742.4

FTFTLHLETPKPSISSSNLN
PSG1_HUMAN
0.269826678

PR_829.4_787.4

VGEYSLYIGR_
SAMP_HUMAN
0.226573173

578.8_708.4

ILPSVPK_377.2_244.2
PGH1_HUMAN
0.225429414

LFIPQITR_494.3_614.4
PSG9_HUMAN
0.18285533

TGYYFDGISR_
FBLN1_HUMAN
0.182474114

589.8_857.4

HYGGLTGLNK_
PGAM1_HUMAN
0.152397007

530.3_759.4

NQSPVLEPVGR_
KS6A3_HUMAN
0.128963949

598.3_866.5

IGKPAPDFK_
PRDX2_HUMAN
0.113383235

324.9_294.2

TSESTGSLPSPFLR_
PSMG1_HUMAN
0.108159874

739.9_716.4

ESDTSYVSLK_
CRP_HUMAN
0.08569303

564.8_347.2

ETPEGAEAKPWYEPIYLGGV
TNFA_HUMAN
0.039781728

FQLEK_951.1_877.5

TSDQIHFFFAK_
ANT3_HUMAN
0.008064465

447.6_512.3

TABLE 21

Lasso32 Middle Window

Coef-

Variable
UniProt_ID
ficient

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
6.99

VFQFLEK_455.8_811.4
CO5_HUMAN
6.43

VLEPTLK_400.3_458.3
VTDB_HUMAN
3.99

SLDFTELDVAAEK_719.4_316.2
ANGT_HUMAN
3.33

TLAFVR_353.7_492.3
FA7_HUMAN
2.44

YGIEEHGK_311.5_599.3
CXA1_HUMAN
2.27

LHEAFSPVSYQHDLALLR_
FA12_HUMAN
2.14

699.4_251.2

QGHNSVFLIK_381.6_520.4
HEMO_HUMAN
0.25

LLAPSDSPEWLSFDVTGVVR_
TGFB1_HUMAN
−2.81

730.1_430.3

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
−3.46

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
−6.61

TABLE 22

Lasso100 Middle Window

Coef-

Variable
UniProt_ID
ficient

VFQFLEK_455.8_811.4
CO5_HUMAN
6.89

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
4.67

GEVTYTTSQVSK_650.3_750.4
EGLN_HUMAN
3.4

QVFAVQR_424.2_473.3
ELNE_HUMAN
1.94

VELAPLPSWQPVGK_760.9_342.2
ICAM1_HUMAN
1.91

LHEAFSPVSYQHDLALLR_
FA12_HUMAN
1.8

699.4_251.2

SLDFTELDVAAEK_719.4_316.2
ANGT_HUMAN
1.67

YGIEEHGK_311.5_599.3
CXA1_HUMAN
1.53

YGIEEHGK_311.5_341.2
CXA1_HUMAN
1.51

HYINLITR_515.3_301.1
NPY_HUMAN
1.47

TLAFVR_353.7_492.3
FA7_HUMAN
1.46

GVTGYFTFNLYLK_508.3_260.2
PSG5_HUMAN
1.28

FSLVSGWGQLLDR_493.3_403.2
FA7_HUMAN
0.84

DALSSVQESQVAQQAR_
APOC3_HUMAN
0.41

573.0_502.3

VELAPLPSWQPVGK_760.9_400.3
ICAM1_HUMAN
0.3

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
−0.95

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
−1.54

DVLLLVHNLPQNLTGHIWYK_
PSG7_HUMAN
−1.54

791.8_310.2

VPLALFALNR_557.3_620.4
PEPD_HUMAN
−1.91

LLAPSDSPEWLSFDVTGVVR_
TGFB1_HUMAN
−2.3

730.1_430.3

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
−3.6

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
−3.96

TABLE 23

Lasso Protein Middle Window

Coef-

Variable
UniProt_ID
ficient

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
5.84

VFQFLEK_455.8_811.4
CO5_HUMAN
5.58

SLDFTELDVAAEK_719.4_316.2
ANGT_HUMAN
2.11

TLAFVR_353.7_492.3
FA7_HUMAN
1.83

LHEAFSPVSYQHDLALLR_
FA12_HUMAN
1.62

699.4_251.2

HYINLITR_515.3_301.1
NPY_HUMAN
1.39

VLEPTLK_400.3_458.3
VTDB_HUMAN
1.37

YGIEEHGK_311.5_599.3
CXA1_HUMAN
1.17

VELAPLPSWQPVGK_
ICAM1_HUMAN
1.13

760.9_342.2

QVFAVQR_424.2_473.3
ELNE_HUMAN
0.79

ANLINNIFELAGLGK_
LCAP_HUMAN
0.23

793.9_299.2

DVLLLVHNLPQNLTGHIWYK_
PSG7_HUMAN
−0.61

791.8_310.2

VEHSDLSFSK_383.5_234.1
B2MG_HUMAN
−0.69

AVDIPGLEAATPYR_
TENA_HUMAN
−0.85

736.9_399.2

VPLALFALNR_557.3_620.4
PEPD_HUMAN
−1.45

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
−1.9

LLAPSDSPEWLSFDVTGVVR_
TGFB1_HUMAN
−2.07

730.1_430.3

EVFSKPISWEELLQ_
FA40A_HUMAN
−2.32

852.9_376.2

TABLE 24

Lasso All Middle Window

Coef-

Variable
UniProt_ID
ficient

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
2.48

VFQFLEK_455.8_811.4
CO5_HUMAN
2.41

SLDFTELDVAAEK_719.4_316.2
ANGT_HUMAN
1.07

YGIEEHGK_311.5_599.3
CXA1_HUMAN
0.64

VLEPTLK_400.3_458.3
VTDB_HUMAN
0.58

LHEAFSPVSYQHDLALLR_
FA12_HUMAN
0.21

699.4_251.2

LLAPSDSPEWLSFDVTGVVR_
TGFB1_HUMAN
−0.62

730.1_430.3

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
−1.28

TABLE 25

Lasso32 Middle-Late Window

Coef-

Variable
UniProt_ID
ficient

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
4.35

TLAFVR_353.7_492.3
FA7_HUMAN
2.42

YGIEEHGK_311.5_599.3
CXA1_HUMAN
1.46

DFNQFSSGEK_386.8_333.2
FETA_HUMAN
1.37

VFQFLEK_455.8_811.4
CO5_HUMAN
0.89

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
0.85

QINSYVK_426.2_496.3
CBG_HUMAN
0.56

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.53

SLQAFVAVAAR_566.8_804.5
IL23A_HUMAN
0.39

TEQAAVAR_423.2_615.4
FA12_HUMAN
0.26

VLEPTLK_400.3_587.3
VTDB_HUMAN
0.24

AQPVQVAEGSEPDGFWEALGGK_
GELS_HUMAN
−2.08

758.0_574.3

VPLALFALNR_557.3_620.4
PEPD_HUMAN
−2.09

AVYEAVLR_460.8_587.4
PEPD_HUMAN
−3.37

TABLE 26

Lasso100 Middle-Late Window

Variable
UniProt_ID
Coefficient

VFQFLEK_455.8_811.4
CO5_HUMAN
3.82

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
2.94

YGIEEHGK_311.5_599.3
CXA1_HUMAN
2.39

DPTFIPAPIQAK_433.2_556.3
ANGT_HUMAN
2.05

TLAFVR_353.7_492.3
FA7_HUMAN
1.9

NQSPVLEPVGR_598.3_866.5
KS6A3_HUMAN
1.87

ALNHLPLEYNSALYSR_621.0_
CO6_HUMAN
1.4

538.3

TQILEWAAER_608.8_761.4
EGLN_HUMAN
1.29

VVGGLVALR_442.3_784.5
FA12_HUMAN
1.24

QINSYVK_426.2_496.3
CBG_HUMAN
1.14

YGIEEHGK_311.5_341.2
CXA1_HUMAN
0.84

ALEQDLPVNIK_620.4_570.4
CNDP1_HUMAN
0.74

GTYLYNDCPGPGQDTDCR_697.0_
TNR1A_HUMAN
0.51

666.3

SLCINASAIESILK_687.4_860.5
IL3_HUMAN
0.44

DLHLSDVFLK_396.2_260.2
CO6_HUMAN
0.38

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
0.37

NIQSVNVK_451.3_674.4
GROA_HUMAN
0.3

FFQYDTWK_567.8_712.3
IGF2_HUMAN
0.19

ANLINNIFELAGLGK_793.9_299.2
LCAP_HUMAN
0.19

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.15

AALAAFNAQNNGSNFQLEEISR_
FETUA_HUMAN
−0.09

789.1_746.4

AQPVQVAEGSEPDGFWEALGGK_
GELS_HUMAN
−0.52

758.0_574.3

TSYQVYSK_488.2_787.4
C163A_HUMAN
−0.62

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
−1.29

TAHISGLPPSTDFIVYLSGLAPSIR_
TENA_HUMAN
−1.53

871.5_472.3

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
−1.73

LLAPSDSPEWLSFDVTGVVR_730.1_
TGFB1_HUMAN
−1.95

430.3

VPLALFALNR_557.3_620.4
PEPD_HUMAN
−2.9

AVYEAVLR_460.8_587.4
PEPD_HUMAN
−3.04

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
−3.49

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
−3.71

TABLE 27

Lasso Protein Middle-LateWindow

Variable
UniProt_ID
Coefficient

VFQFLEK_455.8_811.4
CO5_HUMAN
4.25

ALNHLPLEYNSALYSR_621.0_
CO6_HUMAN
3.06

696.4

YGIEEHGK_311.5_599.3
CXA1_HUMAN
2.36

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
2.11

TQILEWAAER_608.8_761.4
EGLN_HUMAN
1.81

NQSPVLEPVGR_598.3_866.5
KS6A3_HUMAN
1.79

TEQAAVAR_423.2_615.4
FA12_HUMAN
1.72

QINSYVK_426.2_496.3
CBG_HUMAN
0.98

ALEQDLPVNIK_620.4_570.4
CNDP1_HUMAN
0.98

NCSFSIIYPVVIK_770.4_555.4
CRHBP_HUMAN
0.76

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
0.63

SLCINASAIESILK_687.4_860.5
IL3_HUMAN
0.59

ANLINNIFELAGLGK_793.9_299.2
LCAP_HUMAN
0.55

GTYLYNDCPGPGQDTDCR_697.0_
TNR1A_HUMAN
0.55

666.3

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.46

NIQSVNVK_451.3_674.4
GROA_HUMAN
0.22

LTTVDIVTLR_565.8_815.5
IL2RB_HUMAN
0.11

FFQYDTWK_567.8_712.3
IGF2_HUMAN
0.01

TSYQVYSK_488.2_787.4
C163A_HUMAN
−0.76

AQPVQVAEGSEPDGFWEALGGK_
GELS_HUMAN
−1.31

758.0_574.3

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
−1.59

LLAPSDSPEWLSFDVTGVVR_
TGFB1_HUMAN
−1.73

730.1_430.3

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
−2.02

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
−3

TGVAVNKPAEFTVDAK_549.6_
FLNA_HUMAN
−3.15

258.1

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
−3.49

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
−3.82

VPLALFALNR_557.3_620.4
PEPD_HUMAN
−4.94

TABLE 28

Lasso All Middle-LateWindow

Variable
UniProt_ID
Coefficient

ALNHLPLEYNSALYSR_621.0_
CO6_HUMAN
2.38

538.3

TLAFVR_353.7_492.3
FA7_HUMAN
0.96

YGIEEHGK_311.5_599.3
CXA1_HUMAN
0.34

DPTFIPAPIQAK_433.2_461.2
ANGT_HUMAN
0.33

DFNQFSSGEK_386.8_333.2
FETA_HUMAN
0.13

QINSYVK_426.2_496.3
CBG_HUMAN
0.03

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0

AQPVQVAEGSEPDGFWEALGGK_
GELS_HUMAN
−0.02

758.0_574.3

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
−0.05

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
−0.12

LLAPSDSPEWLSFDVTGVVR_730._
TGFB1_HUMAN
−0.17

1430.3

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
−0.31

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
−0.35

VPLALFALNR_557.3_620.4
PEPD_HUMAN
−0.43

AVYEAVLR_460.8_587.4
PEPD_HUMAN
−2.33

TABLE 29

Lasso 32 LateWindow

Variable
UniProt_ID
Coefficient

QINSYVK_426.2_610.3
CBG_HUMAN
3.24

ILDGGNK_358.7_603.3
CXCL5_HUMAN
2.65

VFQYIDLHQDEFVQTLK_708.4_
CNDP1_HUMAN
2.55

375.2

SGVDLADSNQK_567.3_662.3
VGFR3_HUMAN
2.12

YSHYNER_323.5_418.2
HABP2_HUMAN
1.63

DEIPHNDIALLK_459.9_510.8
HABP2_HUMAN
1.22

SGVDLADSNQK_567.3_591.3
VGFR3_HUMAN
0.96

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
0.86

GTYLYNDCPGPGQDTDCR_697.0_
TNR1A_HUMAN
0.45

666.3

TSYQVYSK_488.2_787.4
C163A_HUMAN
−1.73

TGVAVNKPAEFTVDAK_549.6_
FLNA_HUMAN
−2.56

258.1

SPEAEDPLGVER_649.8_314.1
Z512B_HUMAN
−3.04

VPLALFALNR_557.3_620.4
PEPD_HUMAN
−3.33

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
−4.24

AVYEAVLR_460.8_587.4
PEPD_HUMAN
−5.83

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
−6.52

AALAAFNAQNNGSNFQLEEISR_
FETUA_HUMAN
−6.55

789.1_746.4

TABLE 30

Lasso 100 Late Window

Variable
UniProt_ID
Coefficient

SGVDLADSNQK_567.3_662.3
VGFR3_HUMAN
4.13

ILDGGNK_358.7_603.3
CXCL5_HUMAN
3.57

QINSYVK_426.2_610.3
CBG_HUMAN
3.41

DEIPHNDIALLK_459.9_510.8
HABP2_HUMAN
1.64

VFQYIDLHQDEFVQTLK_708.4_
CNDP1_HUMAN
1.57

375.2

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
1.45

LTTVDIVTLR_565.8_815.5
IL2RB_HUMAN
0.71

YSHYNER_323.5_418.2
HABP2_HUMAN
0.68

FFQYDTWK_567.8_712.3
IGF2_HUMAN
0.42

IEVNESGTVASSSTAVIVSAR_
PAI1_HUMAN
0.36

693.0_545.3

GTYLYNDCPGPGQDTDCR_697.0_
TNR1A_HUMAN
0.21

666.3

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
0.1

VGVISFAQK_474.8_580.3
TFR2_HUMAN
0.08

TSYQVYSK_488.2_787.4
C163A_HUMAN
−0.36

ALNFGGIGVVVGHELTHAFDDQGR_
ECE1_HUMAN
−0.65

837.1_360.2

AYSDLSR_406.2_375.2
SAMP_HUMAN
−1.23

TGVAVNKPAEFTVDAK_549.6_
FLNA_HUMAN
−1.63

258.1

SPEAEDPLGVER_649.8_314.1
Z512B_HUMAN
−2.29

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
−2.58

VPLALFALNR_557.3_620.4
PEPD_HUMAN
−2.73

YISPDQLADLYK_713.4_277.2
ENOA_HUMAN
−2.87

AVDIPGLEAATPYR_736.9_286.1
TENA_HUMAN
−3.9

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
−5.29

AVYEAVLR_460.8_587.4
PEPD_HUMAN
−5.51

AALAAFNACINNGSNFQLEEISR_
FETUA_HUMAN
−6.49

789.1_746.4

TABLE 31

Lasso Protein Late Window

Variable
UniProt_ID
Coefficient

SGVDLADSNQK_567.3_662.3
VGFR3_HUMAN
3.33

ILDGGNK_358.7_603.3
CXCL5_HUMAN
3.25

QINSYVK_426.2_496.3
CBG_HUMAN
2.41

YSHYNER_323.5_418.2
HABP2_HUMAN
1.82

ALEQDLPVNIK_620.4_798.5
CNDP1_HUMAN
1.32

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
1.27

GTYLYNDCPGPGQDTDCR_
TNR1A_HUMAN
0.26

697.0_666.3

IEVNESGTVASSSTAVIVSAR_
PAI1_HUMAN
0.18

693.0_545.3

LTTVDIVTLR_565.8_815.5
IL2RB_HUMAN
0.18

TSYQVYSK_488.2_787.4
C163A_HUMAN
−0.11

TGVAVNKPAEFTVDAK_549.6_
FLNA_HUMAN
−0.89

258.1

AYSDLSR_406.2_375.2
SAMP_HUMAN
−1.47

SPEAEDPLGVER_649.8_314.1
Z512B_HUMAN
−1.79

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
−2.22

YISPDQLADLYK_713.4_277.2
ENOA_HUMAN
−2.41

AVDIPGLEAATPYR_736.9_286.1
TENA_HUMAN
−2.94

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
−5.18

AALAAFNAQNNGSNFQLEEISR_
FETUA_HUMAN
−5.71

789.1_746.4

AVYEAVLR_460.8_587.4
PEPD_HUMAN
−7.33

TABLE 32

Lasso All Late Window

Variable
UniProt_ID
Coefficient

QINSYVK_426.2_496.3
CBG_HUMAN
0.5

DEIPHNDIALLK_459.9_510.8
HABP2_HUMAN
0.15

ALEQDLPVNIK_620.4_570.4
CNDP1_HUMAN
0.11

ILDGGNK_358.7_603.3
CXCL5_HUMAN
0.08

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
0.06

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
−0.39

AALAAFNACINNGSNFQLEEISR_
FETUA_HUMAN
−1.57

789.1_746.4

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
−2.46

AVYEAVLR_460.8_587.4
PEPD_HUMAN
−2.92

TABLE 33

Random Forest 32 Early Window

Variable
Protein
MeanDecreaseGini

ELIEELVNITQNQK_557.6_
IL13_HUMAN
3.224369171

517.3

AHYDLR_387.7_288.2
FETUA_HUMAN
1.869007658

FSVVYAK_407.2_381.2
FETUA_HUMAN
1.770198171

ITLPDFTGDLR_624.3_
LBP_HUMAN
1.710936472

288.2

ITGFLKPGK_320.9_301.2
LBP_HUMAN
1.623922439

ITGFLKPGK_320.9_429.3
LBP_HUMAN
1.408035272

ELIEELVNITQNQK_557.6_
IL13_HUMAN
1.345412168

618.3

VFQFLEK_455.8_811.4
CO5_HUMAN
1.311332013

VQTAHFK_277.5_431.2
CO8A_HUMAN
1.308902373

FLNWIK_410.7_560.3
HABP2_HUMAN
1.308093745

DAGLSWGSAR_510.3_390.2
NEUR4_HUMAN
1.297033607

TLLPVSKPEIR_418.3_
CO5_HUMAN
1.291280928

288.2

LIQDAVTGLTVNGQITGDK_
ITIH3_HUMAN
1.28622301

972.0_798.4

QALEEFQK_496.8_680.3
CO8B_HUMAN
1.191731825

FSVVYAK_407.2_579.4
FETUA_HUMAN
1.078909138

ITLPDFTGDLR_624.3_
LBP_HUMAN
1.072613747

920.5

AHYDLR_387.7_566.3
FETUA_HUMAN
1.029562263

ALNHLPLEYNSALYSR_
CO6_HUMAN
1.00992071

621.0_538.3

DVLLLVHNLPQNLPGYFWYK_
PSG9_HUMAN
1.007095529

810.4_967.5

SFRPFVPR_335.9_635.3
LBP_HUMAN
0.970312536

SDLEVAHYK_531.3_617.3
CO8B_HUMAN
0.967904893

VQEAHLTEDQIFYFPK_
CO8G_HUMAN
0.960398254

655.7_701.4

VFQFLEK_455.8_276.2
CO5_HUMAN
0.931652095

SLLQPNK_400.2_599.4
CO8A_HUMAN
0.926470249

SFRPFVPR_335.9_272.2
LBP_HUMAN
0.911599611

FLNWIK_410.7_561.3
HABP2_HUMAN
0.852022868

LSSPAVITDK_515.8_743.4
PLMN_HUMAN
0.825455824

DVLLLVHNLPQNLPGYFWYK_
PSG9_HUMAN
0.756797142

810.4_594.3

ALVLELAK_428.8_672.4
INHBE_HUMAN
0.748802555

DISEVVTPR_508.3_787.4
CFAB_HUMAN
0.733731518

TABLE 34

Random Forest 100 Early Window

Variable
Protein
MeanDecreaseGini

ELIEELVNITQNQK_557.6_
IL13_HUMAN
1.709778508

517.3

LPNNVLQEK_527.8_844.5
AFAM_HUMAN
0.961692716

AHYDLR_387.7_288.2
FETUA_HUMAN
0.901586746

ITLPDFTGDLR_624.3_
LBP_HUMAN
0.879119498

288.2

IEGNLIFDPNNYLPK_874.0_
APOB_HUMAN
0.842483095

414.2

ITGFLKPGK_320.9_301.2
LBP_HUMAN
0.806905233

FSVVYAK_407.2_381.2
FETUA_HUMAN
0.790429706

ITGFLKPGK_320.9_429.3
LBP_HUMAN
0.710312386

VFQFLEK_455.8_811.4
CO5_HUMAN
0.709531553

LIQDAVTGLTVNGQITGDK_
ITIH3_HUMAN
0.624325189

972.0_798.4

DADPDTFFAK_563.8_825.4
AFAM_HUMAN
0.618684313

FLNWIK_410.7_560.3
HABP2_HUMAN
0.617501242

TASDFITK_441.7_781.4
GELS_HUMAN
0.609275999

DAGLSWGSAR_510.3_390.2
NEUR4_HUMAN
0.588718595

VQTAHFK_277.5_431.2
CO8A_HUMAN
0.58669845

TLLPVSKPEIR_418.3_
COS_HUMAN
0.5670608

288.2

ELIEELVNITQNQK_557.6_
IL13_HUMAN
0.555624783

618.3

TYLHTYESEI_628.3_908.4
ENPP2_HUMAN
0.537678415

HFQNLGK_422.2_527.2
AFAM_HUMAN
0.535543137

TASDFITK_441.7_710.4
GELS_HUMAN
0.532743323

ITLPDFTGDLR_624.3_
LBP_HUMAN
0.51667902

920.5

QALEEFQK_496.8_680.3
CO8B_HUMAN
0.511314017

AVLHIGEK_289.5_348.7
THBG_HUMAN
0.510284122

FSVVYAK_407.2_579.4
FETUA_HUMAN
0.503907813

LPNNVLQEK_527.8_730.4
AFAM_HUMAN
0.501281631

AHYDLR_387.7_566.3
FETUA_HUMAN
0.474166711

IAPQLSTEELVSLGEK_
AFAM_HUMAN
0.459595701

857.5_333.2

WWGGQPLWITATK_772.4_
ENPP2_HUMAN
0.44680777

929.5

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.434157773

DALSSVQESQVAQQAR_
APOC3_HUMAN
0.432484862

573.0_502.3

TABLE 35

Random Forest Protein Early Window

Variable
Protein
MeanDecreaseGini

ELIEELVNITQNQK_557.6_
IL13_HUMAN
2.881452809

517.3

LPNNVLQEK_527.8_844.5
AFAM_HUMAN
1.833987752

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN
1.608843881

IEGNLIFDPNNYLPK_874.0_
APOB_HUMAN
1.594658208

414.2

VFQFLEK_455.8_811.4
CO5_HUMAN
1.290134412

LIQDAVTGLTVNGQITGDK_
ITIH3_HUMAN
1.167981736

972.0_798.4

TASDFITK_441.7_781.4
GELS_HUMAN
1.152847453

DAGLSWGSAR_510.3_390.2
NEUR4_HUMAN
1.146752656

FSVVYAK_407.2_579.4
FETUA_HUMAN
1.060168583

AVLHIGEK_289.5_348.7
THBG_HUMAN
1.033625773

FLNWIK_410.7_560.3
HABP2_HUMAN
1.022356789

QALEEFQK_496.8_680.3
CO8B_HUMAN
0.990074129

DVLLLVHNLPQNLPGYFWYK_
PSG9_HUMAN
0.929633865

810.4_967.5

WWGGQPLWITATK_772.4_
ENPP2_HUMAN
0.905895642

929.5

VQEAHLTEDQIFYFPK_
CO8G_HUMAN
0.883887371

655.7_701.4

NNQLVAGYLQGPNVNLEEK_
IL1RA_HUMAN
0.806472085

700.7_999.5

SLLQPNK_400.2_599.4
CO8A_HUMAN
0.783623222

DALSSVQESQVAQQAR_
APOC3_HUMAN
0.774365756

573.0_672.4

NIQSVNVK_451.3_674.4
GROA_HUMAN
0.767963386

HPWIVHWDQLPQYQLNR_
KS6A3_HUMAN
0.759960139

744.0_1047.0

TTSDGGYSFK_531.7_860.4
INHA_HUMAN
0.732813448

ALNHLPLEYNSALYSR_
CO6_HUMAN
0.718779092

621.0_538.3

LSSPAVITDK_515.8_743.4
PLMN_HUMAN
0.699547739

TGVAVNKPAEFTVDAK_
FLNA_HUMAN
0.693159192

549.6_258.1

TLNAYDHR_330.5_312.2
PAR3_HUMAN
0.647300964

DISEVVTPR_508.3_787.4
CFAB_HUMAN
0.609165621

LIENGYFHPVK_439.6_627.4
F13B_HUMAN
0.60043345

SGVDLADSNQK_567.3_662.3
VGFR3_HUMAN
0.596079858

ALQDQLVLVAAK_634.9_
ANGT_HUMAN
0.579034994

289.2

ALVLELAK_428.8_672.4
INHBE_HUMAN
0.573458483

TABLE 36

Random Forest All Early Window

Variable
Protein
MeanDecreaseGini

ELIEELVNITQNQK_557.6_
IL13_HUMAN
0.730972421

517.3

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN
0.409808774

AHYDLR_387.7_288.2
FETUA_HUMAN
0.409298983

FSVVYAK_407.2_381.2
FETUA_HUMAN
0.367730833

ITGFLKPGK_320.9_301.2
LBP_HUMAN
0.350485117

VFQFLEK_455.8_811.4
CO5_HUMAN
0.339289475

ELIEELVNITQNQK_557.6_
IL13_HUMAN
0.334303166

618.3

LPNNVLQEK_527.8_844.5
AFAM_HUMAN
0.329800706

IEGNLIFDPNNYLPK_
APOB_HUMAN
0.325596677

874.0_414.2

ITGFLKPGK_320.9_429.3
LBP_HUMAN
0.31473104

FLNWIK_410.7_560.3
HABP2_HUMAN
0.299810081

LIQDAVTGLTVNGQITGDK_
ITIH3_HUMAN
0.295613448

972.0_798.4

ITLPDFTGDLR_624.3_920.5
LBP_HUMAN
0.292212699

DAGLSWGSAR_510.3_390.2
NEUR4_HUMAN
0.285812225

TLLPVSKPEIR_418.3_288.2
CO5_HUMAN
0.280857718

FSVVYAK_407.2_579.4
FETUA_HUMAN
0.278531322

DADPDTFFAK_563.8_825.4
AFAM_HUMAN
0.258938798

AHYDLR_387.7_566.3
FETUA_HUMAN
0.256160046

QALEEFQK_496.8_680.3
CO8B_HUMAN
0.245543641

HTLNQIDEVK_598.8_951.5
FETUA_HUMAN
0.239528081

TASDFITK_441.7_781.4
GELS_HUMAN
0.227485958

VFQFLEK_455.8_276.2
CO5_HUMAN
0.226172392

DVLLLVHNLPQNLPGYFWYK_
PSG9_HUMAN
0.218613384

810.4_967.5

VQTAHFK_277.5_431.2
CO8A_HUMAN
0.217171548

SFRPFVPR_335.9_635.3
LBP_HUMAN
0.214798112

HFQNLGK_422.2_527.2
AFAM_HUMAN
0.211756476

SVSLPSLDPASAK_636.4_
APOB_HUMAN
0.211319422

473.3

FGFGGSTDSGPIR_649.3_
ADA12_HUMAN
0.206574494

745.4

HFQNLGK_422.2_285.1
AFAM_HUMAN
0.204024196

AVLHIGEK_289.5_348.7
THBG_HUMAN
0.201102917

TABLE 37

Random Forest SummedGini Early Window

Transition
Protein
SumBestGini

ELIEELVNITQNQK_557.6_517.3
IL13_HUMAN
242.5373659

VFQFLEK_455.8_811.4
CO5_HUMAN
115.1113943

FLNWIK_410.7_560.3
HABP2_HUMAN
107.4572447

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN
104.0742727

LIQDAVTGLTVNGQITGDK_972.0_798.4
ITIH3_HUMAN
103.3238077

DAGLSWGSAR_510.3_390.2
NEUR4_HUMAN
70.4151533

AHYDLR_387.7_288.2
FETUA_HUMAN
140.2670822

FSVVYAK_407.2_381.2
FETUA_HUMAN
121.3664352

LPNNVLQEK_527.8_844.5
AFAM_HUMAN
115.5211679

ITGFLKPGK_320.9_429.3
LBP_HUMAN
114.9512704

ITGFLKPGK_320.9_301.2
LBP_HUMAN
112.916627

IEGNLIFDPNNYLPK_874.0_414.2
APOB_HUMAN
52.21169288

VQTAHFK_277.5_431.2
CO8A_HUMAN
144.5237215

TLLPVSKPEIR_418.3_288.2
CO5_HUMAN
96.16982897

QALEEFQK_496.8_680.3
CO8B_HUMAN
85.35050759

FSVVYAK_407.2_579.4
FETUA_HUMAN
73.23969945

ELIEELVNITQNQK_557.6_618.3
IL13_HUMAN
61.61450671

TASDFITK_441.7_781.4
GELS_HUMAN
61.32155633

DVLLLVHNLPQNLPGYFWYK_810.4_967.5
PSG9_HUMAN
99.68404123

AVLHIGEK_289.5_348.7
THBG_HUMAN
69.96748485

ITLPDFTGDLR_624.3_920.5
LBP_HUMAN
56.66810872

WWGGQPLWITATK_772.4_929.5
ENPP2_HUMAN
56.54173176

VQEAHLTEDQIFYFPK_655.7_701.4
CO8G_HUMAN
47.92505575

DADPDTFFAK_563.8_825.4
AFAM_HUMAN
40.34147696

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
145.0311483

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
109.4072996

FLPCENK_454.2_550.2
IL10_HUMAN
105.7756691

VQTAHFK_277.5_502.3
CO8A_HUMAN
101.5877845

VFQFLEK_455.8_276.2
CO5_HUMAN
95.71159157

TYLHTYESEI_628.3_908.4
ENPP2_HUMAN
94.92157517

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
90.67568777

NKPGVYTDVAYYLAWIR_677.0_545.3
FA12_HUMAN
90.35890105

LEEHYELR_363.5_580.3
PAI2_HUMAN
88.44833508

HPWIVHWDQLPQYQLNR_744.0_1047.0
KS6A3_HUMAN
88.37680942

HTLNQIDEVK_598.8_951.5
FETUA_HUMAN
87.63064143

LPNNVLQEK_527.8_730.4
AFAM_HUMAN
86.64484642

ALDLSLK_380.2_575.3
ITIH3_HUMAN
83.51201287

YGIEEHGK_311.5_599.3
CXA1_HUMAN
82.47620831

LSSPAVITDK_515.8_830.5
PLMN_HUMAN
81.5433587

LEEHYELR_363.5_288.2
PAI2_HUMAN
79.01571985

NVIQISNDLENLR_509.9_402.3
LEP_HUMAN
78.86670236

SGFSFGFK_438.7_732.4
CO8B_HUMAN
78.71961929

SDLEVAHYK_531.3_617.3
CO8B_HUMAN
78.24005567

NADYSYSVWK_616.8_333.2
CO5_HUMAN
76.07974354

AHYDLR_387.7_566.3
FETUA_HUMAN
74.68253347

GAVHVVVAETDYQSFAVLYLER_822.8_580.3
CO8G_HUMAN
73.75860248

LIENGYFHPVK_439.6_627.4
F13B_HUMAN
73.74965194

ALDLSLK_380.2_185.1
ITIH3_HUMAN
72.760739

WWGGQPLWITATK_772.4_373.2
ENPP2_HUMAN
72.51936706

FGFGGSTDSGPIR_649.3_946.5
ADA12_HUMAN
72.49183198

GLQYAAQEGLLALQSELLR_1037.1_929.5
LBP_HUMAN
67.17588648

HFQNLGK_422.2_527.2
AFAM_HUMAN
66.11702719

YSHYNER_323.5_581.3
HABP2_HUMAN
65.56238612

ISQGEADINIAFYQR_575.6_684.4
MMP8_HUMAN
65.50301246

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
64.85259525

NIQSVNVK_451.3_674.4
GROA_HUMAN
64.53010225

DALSSVQESQVAQQAR_573.0_672.4
APOC3_HUMAN
64.12149927

SLLQPNK_400.2_599.4
CO8A_HUMAN
62.68167847

SFRPFVPR_335.9_635.3
LBP_HUMAN
61.90157662

NNQLVAGYLQGPNVNLEEK_700.7_999.5
IL1RA_HUMAN
61.54435815

LYYGDDEK_501.7_563.2
CO8A_HUMAN
60.16700473

SWNEPLYHLVTEVR_581.6_716.4
PRL_HUMAN
59.78209065

SGVDLADSNQK_567.3_662.3
VGFR3_HUMAN
58.93982896

GTYLYNDCPGPGQDTDCR_697.0_335.2
TNR1A_HUMAN
58.72963941

HATLSLSIPR_365.6_472.3
VGFR3_HUMAN
57.98669834

FIVGFTR_420.2_261.2
CCL20_HUMAN
57.23165578

QNYHQDSEAAINR_515.9_544.3
FRIH_HUMAN
57.21116697

DVLLLVHNLPQNLPGYFWYK_810.4_594.3
PSG9_HUMAN
56.84150484

FLNWIK_410.7_561.3
HABP2_HUMAN
56.37258274

SLQAFVAVAAR_566.8_487.3
IL23A_HUMAN
56.09012981

HFQNLGK_422.2_285.1
AFAM_HUMAN
56.04480022

GPGEDFR_389.2_322.2
PTGDS_HUMAN
55.7583763

NKPGVYTDVAYYLAWIR_677.0_821.5
FA12_HUMAN
55.53857645

LIQDAVTGLTVNGQITGDK_972.0_640.4
ITIH3_HUMAN
55.52577583

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
54.27147366

TLNAYDHR_330.5_312.2
PAR3_HUMAN
54.19190934

IQTHSTTYR_369.5_627.3
F13B_HUMAN
54.18950583

TASDFITK_441.7_710.4
GELS_HUMAN
54.1056456

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
53.8997252

DADPDTFFAK_563.8_302.1
AFAM_HUMAN
53.85914848

SVSLPSLDPASAK_636.4_473.3
APOB_HUMAN
53.41996191

TTSDGGYSFK_531.7_860.4
INHA_HUMAN
52.24655536

AFTECCVVASQLR_770.9_574.3
CO5_HUMAN
51.67853429

ELPQSIVYK_538.8_409.2
FBLN3_HUMAN
51.35853002

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
51.23842124

FQLSETNR_497.8_605.3
PSG2_HUMAN
51.01576848

GSLVQASEANLQAAQDFVR_668.7_806.4
ITIH1_HUMAN
50.81923338

FSLVSGWGQLLDR_493.3_403.2
FA7_HUMAN
50.54425114

ECEELEEK_533.2_405.2
IL15_HUMAN
50.41977421

NADYSYSVWK_616.8_769.4
CO5_HUMAN
50.36434595

SLLQPNK_400.2_358.2
CO8A_HUMAN
49.75593162

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
49.43389721

DISEVVTPR_508.3_787.4
CFAB_HUMAN
49.00234897

AEVIWTSSDHQVLSGK_586.3_300.2
PD1L1_HUMAN
48.79028835

SGVDLADSNQK_567.3_591.3
VGFR3_HUMAN
48.70665587

SILFLGK_389.2_201.1
THBG_HUMAN
48.5997957

AVLHIGEK_289.5_292.2
THBG_HUMAN
48.4605866

QLYGDTGVLGR_589.8_501.3
CO8G_HUMAN
48.11414904

FSLVSGWGQLLDR_493.3_516.3
FA7_HUMAN
47.59635333

DSPVLIDFFEDTER_841.9_399.2
HRG_HUMAN
46.83840473

INPASLDK_429.2_630.4
C163A_HUMAN
46.78947931

GAVHVVVAETDYQSFAVLYLER_822.8_863.5
CO8G_HUMAN
46.66185339

FLQEQGHR_338.8_497.3
CO8G_HUMAN
46.64415952

LNIGYIEDLK_589.3_837.4
PAI2_HUMAN
46.5879123

LSSPAVITDK_515.8_743.4
PLMN_HUMAN
46.2857838

GLQYAAQEGLLALQSELLR_1037.1_858.5
LBP_HUMAN
45.7427767

SDGAKPGPR_442.7_213.6
COLI_HUMAN
45.27828366

GYQELLEK_490.3_502.3
FETA_HUMAN
43.52928868

GGEGTGYFVDFSVR_745.9_869.5
HRG_HUMAN
43.24514327

ADLFYDVEALDLESPK_913.0_447.2
HRG_HUMAN
42.56268679

ADLFYDVEALDLESPK_913.0_331.2
HRG_HUMAN
42.48967422

EAQLPVIENK_570.8_699.4
PLMN_HUMAN
42.21213429

SILFLGK_389.2_577.4
THBG_HUMAN
42.03379581

HTLNQIDEVK_598.8_958.5
FETUA_HUMAN
41.98377176

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
41.89547273

FLPCENK_454.2_390.2
IL10_HUMAN
41.66612478

LIEIANHVDK_384.6_498.3
ADA12_HUMAN
41.50878046

DEIPHNDIALLK_459.9_510.8
HABP2_HUMAN
41.27830935

SLQAFVAVAAR_566.8_804.5
IL23A_HUMAN
41.00430596

YISPDQLADLYK_713.4_277.2
ENOA_HUMAN
40.90053801

SLPVSDSVLSGFEQR_810.9_836.4
CO8G_HUMAN
40.62020941

DGSPDVTTADIGANTPDATK_973.5_531.3
PGRP2_HUMAN
40.33913091

NTGVISVVTTGLDR_716.4_662.4
CADH1_HUMAN
40.05291612

ALVLELAK_428.8_672.4
INHBE_HUMAN
40.01646465

YEFLNGR_449.7_293.1
PLMN_HUMAN
39.83344278

WGAAPYR_410.7_577.3
PGRP2_HUMAN
39.52766213

TFLTVYWTPER_706.9_401.2
ICAM1_HUMAN
39.13662034

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
38.77511119

VGVISFAQK_474.8_693.4
TFR2_HUMAN
38.5823457

IIEVEEEQEDPYLNDR_996.0_777.4
FBLN1_HUMAN
38.30913304

TGYYFDGISR_589.8_694.4
FBLN1_HUMAN
38.30617106

LQGTLPVEAR_542.3_571.3
CO5_HUMAN
37.93064544

DSPVLIDFFEDTER_841.9_512.3
HRG_HUMAN
37.4447737

AALAAFNAQNNGSNFQLEEISR_789.1_746.4
FETUA_HUMAN
37.02483715

DGSPDVTTADIGANTPDATK_973.5_844.4
PGRP2_HUMAN
36.59864788

ILILPSVTR_506.3_785.5
PSGx_HUMAN
36.43814815

SVSLPSLDPASAK_636.4_885.5
APOB_HUMAN
36.27689491

TLAFVR_353.7_492.3
FA7_HUMAN
36.18771771

VAPGVANPGTPLA_582.3_555.3
A6NIT4_HUMAN
35.70677357

HELTDEELQSLFTNFANVVDK_817.1_906.5
AFAM_HUMAN
35.14441609

AGLLRPDYALLGHR_518.0_369.2
PGRP2_HUMAN
35.13047098

GDTYPAELYITGSILR_885.0_1332.8
F13B_HUMAN
34.97832404

LFIPQITR_494.3_727.4
PSG9_HUMAN
34.76811249

GYQELLEK_490.3_631.4
FETA_HUMAN
34.76117605

VSEADSSNADWVTK_754.9_533.3
CFAB_HUMAN
34.49787512

LNIGYIEDLK_589.3_950.5
PAI2_HUMAN
34.48448691

SFRPFVPR_335.9_272.2
LBP_HUMAN
34.27529415

ILDGGNK_358.7_490.2
CXCL5_HUMAN
34.2331388

EANQSTLENFLER_775.9_678.4
IL4_HUMAN
34.14295797

DFNQFSSGEK_386.8_189.1
FETA_HUMAN
34.05459951

IEEIAAK_387.2_660.4
CO5_HUMAN
33.93778148

TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3
ENPP2_HUMAN
33.87864446

LPATEKPVLLSK_432.6_347.2
HYOU1_HUMAN
33.69005522

FLQEQGHR_338.8_369.2
CO8G_HUMAN
33.61179024

APLTKPLK_289.9_357.2
CRP_HUMAN
33.59900279

YSHYNER_323.5_418.2
HABP2_HUMAN
33.50888447

TSYQVYSK_488.2_787.4
C163A_HUMAN
33.11650018

IALGGLLFPASNLR_481.3_657.4
SHBG_HUMAN
33.02974341

TGISPLALIK_506.8_741.5
APOB_HUMAN
32.64471573

LYYGDDEK_501.7_726.3
CO8A_HUMAN
32.60782458

IVLSLDVPIGLLQILLEQAR_735.1_503.3
UCN2_HUMAN
32.37907686

EAQLPVIENK_570.8_329.2
PLMN_HUMAN
32.34049256

TGYYFDGISR_589.8_857.4
FBLN1_HUMAN
32.14526507

VGVISFAQK_474.8_580.3
TFR2_HUMAN
32.11753213

FQSVFTVTR_542.8_623.4
C1QC_HUMAN
32.11360444

TSDQIHFFFAK_447.6_659.4
ANT3_HUMAN
31.95867038

IAPQLSTEELVSLGEK_857.5_333.2
AFAM_HUMAN
31.81531364

EVFSKPISWEELLQ_852.9_260.2
FA40A_HUMAN
31.36698726

DEIPHNDIALLK_459.9_260.2
HABP2_HUMAN
31.1839869

NYFTSVAHPNLFIATK_608.3_319.2
ILIA_HUMAN
31.09867061

ITENDIQIALDDAK_779.9_632.3
APOB_HUMAN
30.77026845

DTYVSSFPR_357.8_272.2
TCEA1_HUMAN
30.67784731

TDAPDLPEENQAR_728.3_843.4
CO5_HUMAN
30.66251941

LFYADHPFIFLVR_546.6_647.4
SERPH_HUMAN
30.65831566

TEQAAVAR_423.2_487.3
FA12_HUMAN
30.44356842

AVGYLITGYQR_620.8_737.4
PZP_HUMAN
30.36425528

HSHESQDLR_370.2_288.2
HRG_HUMAN
30.34684703

IALGGLLFPASNLR_481.3_412.3
SHBG_HUMAN
30.34101643

IAQYYYTFK_598.8_884.4
F13B_HUMAN
30.23453833

SLPVSDSVLSGFEQR_810.9_723.3
CO8G_HUMAN
30.11396489

IIGGSDADIK_494.8_762.4
C1S_HUMAN
30.06572687

QTLSWTVTPK_580.8_545.3
PZP_HUMAN
30.04139865

HYFIAAVER_553.3_658.4
FA8_HUMAN
29.80239884

QVCADPSEEWVQK_788.4_374.2
CCL3_HUMAN
29.61435573

DLHLSDVFLK_396.2_366.2
CO6_HUMAN
29.60077507

NIQSVNVK_451.3_546.3
GROA_HUMAN
29.47619619

QTLSWTVTPK_580.8_818.4
PZP_HUMAN
29.40047934

HSHESQDLR_370.2_403.2
HRG_HUMAN
29.32242262

LLEVPEGR_456.8_356.2
C1S_HUMAN
29.14169137

LIENGYFHPVK_439.6_343.2
F13B_HUMAN
28.63056809

EDTPNSVWEPAK_686.8_630.3
C1S_HUMAN
28.61352686

AFTECCVVASQLR_770.9_673.4
CO5_HUMAN
28.57830281

VNHVTLSQPK_374.9_459.3
B2MG_HUMAN
28.27203693

VSFSSPLVAISGVALR_802.0_715.4
PAPP1_HUMAN
28.13008712

DPDQTDGLGLSYLSSHIANVER_796.4_456.2
GELS_HUMAN
28.06549895

VVGGLVALR_442.3_784.5
FA12_HUMAN
28.00684006

NEIVFPAGILQAPFYTR_968.5_357.2
ECE1_HUMAN
27.97758456

QVCADPSEEWVQK_788.4_275.2
CCL3_HUMAN
27.94276837

LQDAGVYR_461.2_680.3
PD1L1_HUMAN
27.88063261

IQTHSTTYR_369.5_540.3
F13B_HUMAN
27.68873826

TPSAAYLWVGTGASEAEK_919.5_849.4
GELS_HUMAN
27.66889639

ALALPPLGLAPLLNLWAKPQGR_770.5_256.2
SHBG_HUMAN
27.63105727

ALQDQLVLVAAK_634.9_289.2
ANGT_HUMAN
27.63097319

IEEIAAK_387.2_531.3
CO5_HUMAN
27.52427934

TAVTANLDIR_537.3_288.2
CHL1_HUMAN
27.44246841

VSEADSSNADWVTK_754.9_347.2
CFAB_HUMAN
27.43976782

ITENDIQIALDDAK_779.9_873.5
APOB_HUMAN
27.39263522

SSNNPHSPIVEEFQVPYNK_729.4_521.3
C15_HUMAN
27.34493617

HPWIVHWDQLPQYQLNR_744.0_918.5
K56A3_HUMAN
27.19681613

TPSAAYLWVGTGASEAEK_919.5_428.2
GELS_HUMAN
27.17319953

AFLEVNEEGSEAAASTAVVIAGR_764.4_614.4
ANT3_HUMAN
27.10487351

WGAAPYR_410.7_634.3
PGRP2_HUMAN
27.09930054

IEVNESGTVASSSTAVIVSAR_693.0_545.3
PAI1_HUMAN
27.02567296

AEAQAQYSAAVAK_654.3_908.5
ITIH4_HUMAN
26.98305259

VPLALFALNR_557.3_917.6
PEPD_HUMAN
26.96988826

TLEAQLTPR_514.8_685.4
HEP2_HUMAN
26.94672621

QALEEFQK_496.8_551.3
CO8B_HUMAN
26.67037155

WNFAYWAAHQPWSR_607.3_545.3
PRG2_HUMAN
26.62600679

IYLQPGR_423.7_570.3
ITIH2_HUMAN
26.58752589

FFQYDTWK_567.8_840.4
IGF2_HUMAN
26.39942037

NEIWYR_440.7_357.2
FA12_HUMAN
26.35177282

GGEGTGYFVDFSVR_745.9_722.4
HRG_HUMAN
26.31688167

VGEYSLYIGR_578.8_708.4
SAMP_HUMAN
26.17367498

TAHISGLPPSTDFIVYLSGLAPSIR_871.5_800.5
TENA_HUMAN
26.13688183

GVTGYFTFNLYLK_508.3_260.2
PSG5_HUMAN
26.06007032

DYWSTVK_449.7_620.3
APOC3_HUMAN
26.03765187

YENYTSSFFIR_713.8_756.4
IL12B_HUMAN
25.9096605

YGLVTYATYPK_638.3_334.2
CFAB_HUMAN
25.84440452

LFIPQITR_494.3_614.4
PSG9_HUMAN
25.78081129

YEFLNGR_449.7_606.3
PLMN_HUMAN
25.17159874

SEPRPGVLLR_375.2_454.3
FA7_HUMAN
25.16444381

NSDQEIDFK_548.3_294.2
S10A5_HUMAN
25.12266401

YEVQGEVFTKPQLWP_911.0_293.1
CRP_HUMAN
24.77595195

GVTGYFTFNLYLK_508.3_683.9
PSG5_HUMAN
24.75289081

ISLLLIESWLEPVR_834.5_371.2
CSH_HUMAN
24.72379326

ALLLGWVPTR_563.3_373.2
PAR4_HUMAN
24.68096599

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
24.53420489

SGAQATWTELPWPHEK_613.3_793.4
HEMO_HUMAN
24.25610995

AQPVQVAEGSEPDGFWEALGGK_758.0_623.4
GELS_HUMAN
24.18769142

DLPHITVDR_533.3_490.3
MMP7_HUMAN
24.02606052

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
24.00163743

AVGYLITGYQR_620.8_523.3
PZP_HUMAN
23.93958524

GFQALGDAADIR_617.3_717.4
TIMP1_HUMAN
23.69249513

YEVQGEVFTKPQLWP_911.0_392.2
CRP_HUMAN
23.67764212

SDGAKPGPR_442.7_459.2
COLI_HUMAN
23.63551614

GFQALGDAADIR_617.3_288.2
TIMP1_HUMAN
23.55832742

IAPQLSTEELVSLGEK_857.5_533.3
AFAM_HUMAN
23.38139357

DTDTGALLFIGK_625.8_217.1
PEDF_HUMAN
23.33375418

LHEAFSPVSYQHDLALLR_699.4_380.2
FA12_HUMAN
23.27455931

IYLQPGR_423.7_329.2
ITIH2_HUMAN
23.19122626

TABLE 38

Random Forest 32 Middle Window

Variable
UniProt_ID
MeanDecreaseGini

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
2.27812193

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
2.080133179

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
1.952233942

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
1.518833357

VEHSDLSFSK_383.5_234.1
B2MG_HUMAN
1.482593086

VFQFLEK_455.8_811.4
CO5_HUMAN
1.448810425

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
1.389922815

YGIEEHGK_311.5_599.3
CXA1_HUMAN
1.386794676

TLAFVR_353.7_492.3
FA7_HUMAN
1.371530925

VLEPTLK_400.3_587.3
VTDB_HUMAN
1.368583173

VLEPTLK_400.3_458.3
VTDB_HUMAN
1.336029064

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
1.307024357

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
1.282930911

LHEAFSPVSYQHDLALLR_699.4_251.2
FA12_HUMAN
1.25362163

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
1.205539225

VEHSDLSFSK_383.5_468.2
B2MG_HUMAN
1.201047302

SLDFTELDVAAEK_719.4_316.2
ANGT_HUMAN
1.189617326

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
1.120706696

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
1.107036657

VNHVTLSQPK_374.9_459.3
B2MG_HUMAN
1.083264902

IEEIAAK_387.2_660.4
CO5_HUMAN
1.043635292

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.962643698

TLLPVSKPEIR_418.3_514.3
CO5_HUMAN
0.933440467

TEQAAVAR_423.2_615.4
FA12_HUMAN
0.878933553

DLHLSDVFLK_396.2_260.2
CO6_HUMAN
0.816855601

ALQDQLVLVAAK_634.9_289.2
ANGT_HUMAN
0.812620232

SLQAFVAVAAR_566.8_804.5
IL23A_HUMAN
0.792274782

QGHNSVFLIK_381.6_260.2
HEMO_HUMAN
0.770830031

ALQDQLVLVAAK_634.9_956.6
ANGT_HUMAN
0.767468246

SLDFTELDVAAEK_719.4_874.5
ANGT_HUMAN
0.745827911

TABLE 39

Random Forest 100 Middle Window

Variable
UniProt_ID
MeanDecreaseGini

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
1.241568411

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
0.903126414

LLAPSDSPEWLSFDVTGVVR_730._1430.3
TGFB1_HUMAN
0.846216563

ANLINNIFELAGLGK_793.9_299.2
LCAP_HUMAN
0.748261193

VFQFLEK_455.8_811.4
CO5_HUMAN
0.717545171

VEHSDLSFSK_383.5_234.1
B2MG_HUMAN
0.683219617

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
0.671091545

LNIGYIEDLK_589.3_950.5
PAI2_HUMAN
0.652293621

VLEPTLK_400.3_587.3
VTDB_HUMAN
0.627095631

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
0.625773888

VLEPTLK_400.3_458.3
VTDB_HUMAN
0.613655529

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
0.576305627

TLFIFGVTK_513.3_811.5
PSG4_HUMAN
0.574056825

YGIEEHGK_311.5_599.3
CXA1_HUMAN
0.570270447

VPLALFALNR_557.3_620.4
PEPD_HUMAN
0.556087614

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
0.531461012

VEHSDLSFSK_383.5_468.2
B2MG_HUMAN
0.531214597

TLAFVR_353.7_492.3
FA7_HUMAN
0.53070743

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
0.521633041

SEYGAALAWEK__612.8_845.5
CO6_HUMAN
0.514509661

SLDFTELDVAAEK_719.4_316.2
ANGT_HUMAN
0.50489698

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
0.4824926

LHEAFSPVSYQHDLALLR_699.4_251.2
FA12_HUMAN
0.48217238

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.472286273

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
0.470892051

FSLVSGWGQLLDR_493.3_403.2
FA7_HUMAN
0.465839813

GEVTYTTSQVSK_650.3_750.4
EGLN_HUMAN
0.458736205

VNHVTLSQPK_374.9_459.3
B2MG_HUMAN
0.454348892

HFQNLGK_422.2_527.2
AFAM_HUMAN
0.45127405

YGIEEHGK_311.5_341.2
CXA1_HUMAN
0.430641646

TABLE 40

Random Forest Protein Middle Window

Variable
UniProt_ID
MeanDecreaseGini

SEYGAALAWEK_
CO6_HUMAN
2.09649626

612.8_788.4

LLAPSDSPEWLSF
TGFB1_HUMAN
1.27664656

DVTGVVR_

730.1_430.3

VFQFLEK_
CO5_HUMAN
1.243884833

455.8_811.4

ANLINNIFELAG
LCAP_HUMAN
1.231814882

LGK_

793.9_299.2

VEHSDLSFSK_
B2MG_HUMAN
1.188808078

383.5_234.1

ELPQSIVYK_
FBLN3_HUMAN
1.185075445

538.8_417.7

LNIGYIEDLK_
PAI2_HUMAN
1.122351536

589.3_950.5

VLEPTLK_
VTDB_HUMAN
1.062664798

400.3_458.3

VPLALFALNR_
PEPD_HUMAN
1.019466776

557.3_620.4

TLAFVR_
FA7_HUMAN
0.98797064

353.7_492.3

TLFIFGVTK_
PSG4_HUMAN
0.980159531

513.3_811.5

AQPVQVAEGSEP
GELS_HUMAN
0.960286027

DGFWEALGGK

758.0_574.3

DALSSVQESQVA
APOC3_HUMAN
0.947091926

QQAR_

573.0_502.3

YGIEEHGK_
CXA1_HUMAN
0.946937719

311.5_599.3

EVFSKPISWEELLQ_
FA40A_HUMAN
0.916262164

852.9_376.2

LHEAFSPVSYQ
FA12_HUMAN
0.891310053

HDLALLR_

699.4_251.2

SLDFTELDVAAEK_
ANGT_HUMAN
0.884498494

719.4_316.2

TYLHTYESEI_
ENPP2_HUMAN
0.869043942

628.3_515.3

HFQNLGK_
AFAM_HUMAN
0.865435217

422.2_527.2

AVDIPGLEAATPYR_
TENA_HUMAN
0.844842109

736.9_399.2

TLNAYDHR_
PAR3_HUMAN
0.792615068

330.5_312.2

DVLLLVHNLPQNL
PSG7_HUMAN
0.763629346

TGHIWYK_

791.8_310.2

GPITSAAELNDPQ
EGLN_HUMAN
0.762305265

SILLR_

632.4_826.5

VVLSSGSGPGLDL
SHBG_HUMAN
0.706312721

PLVLGLPLQLK_

791.5_598.4

SLQNASAIESILK_
IL3_HUMAN
0.645503581

687.4_860.5

HYINLITR_
NPY_HUMAN
0.62631682

515.3_301.1

VELAPLPSWQPVGK_
ICAM1_HUMAN
0.608991877

760.9_342.2

LQVNTPLVGASLLR_
BPIA1_HUMAN
0.607801279

741.0_925.6

TLEAQLTPR_
HEP2_HUMAN
0.597771074

514.8_814.4

SDGAKPGPR_
COLI_HUMAN
0.582773073

442.7_459.2

TABLE 41

Random Forest All Middle Window

Variable
UniProt ID
MeanDecreaseGini

SEYGAALAWEK_
CO6_HUMAN
0.493373282

612.8_788.4

ALNHLPLEYNSAL
CO6_HUMAN
0.382180772

YSR_

621.0_696.4

VFQFLEK_
CO5_HUMAN
0.260292083

455.8_811.4

LLAPSDSPEWLSFD
TGFB1_HUMAN
0.243156718

VTGWR_

730.1_430.3

NADYSYSVWK_
CO5_HUMAN
0.242388196

616.8_769.4

VLEPTLK_
VTDB_HUMAN
0.238171849

400.3_458.3

VEHSDLSFSK_
B2MG_HUMAN
0.236873731

383.5_234.1

ELPQSIVYK_
FBLN3_HUMAN
0.224727161

538.8_417.7

VLEPTLK_
VTDB_HUMAN
0.222105614

400.3_587.3

TLFIFGVTK_
PSG4_HUMAN
0.210807574

513.3_811.5

ANLINNIFELAGLGK_
LCAP_HUMAN
0.208714978

793.9_299.2

LNIGYIEDLK_
PAI2_HUMAN
0.208027555

589.3_950.5

SEYGAALAWEK_
CO6_HUMAN
0.197362212

612.8_845.5

VNHVTLSQPK_
B2MG_HUMAN
0.195728091

374.9_244.2

YGIEEHGK_
CXA1_HUMAN
0.189969499

311.5_599.3

HFQNLGK_
AFAM_HUMAN
0.189572857

422.2_527.2

AGITIPR_
IL17_HUMAN
0.188351054

364.2_486.3

AQPVQVAEGSE
GELS_HUMAN
0.185069517

PDGFWEALGGK_

758.0_574.3

SLDFTELDVAAEK_
ANGT_HUMAN
0.173688295

719.4_316.2

TLAFVR_
FA7_HUMAN
0.170636045

353.7_492.3

SEPRPGVLLR_
FA7_HUMAN
0.170608352

375.2_654.4

TLLIANETLR_
IL5_HUMAN
0.16745571

572.3_703.4

ALNHLPLEYNS
CO6_HUMAN
0.161514946

ALYSR_

621.0_538.3

LHEAFSPVSYQ
FA12_HUMAN
0.15852146

HDLALLR_

699.4_251.2

DGSPDVTTADI
PGRP2_HUMAN
0.154028378

GANTPDATK_

973.5_844.4

VPLALFALNR_
PEPD_HUMAN
0.153725879

557.3_620.4

AVDIPGLEAATPYR_
TENA_HUMAN
0.150920884

736.9_399.2

YGIEEHGK_
CXA1_HUMAN
0.150319671

311.5_341.2

FSLVSGWGQLLDR_
FA7_HUMAN
0.144781622

493.3_403.2

IEEIAAK_
CO5_HUMAN
0.141983196

387.2_660.4

TABLE 42

Random Forest 32 Middle-Late Window

Variable
UniProt_ID
MeanDecreaseGini

VPLALFALNR_
PEPD_HUMAN
4.566619475

557.3_620.4

VFQFLEK_
CO5_HUMAN
3.062474666

455.8_811.4

AQPVQVAEGSEP
GELS_HUMAN
3.033740627

DGFWEALGGK_

758.0_574.3

LIEIANHVDK_
ADA12_HUMAN
2.825082394

384.6_498.3

DALSSVQESQVA
APOC3_HUMAN
2.787777983

QQAR_

573.0_502.3

TLAFVR_
FA7_HUMAN
2.730532075

353.7_492.3

ALNHLPLEYNSA
CO6_HUMAN
2.671290375

LYSR_

621.0_696.4

AVYEAVLR_
PEPD_HUMAN
2.621357053

460.8_587.4

SEPRPGVLLR_
FA7_HUMAN
2.57568964

375.2_654.4

TYLHTYESEI_
ENPP2_HUMAN
2.516708906

628.3_515.3

ALNHLPLEYNS
CO6_HUMAN
2.497348374

ALYSR_

621.0_538.3

LIEIANHVDK_
ADA12_HUMAN
2.457401462

384.6_683.4

YGIEEHGK_
CXA1_HUMAN
2.396824268

311.5_599.3

VLEPTLK_
VTDB_HUMAN
2.388105564

400.3_587.3

SEYGAALAWEK_
CO6_HUMAN
2.340473883

612.8_788.4

WSAGLTSSQVD
CBG_HUMAN
2.332007976

LYIPK_

883.0_515.3

FGFGGSTDSGPIR_
ADA12_HUMAN
2.325669514

649.3_946.5

SEYGAALAWEK_
CO6_HUMAN
2.31761671

612.8_845.5

QINSYVK_
CBG_HUMAN
2.245221163

426.2_496.3

QINSYVK_
CBG_HUMAN
2.212307699

426.2_610.3

TEQAAVAR_
FA12_HUMAN
2.105860336

423.2_615.4

AVYEAVLR_
PEPD_HUMAN
2.098321893

460.8_750.4

TEQAAVAR_
FA12_HUMAN
2.062684763

423.2_487.3

DFNQFSSGEK_
FETA_HUMAN
2.05160689

386.8_333.2

SLQAFVAVAAR_
IL23A_HUMAN
1.989521006

566.8_804.5

SLDFTELDVAAEK_
ANGT_HUMAN
1.820628782

719.4_316.2

DPTFIPAPIQAK_
ANGT_HUMAN
1.763514326

433.2_556.3

DPTFIPAPIQAK_
ANGT_HUMAN
1.760870392

433.2_461.2

VLEPTLK_
VTDB_HUMAN
1.723389354

400.3_458.3

YENYTSSFFIR_
IL12B_HUMAN
1.63355187

713.8_756.4

TABLE 43

Random Forest 100 Middle-Late Window

Variable
UniProt_ID
MeanDecreaseGini

VPLALFALNR_
PEPD_HUMAN
1.995805024

557.3_620.4

VFQFLEK_
CO5_HUMAN
1.235926416

455.8_811.4

DALSSVQESQVAQQAR_
APOC3_HUMAN
1.187464899

573.0_502.3

EVFSKPISWEELLQ_
FA40A_HUMAN
1.166642578

852.9_376.2

AQPVQVAEGSEPDGFW
GELS_HUMAN
1.146077071

EALGGK_

758.0_574.3

TLAFVR_
FA7_HUMAN
1.143038275

353.7_492.3

ANLINNIFELAGLGK_
LCAP_HUMAN
1.130656591

793.9_299.2

ALNHLPLEYNSALYSR_
CO6_HUMAN
1.098305298

621.0_538.3

ELPQSIVYK_
FBLN3_HUMAN
1.096715712

538.8_417.7

LLAPSDSPEWLSFDV
TGFB1_HUMAN
1.086171713

TGWR_

730.1_430.3

YGIEEHGK_
CXA1_HUMAN
1.071880823

311.5_341.2

ALNHLPLEYNSALY
CO6_HUMAN
1.062278869

SR_

621.0_696.4

TQILEWAAER_
EGLN_HUMAN
1.059019017

608.8_761.4

AVYEAVLR_
PEPD_HUMAN
1.057920661

460.8_587.4

AEIEYLEK_
LYAM1_HUMAN
1.038388955

497.8_552.3

SEPRPGVLLR_
FA7_HUMAN
1.028275728

375.2_654.4

AVDIPGLEAATPYR_
TENA_HUMAN
1.026032369

736.9_399.2

LIEIANHVDK_
ADA12_HUMAN
1.015065282

384.6_498.3

YGIEEHGK_
CXA1_HUMAN
0.98667651

311.5_599.3

VLEPTLK_
VTDB_HUMAN
0.970330675

400.3_587.3

DVLLLVHNLPQNLT
PSG7_HUMAN
0.934747674

GHIWYK_

791.8_883.0

TAHISGLPPSTDFI
TENA_HUMAN
0.889111923

VYLSGLAPSIR_

871.5_472.3

TLNAYDHR_
PAR3_HUMAN
0.887605636

330.5_312.2

FGFGGSTDSGPIR_
ADA12_HUMAN
0.884305889

649.3_946.5

LIEIANHVDK_
ADA12_HUMAN
0.880889836

384.6_683.4

SEYGAALAWEK_
CO6_HUMAN
0.863585472

612.8_788.4

TYLHTYESEI_
ENPP2_HUMAN
0.849232356

628.3_515.3

FGFGGSTDSGPIR_
ADA12_HUMAN
0.843334824

649.3_745.4

SEYGAALAWEK_
CO6_HUMAN
0.842319271

612.8_845.5

TPSAAYLWVGTGA
GELS_HUMAN
0.828959173

SEAEK_

919.5_849.4

TABLE 44

Random Forest Protein Middle-Late Window

Variable
UniProt_ID
MeanDecreaseGini

VPLALFALNR_
PEPD_HUMAN
3.202123047

557.3_620.4

ANUNNIFELAGLGK_
LCAP_HUMAN
2.100447309

793.9_299.2

VFQFLEK_
CO5_HUMAN
2.096157529

455.8_811.4

AQPVQVAEGSEP
GELS_HUMAN
2.052960939

DGFWEALGGK_

758.0_574.3

ALNHLPLEYNSAL
CO6_HUMAN
2.046139797

YSR_

621.0_696.4

TQILEWAAER_
EGLN_HUMAN
1.99287941

608.8_761.4

ELPQSIVYK_
FBLN3_HUMAN
1.920894959

538.8_417.7

TGVAVNKPAEFTV
FLNA_HUMAN
1.917665697

DAK_

549.6_258.1

SEPRPGVLLR_
FA7_HUMAN
1.883557705

375.2_654.4

DALSSVQESQVAQ
APOC3_HUMAN
1.870232155

QAR_

573.0_502.3

EVFSKPISWEELL
FA40A_HUMAN
1.869000136

Q_852.9_376.2

LIEIANHVDK_
ADA12_HUMAN
1.825457092

384.6_683.4

VLEPTLK_
VTDB_HUMAN
1.695327774

400.3_587.3

TEQAAVAR_
FA12_HUMAN
1.685013152

423.2_615.4

LLAPSDSPEWLS
TGFB1_HUMAN
1.684068039

FDVTGWR_

730.1_430.3

TLNAYDHR_
PAR3_HUMAN
1.673758239

330.5_312.2

AVDIPGLEAATP
TENA_HUMAN
1.648896853

YR_

736.9_399.2

DVLLLVHNLPQN
PSG7_HUMAN
1.648146088

LTGHIWYK_

791.8_883.0

AEIEYLEK_
LYAM1_HUMAN
1.645833005

497.8_552.3

TYLHTYESEI_
ENPP2_HUMAN
1.639121965

628.3_515.3

AGLLRPDYALLG
PGRP2_HUMAN
1.610227875

HR_

518.0_595.4

YGIEEHGK_
CXA1_HUMAN
1.606978339

311.5_599.3

QINSYVK_
CBG_HUMAN
1.554905578

426.2_496.3

LTTVDIVTLR_
IL2RB_HUMAN
1.484081016

565.8_815.5

AALAAFNAQNNGS
FETUA_HUMAN
1.43173022

NFQLEEISR_

789.1_746.4

AEVIWTSSDHQVL
PD1L1_HUMAN
1.394857397

SGK_

586.3_300.2

ALEQDLPVNIK_
CNDP1_HUMAN
1.393464547

620.4_570.4

DFNQFSSGEK_
FETA_HUMAN
1.374296237

386.8_333.2

TSYQVYSK_
C163A_HUMAN
1.36141387

488.2_787.4

TLEAQLTPR_
HEP2_HUMAN
1.311118611

514.8_685.4

TABLE 45

Random Forest All Middle-Late Window

Variable
UniProt_ID
MeanDecreaseGini

VPLALFALNR_
PEPD_HUMAN
0.685165163

557.3_620.4

VFQFLEK_
CO5_HUMAN
0.426827804

455.8_811.4

ALNHLPLEYNSA
CO6_HUMAN
0.409942379

LYSR_

621.0_538.3

YGIEEHGK_
CXA1_HUMAN
0.406589512

311.5_341.2

ALNHLPLEYNSA
CO6_HUMAN
0.402152062

LYSR_

621.0_696.4

AQPVQVAEGSEP
GELS_HUMAN
0.374861014

DGFWEALGGK_

758.0_574.3

ANLINNIFELAG
LCAP_HUMAN
0.367089422

LGK_

793.9_299.2

TQILEWAAER_
EGLN_HUMAN
0.353757524

608.8_761.4

AVYEAVLR_
PEPD_HUMAN
0.350518668

460.8_587.4

TLAFVR_
FA7_HUMAN
0.344669505

353.7_492.3

SEPRPGVLLR_
FA7_HUMAN
0.338752336

375.2_654.4

LIEIANHVDK_
ADA12_HUMAN
0.321850027

384.6_683.4

ELPQSIVYK_
FBLN3_HUMAN
0.301819017

538.8_417.7

EVFSKPISWEEL
FA40A_HUMAN
0.299561811

LQ_

852.9_376.2

LIEIANHVDK_
ADA12_HUMAN
0.298253589

384.6_498.3

VLEPTLK_
VTDB_HUMAN
0.296206088

400.3_587.3

YGIEEHGK_
CXA1_HUMAN
0.295621408

311.5_599.3

DVLLLVHNLPQN
PSG7_HUMAN
0.292937475

LTGHIWYK_

791.8_883.0

TYLHTYESEI_
ENPP2_HUMAN
0.275902848

628.3_515.3

DALSSVQESQVA
APOC3_HUMAN
0.275664578

QQAR_

573.0_502.3

FGFGGSTDSG
ADA12_HUMAN
0.27120436

PIR_

649.3_745.4

AVDIPGLEAAT
TENA_HUMAN
0.266568271

PYR_

736.9_399.2

TGVAVNKPAEFT
FLNA_HUMAN
0.262537889

VDAK_

549.6_258.1

TLNAYDHR_
PAR3_HUMAN
0.259901193

330.5_312.2

IYLQPGR_
ITIH2_HUMAN
0.259086112

423.7_329.2

AEVIWTSSDHQV
PD1L1_HUMAN
0.25722354

LSGK_

586.3_300.2

VPSHAVVAR_
TRFL_HUMAN
0.256151812

312.5_515.3

SEYGAALAWEK_
CO6_HUMAN
0.251704855

612.8_845.5

FGFGGSTDSGPIR_
ADA12_HUMAN
0.249400642

649.3_946.5

SEYGAALAWEK_
CO6_HUMAN
0.245930393

612.8_788.4

TABLE 46

Random Forest 32 Late Window

Variable
UniProt_D
MeanDecreaseGini

AVYEAVLR_
PEPD_HUMAN
1.889521223

460.8_587.4

AEIEYLEK_
LYAM1_HUMAN
1.75233545

497.8_552.3

AALAAFNAQNNGS
FETUA_HUMAN
1.676813493

NFQLEEISR_

789.1_746.4

TGVAVNKPAEFTV
FLNA_HUMAN
1.600684153

DAK_

549.6_258.1

AVYEAVLR_
PEPD_HUMAN
1.462889662

460.8_750.4

LIEIANHVDK_
ADA12_HUMAN
1.364115361

384.6_683.4

VPLALFALNR_
PEPD_HUMAN
1.324317148

557.3_620.4

QINSYVK_
CBG_HUMAN
1.305932064

426.2_610.3

ITQDAQLK_
CBG_HUMAN
1.263533228

458.8_702.4

FGFGGSTDSGPIR_
ADA12_HUMAN
1.245153376

649.3_745.4

LIEIANHVDK_
ADA12_HUMAN
1.236529173

384.6_498.3

QINSYVK_
CBG_HUMAN
1.221866266

426.2_496.3

YSHYNER_
HABP2_HUMAN
1.169575572

323.5_418.2

YYGYTGAFR_
TRFL_HUMAN
1.126684146

549.3_450.3

VGVISFAQK_
TFR2_HUMAN
1.075283855

474.8_580.3

VFQYIDLHQDEFV
CNDP1_HUMAN
1.07279097

QTLK_

708.4_375.2

SPEAEDPLGVER_
Z512B_HUMAN
1.05759256

649.8_314.1

DEIPHNDIALLK_
HABP2_HUMAN
1.028933332

459.9_510.8

ALEQDLPVNIK_
CNDP1_HUMAN
1.014443799

620.4_798.5

ALEQDLPVNIK_
CNDP1_HUMAN
1.010573267

620.4_570.4

ILDGGNK_
CXCL5_HUMAN
0.992175141

358.7_603.3

TSYQVYSK_
C163A_HUMAN
0.95649585

488.2_787.4

YENYTSSFFIR_
IL12B_HUMAN
0.955085198

713.8_756.4

SETEIHQGFQHL
CBG_HUMAN
0.944726739

HQLFAK_

717.4_447.2

TLPFSR_
LYAM1_HUMAN
0.944426109

360.7_506.3

VLSSIEQK_
1433S_HUMAN
0.933902495

452.3_691.4

AEIEYLEK_
LYAM1_HUMAN
0.891235263

497.8_389.2

GTYLYNDCPGPG
TNR1A_HUMAN
0.87187037

QDTDCR_

697.0_666.3

SGVDLADSNQK_
VGFR3_HUMAN
0.869821307

567.3_662.3

SGVDLADSNQK_
VGFR3_HUMAN
0.839946466

567.3_591.3

TABLE 47

Random Forest 100 Late Window

Variable
UniProt_ID
MeanDecreaseGini

AVYEAVLR_
PEPD_HUMAN
0.971695767

460.8_587.4

AEIEYLEK_
LYAM1_HUMAN
0.920098693

497.8_552.3

TGVAVNKPAEFTVDAK_
FLNA_HUMAN
0.786924487

549.6_258.1

AVYEAVLR_
PEPD_HUMAN
0.772867983

460.8_750.4

AALAAFNAQNNGSNFQ
FETUA_HUMAN
0.744138513

LEEISR_

789.1_746.4

AYSDLSR_
SAMP_HUMAN
0.736078079

406.2_375.2

VPLALFALNR_
PEPD_HUMAN
0.681784822

557.3_620.4

QINSYVK_
CBG_HUMAN
0.585819307

426.2_610.3

LIEIANHVDK_
ADA12_HUMAN
0.577161158

384.6_498.3

FGFGGSTDSGPIR_
ADA12_HUMAN
0.573055613

649.3_745.4

WSAGLTSSQVDLY
CBG_HUMAN
0.569156128

IPK_

883.0_515.3

ITQDAQLK_
CBG_HUMAN
0.551017844

458.8_702.4

LIEIANHVDK_
ADA12_HUMAN
0.539330047

384.6_683.4

YYGYTGAFR_
TRFL_HUMAN
0.527652175

549.3_450.3

VFQYIDLHQDEFV
CNDP1_HUMAN
0.484155289

QTLK_

708.4_375.2

FQLPGQK_
PSG1_HUMAN
0.480394031

409.2_429.2

AVDIPGLEAATPYR_
TENA_HUMAN
0.475252565

736.9_286.1

QINSYVK_
CBG_HUMAN
0.4728541

426.2_496.3

YISPDQLADLYK_
ENOAHUMAN
0.470079977

713.4_277.2

TLPFSR_
LYAM1_HUMAN
0.46881451

360.7_506.3

SPEAEDPLGVER_
Z512B_HUMAN
0.4658941

649.8_314.1

ALEQDLPVNIK_
CNDP1_HUMAN
0.463604174

620.4_798.5

YSHYNER_
HABP2_HUMAN
0.453076307

323.5_418.2

VGVISFAQK_
TFR2_HUMAN
0.437768219

474.8_580.3

LQDAGVYR_
PD1L1_HUMAN
0.428524689

461.2_680.3

AEIEYLEK_
LYAM1_HUMAN
0.42041448

497.8_389.2

TSYQVYSK_
C163A_HUMAN
0.419411932

488.2_787.4

SVVLIPLGAVDD
CNDP1_HUMAN
0.415325735

GEHSQNEK_

703.0_798.4

ALEQDLPVNIK_
CNDP1_HUMAN
0.407951733

620.4_570.4

ILDGGNK_
CXCL5_HUMAN
0.401059572

358.7_603.3

TABLE 48

Random Forest Protein Late Window

Variable
UniProt_D
MeanDecreaseGini

AVYEAVLR_
PEPD_HUMAN
1.836010146

460.8_587.4

AEIEYLEK_
LYAM1_HUMAN
1.739802548

497.8_552.3

AALAAFNAQNNG
FETUA_HUMAN
1.455337749

SNFQ

LEEISR_

789.1_746.4

TGVAVNKPAEFT
FLNA_HUMAN
1.395043941

VDAK_

549.6_258.1

AYSDLSR_
SAMP_HUMAN
1.177349958

406.2_375.2

LIEIANHVDK_
ADA12_HUMAN
1.14243936

384.6_683.4

QINSYVK_
CBG_HUMAN
1.05284482

426.2_496.3

ALEQDLPVNIK_
CNDP1_HUMAN
0.971678206

620.4_798.5

YISPDQLADLYK_
ENOA_HUMAN
0.902293734

713.4_277.2

AVDIPGLEAATP
TENA_HUMAN
0.893163413

YR_

736.9_286.1

SPEAEDPLGVER_
Z512B_HUMAN
0.856551531

649.8_314.1

ILDGGNK_
CXCL5_HUMAN
0.841485153

358.7_603.3

VGVISFAQK_
TFR2_HUMAN
0.835256078

474.8_580.3

YYGYTGAFR_
TRFL_HUMAN
0.831195917

549.3_450.3

YSHYNER_
HABP2_HUMAN
0.814479968

323.5_418.2

FQLPGQK_
PSG1_HUMAN
0.77635168

409.2_276.1

YENYTSSFFIR_
IL12B_HUMAN
0.761241391

713.8_756.4

TEQAAVAR_
FA12_HUMAN
0.73195592

423.2_615.4

SGVDLADSNQK_
VGFR3_HUMAN
0.72504131

567.3_662.3

VLSSIEQK_
1433S_HUMAN
0.713380314

452.3_691.4

GTYLYNDCPGPGQ
TNR1A_HUMAN
0.704248586

DTDCR_

697.0_666.3

TSYQVYSK_
C163A_HUMAN
0.69026345

488.2_787.4

TLEAQLTPR_
HEP2_HUMAN
0.654641588

514.8_685.4

AEVIWTSSDHQV
PD1L1_HUMAN
0.634751081

LSGK_

586.3_300.2

TAVTANLDIR_
CHL1_HUMAN
0.619871203

537.3_288.2

ITENDIQIALDDAK_
APOB_HUMAN
0.606313398

779.9_632.3

TASDFITK_
GELS_HUMAN
0.593535076

441.7_781.4

SPQAFYR_
REL3_HUMAN
0.592004045

434.7_556.3

NHYTESISVAK_
NEUR1_HUMAN
0.588383911

624.8_415.2

LTTVDIVTLR_
IL2RB_HUMAN
0.587343951

565.8_815.5

Random Forest All Late Window

Variable
UniProt_ID
MeanDecreaseGini

AVYEAVLR_
PEPD_HUMAN
0.437300283

460.8_587.4

AEIEYLEK_
LYAM1_HUMAN
0.371624293

497.8_552.3

AALAAFNAQNNG
FETUA_HUMAN
0.304039734

SNFQLEEISR_

789.1_746.4

TGVAVNKPAEFT
FLNA_HUMAN
0.280588526

VDAK_

549.6_258.1

AVYEAVLR_
PEPD_HUMAN
0.266788699

460.8_750.4

AYSDLSR_
SAMP_HUMAN
0.247412666

406.2_375.2

VPLALFALNR_
PEPD_HUMAN
0.229955358

557.3_620.4

LIEIANHVDK_
ADA12_HUMAN
0.218186524

384.6_683.4

ITQDAQLK_
CBG_HUMAN
0.217646659

458.8_702.4

WSAGLTSSQVD_
CBG_HUMAN
0.213840705

LYIPK_

883.0_515.3

FGFGGSTDSGPIR_
ADA12_HUMAN
0.212794469

649.3_745.4

LIEIANHVDK_
ADA12_HUMAN
0.208620264

384.6_498.3

QINSYVK_
CBG_HUMAN
0.202054546

426.2_610.3

QINSYVK_
CBG_HUMAN
0.197235139

426.2_496.3

FQLPGQK_
PSG1_HUMAN
0.188311102

409.2_429.2

VFQYIDLHQDEFVQ
CNDP1_HUMAN
0.180534913

TLK_

708.4_375.2

ALEQDLPVNIK_
CNDP1_HUMAN
0.178464358

620.4_798.5

YYGYTGAFR_
TRFL_HUMAN
0.176050092

549.3_450.3

ALFLDALGPPAVTR_
INHA_HUMAN
0.171492975

720.9_640.4

FQLPGQK_
PSG1_HUMAN
0.167576198

409.2_276.1

SETEIHQGFQHL
CBG_HUMAN
0.162231844

HQLFAK_

717.4_447.2

ALEQDLPVNIK_
CNDP1_HUMAN
0.162165399

620.4_570.4

VPSHAVVAR_
TRFL_HUMAN
0.156742065

312.5_515.3

AVDIPGLEAATPYR_
TENA_HUMAN
0.153681405

736.9_286.1

FTFTLHLETPKPS
PSG1_HUMAN
0.152042057

ISSSNLNPR_

829.4_874.4

VGVISFAQK_
TFR2_HUMAN
0.149034355

474.8_580.3

TLPFSR_
LYAM1_HUMAN
0.143223501

360.7_506.3

SLDFTELDVAAEK_
ANGT_HUMAN
0.141216186

719.4_874.5

SPEAEDPLGVER_
Z512B_HUMAN
0.139843479

649.8_314.1

YGIEEHGK_
CXA1_HUMAN
0.135236953

311.5_341.2

TABLE 50

Selected Transitions for Early Window

Transition
Parent Protein

LIQDAVTGLTVNGQI
ITIH3_HUMAN

TGDK_

972.0_798.4

VQTAHFK_
CO8A_HUMAN

277.5_431.2

FLNWIK_
HABP2_HUMAN

410.7_560.3

ITGFLKPGK_
LBP_HUMAN

320.9_429.3

ALNHLPLEYNSALYSR_
CO6_HUMAN

621.0_538.3

TYLHTYESEI_
ENPP2_HUMAN

628.3_908.4

LIENGYFHPVK_
F13B_HUMAN

439.6_627.4

AVLH1GEK_
THBG_HUMAN

289.5_292.2

QALEEFQK_
CO8B_HUMAN

496.8_680.3

TEFLSNYLTNVDDITL
ENPP2_HUMAN

VPGTLGR_

846.8_600.3

TASDFITK_
GELS_HUMAN

441.7_781.4

LPNNVLQEK_
AFAM_HUMAN

527.8_844.5

AHYDLR_
FETUA_HUMAN

387.7_288.2

ITLPDFTGDLR_
LBP_HUMAN

624.3_288.2

IEGNLIFDPNNYLPK_
APOB_HUMAN

874.0_414.2

ITGFLKPGK_
LBP_HUMAN

320.9_301.2

FSVVYAK_
FETUA_HUMAN

407.2_381.2

ITGFLKPGK_
LBP_HUMAN

320.9_429.3

VFQFLEK_
CO5_HUMAN

455.8_811.4

LIQDAVTGLTVNGQI
ITIH3_HUMAN

TGDK_

972.0_798.4

DADPDTFFAK_
AFAM_HUMAN

563.8_825.4

TABLE 51

Selected Proteins for Early Window

Protein

complement component C6 precursor
CO6_HUMAN

inter-alpha-trypsin inhibitor heavy chain H3
ITIH3_HUMAN

preproprotein

Coagulation factor XIII B chain
F13B_HUMAN

Ectonucleotide pyrophosphatase/phosphodiesterase
ENPP2_HUMAN

family member 2

Complement component C8 beta chain
CO8B_HUMAN

thyroxine-binding globulin precursor
THBG_HUMAN

Hyaluronan-binding protein 2
HABP2_HUMAN

lipopolysaccharide-binding protein
LBP_HUMAN

Complement factor B
CFAB_HUMAN

Gelsolin
GELS_HUMAN

afamin precursor
AFAM_HUMAN

apolipoprotein B-100 precursor
APOB_HUMAN

complement component C5
CO5_HUMAN

Alpha-2-HS-glycoprotein
FETUA_HUMAN

complement component C8 gamma chain
CO8G_HUMAN

TABLE 52

Selected Transitions for Middle-Late Window

Transition
Patent Protein

VPLALFALNR_
PEPD_HUMAN

557.3_620.4

VFQFLEK_
CO5_HUMAN

455.8_811.4

AQPVQVAEGSEPDGF
GELS_HUMAN

WEALGGK_

758.0_574.3

LIEIANHVDK_
ADA12_HUMAN

384.6_498.3

TLAFVR_
FA7_HUMAN

353.7_492.3

ALNHLPLEYNSALYSR_
CO6_HUMAN

621.0_696.4

AVYEAVLR_
PEPD_HUMAN

460.8_587.4

SEPRPGVLLR_
FA7_HUMAN

375.2_654.4

TYLHTYESEI_
ENPP2_HUMAN

628.3_515.3

ALNHLPLEYNSALYSR_
CO6_HUMAN

621.0_538.3

TABLE 53

Selected Proteins for Middle-Late Window

Protein

Xaa-Pro dipeptidase
PEPD_HUMAN

Leucyl-cystinyl aminopeptidase
LCAP_HUMAN

complement component C5
CO5__HUMAN

Gelsolin
GELS_HUMAN

complement component C6 precursor
CO6_HUMAN

Endoglin precursor
EGLN_HUMAN

EGF-containing fibulin-like extracellular matrix
FBLN3_HUMAN

protein 1

coagulation factor VII isoform a
FA7_HUMAN

Disintegrin and metalloproteinase domain-containing
ADA12_HUMAN

protein 12

vitamin D-binding protein isoform 1 precursor
VTDB_HUMAN

coagulation factor XII precursor
FA12_HUMAN

Corticosteroid-binding globulin
CBG_HUMAN

Example 6. Study V to Further Refine Preterm Birth Biomarkers

A additional hypothesis-dependent discovery study was performed with a further refined scheduled MRM assay. Less robust transitions were again removed to improve analytical performance and make room for the inclusion of stable-isotope labeled standards (SIS) corresponding to 79 analytes of interest identified in previous studies. SIS peptides have identical amino acid sequence, chromatographic and MS fragmentation behaviour as their endogenous peptide counterparts, but differ in mass. Therefore they can be used to reduce LC-MS analytical variability and confirm analyte identity. Samples included approximately 60 spontaneous PTB cases (delivery at less than 37 weeks, 0 days), and 180 term controls (delivery at greater than or equal to 37 weeks, 0 days). Each case was designated a “matched” control to within one day of blood draw and two “random” controls matched to the same 3 week blood draw window (17-19, 20-22 or 23-25 weeks gestation). For the purposes of analysis these three blood draw windows were combined. Samples were processed essentially as described previously, except that in this study, tryptic digests were reconstituted in a solution containing SIS standards. Raw analyte peak areas were Box-Cox transformed, corrected for run order and batch effects by regression and used for univariate and multivariate statistical analyses. Univariate analysis included determination of p-values for adjusted peak areas for all analytes from t-tests considering cases vs controls defined as either deliveries at >37 weeks (Table 54) or deliveries at >40 weeks (Table 55). Univariate analysis also included the determination of p-values for a linear model that evaluates the dependence of each analyte's adjusted peak area on the time to birth (gestational age at birth minus the gestational age at blood draw) (Table 56) and the gestational age at birth (Table 57). Additionally raw peak area ratios were calculated for endogenous analytes and their corresponding SIS counterparts, Box-Cox transformed and then used for univariate and multivariate statistical analyses. The above univariate analysis was repeated for analyte/SIS peak area ratio values, summarized in Tables 58-61, respectively.

Multivariate random forest regression models were built using analyte values and clinical variables (e.g. Maternal age, (MAGE), Body mass index, (BMI)) to predict Gestational Age at Birth (GAB). The accuracy of the random forest was evaluated with respect to correlation of the predicted and actual GAB, and with respect to the mean absolute deviation (MAD) of the predicted from actual GAB. The accuracy was further evaluated by determining the area under the receiver operating characteristic curve (AUC) when using the predicted GAB as a quantitative variable to classify subjects as full term or pre-term. Random Forest Importance Values were fit to an Empirical Cumulative Distribution Function and probabilities (P) were calculated. We report the analytes by importance ranking (P>0.7) in the random forest models, using adjusted analyte peak area values (Table 62) and analyte/SIS peak area ratio values (Table 63).

The probability of pre-term birth, p(PTB), may be estimated using the predicted gestational age at birth (GAB) as follows. The estimate will be based on women enrolled in the Sera PAPR clinical trial, which provided the subjects used to develop the PTB prediction methods.

Among women with a predicted GAB of j days plus or minus k days, p(PTB) was estimated as the proportion of women in the PAPR clinical trial with a predicted GAB of j days plus or minus k days who actually deliver before 37 weeks gestational age.

More generally, for women with a predicted GAB of j days plus or minus k days, the probability that the actual gestational age at birth will be less than a specified gestational age, p(actual GAB<specified GAB), was estimated as the proportion of women in the PAPR clinical trial with a predicted GAB of j days plus or minus k days who actually deliver before the specified gestational age. FIG. 1 depicts a scatterplot of actual gestational age at birth versus predicted gestational age from random forest regression model. FIG. 2 shows the distribution of predicted gestational age from random forest regression model versus actual gestational age at birth (GAB), where actual GAB was given in categories of (i) less than 37 weeks, (ii) 37 to 39 weeks, and (iii) 40 weeks or greater.

TABLE 54

Univariate p-values for Ad_usted Peak Areas

(<37 vs >37 weeks)

Transition
Protein
pvalue

SPELQAEAK_
APOA2_HUMAN
0.00246566

486.8_659.4

ALALPPLGLAPLLNLW
SHBG_HUMAN
0.002623332

AKPQGR_

770.5_457.3

ALALPPLGLAPLLNLW
SHBG_HUMAN
0.002822593

AKPQGR_

770.5_256.2

SPELQAEAK_
APOA2_HUMAN
0.003183869

486.8_788.4

VVLSSGSGPGLDLPLVL
SHBG_HUMAN
0.004936049

GLPLQLK_

791.5_768.5

VVLSSGSGPGLDLPLVL
SHBG_HUMAN
0.005598977

GLPLQLK_

791.5_598.4

DYWSTVK_
APOC3_HUMAN
0.005680405

449.7_347.2

DYWSTVK_
APOC3_HUMAN
0.006288693

449.7_620.3

WGAAPYR_
PGRP2_HUMAN
0.006505238

410.7_634.3

DALSSVQESQVAQQAR_
APOC3_HUMAN
0.007626246

573.0_502.3

DALSSVQESQVAQQAR_
APOC3_HUMAN
0.008149335

573.0_672.4

LSIPQITTK_
PSG5_HUMAN
0.009943955

500.8_687.4

GWVTDGFSSLK_
APOC3_HUMAN
0.010175055

598.8_854.4

IALGGLLFPASNLR_
SHBG_HUMAN
0.010784167

481.3_657_4

AKPALEDLR_
APOA1_HUMAN
0.011331968

506.8_813.5

WGAAPYR_
PGRP2_HUMAN
0.011761088

410.7_577.3

VPLALFALNR_
PEPD_HUMAN
0.014050395

557.3_620.4

FSLVSGWGQLLDR_
FA7_HUMAN
0.014271151

493.3_447.3

LSIPQITTK_
PSG5_HUMAN
0.014339942

500.8_800.5

TLAFVR_
FA7_HUMAN
0.014459876

353.7_274_2

DVLLLVHNLPQNLPGY
PSG9_HUMAN
0.016720007

FWYK_

810.4_960.5

FSVVYAK_
FETUA_HUMAN
0.016792786

407.2_381.2

DVLLLVHNLPQNLPGY
PSG9_HUMAN
0.017335929

FWYK_

810.4_215.1

SEPRPGVLLR_
FA7_HUMAN
0.018147773

375.2_654.4

ALNHLPLEYNSALYSR_
CO6_HUMAN
0.019056484

621.0_538.3

WNFAYWAAHQPWSR_
PRG2_HUMAN
0.019190043

607.3_545.3

ALNHLPLEYNSALYSR_
CO6_HUMAN
0.020218682

621.0_696.4

AQPVQVAEGSEPDGFW
GELS_HUMAN
0.020226218

EALGGK_

758.0_623.4

GWVTDGFSSLK_
APOC3_HUMAN
0.023192703

598.8_953.5

IALGGLLFPASNLR_
SHBG_HUMAN
0.02391691

481.3_412.3

WNFAYWAAHQPWSR_
PRG2_HUMAN
0.026026975

607.3_673.3

FGFGGSTDSGPIR_
ADA12_HUMAN
0.027731407

649.3_745.4

SEYGAALAWEK_
CO6_HUMAN
0.031865281

612.8_788.4

DADPDTFFAK_
AFAM_HUMAN
0.0335897

563.8_302.1

LFIPQITR_
PSG9_HUMAN
0.034140767

494.3_614.4

DVLLLVHNLPQNLP
PSG9_HUMAN
0.034653304

GYFWYK_

810.4_328.2

TLAFVR_
FA7_HUMAN
0.036441189

353.7_492.3

AVLHIGEK_
THBG_HUMAN
0.038539433

289.5_292.2

IHPSYTNYR_
PSG2_HUMAN
0.039733019

384.2_452.2

AGLLRPDYALLGHR_
PGRP2_HUMAN
0.040916226

518.0_369.2

ILILPSVTR_
PSGx_HUMAN
0.042460036

506.3_559.3

YYLQGAK_
ITIH4_HUMAN
0.044511962

421.7_516.3

TPSAAYLWVGTGAS
GELS_HUMAN
0.046362381

EAEK_

919.5_849.4

AGLLRPDYALLGHR_
PGRP2_HUMAN
0.046572355

518.0_595.4

TYLHTYESEI_
ENPP2_HUMAN
0.04754503

628.3_908.4

FSLVSGWGQLLDR_
FA7_HUMAN
0.048642964

493.3_403_2

VNFTEIQK_
FETA_HUMAN
0.04871392

489.8_765.4

LFIPQITR_
PSG9_HUMAN
0.040288923

494.3_727.4

DISEVVTPR_
CFAB_HUMAN
0.049458374

508.3_787.4

SEPRPGVLLR_
FA7_HUMAN
0.049567047

375.2_454_3

Univariate p-values for Ad_usted Peak

Areas (<37 vs >40 weeks)

Transition
Protein
pvalue

SPELQAEAK_
APOA2_HUMAN
0.001457796

486.8_659.4

DYWSTVK_
APOC3_HUMAN
0.001619622

449.7_347.2

DYWSTVK_
APOC3_HUMAN
0.002068704

449.7_620.3

DALSSVQESQVAQQAR_
APOC3_HUMAN
0.00250563

573.0_502.3

GWVTDGFSSLK_
APOC3_HUMAN
0.002543943

598.8_854.4

SPELQAEAK_
APOA2_HUMAN
0.003108814

486.8_788.4

SEPRPGVLLR_
FA7_HUMAN
0.004035832

375.2_654.4

DALSSVQESQVAQQAR_
APOC3_HUMAN
0.00434652

573.0_672.4

SEYGAALAWEK_
CO6_HUMAN
0.005306924

612.8_788.4

GWVTDGFSSLK_
APOC3_HUMAN
0.005685534

598.8_953.5

ALNHLPLEYNSALYSR_
CO6_HUMAN
0.005770384

621.0_696.4

TYLHTYESEI_
ENPP2_HUMAN
0.005798991

628.3_515.3

ENPAVIDFELAPIVDLVR_
CO6_HUMAN
0.006248095

670.7_601.4

ALNHLPLEYNSALYSR_
CO6_HUMAN
0.006735817

621.0_538.3

TYLHTYESEI_
ENPP2_HUMAN
0.007351774

628.3_908.4

AGLLRPDYALLGF1R_
PGRP2_HUMAN
0.009541521

518_0_369.2

AKPALEDLR_
APOA1_HUMAN
0.009780371

506.8_813.5

SEYGAALAWEK_
CO6_HUMAN
0.010085363

612.8_845.5

FSLVSGWGQLLDR_
FA7_HUMAN
0.010401836

493.3_447.3

WGAAPYR_
PGRP2_HUMAN
0.011233623

410.7_634.3

ENPAVIDFELAPIVDLVR_
CO6_HUMAN
0.012029564

670.7_811.5

DVLLLVHNLPQNLPGYFWYK_
PSG9_HUMAN
0.014808277

810_4_215.1

LFIPQITR_
PSG9_HUMAN
0.015879755

494.3_614.4

WGAAPYR_
PGRP2_HUMAN
0.016562435

410.7_577.3

AGLLRPDYALLGHR_
PGRP2_HUMAN
0.016793521

518_0_595.4

TLAFVR_
FA7_HUMAN
0.016919708

353.7_492.3

FSLVSGWGQLLDR_
FA7_HUMAN
0.016937583

493.3_403.2

WWGGQPLWITATK_
ENPP2_HUMAN
0.019050115

772.4_373.2

GYVIIKPLVWV_
SAMP_HUMAN
0.019675317

643.9_304.2

DVLLLVHNLPQNLPG
PSG9_HUMAN
0.020387647

YFWYK_

810.4_960.5

FGFGGSTDSGPIR_
ADA12_HUMAN
0.020458335

649.3_745.4

DVLLLVHNLPQNLP
PSG9_HUMAN
0.021488084

GYFWYK_

810.4_328.2

WWGGQPLWITATK_
ENPP2_HUMAN
0.021709354

772.4_929.5

LDFHFSSDR_
INHBC_HUMAN
0.022403383

375.2_448.2

LFIPQITR_
PSG9_HUMAN
0.025561103

494.3_727.4

TEFLSNYLTNVDDI
ENPP2_HUMAN
0.029344366

TLVPGTLGR_

846.8_600.3

LSIPQITTK_
PSG5_HUMAN
0.031361776

500.8_800.5

ALVLELAK_
INHBE_HUMAN
0.031690737

428.8_672.4

SEPRPGVLLR_
FA7_HUMAN
0.033067953

375.2_454.3

LSIPQITTK_
PSG5_HUMAN
0.033972449

500.8_687.4

LDFHFSSDR_
INHBC_HUMAN
0.034500249

375.2_611.3

LDFHFSSDR_
INHBC_HUMAN
0.035166664

375.2_464.2

GAVHVVVAETDYQS
CO8G_HUMAN
0.037334975

FAVLYLER_

822.8_580.3

HELTDEELQSLFTN
AFAM_HUMAN
0.039258528

FANVVDK_

817.1_854_4

AYSDLSR_
SAMP_HUMAN
0.04036485

406.2_375.2

YYLQGAK_
ITIH4_HUMAN
0.042204165

421.7_516.3

ILPSVPK_
PGH1_HUMAN
0.042397885

377.2_264.2

ELLESYIDGR_
THRB_HUMAN
0.043053589

597.8_710.4

ALALPPLGLAPLLN
SHBG_HUMAN
0.045692283

LWAKPQGR_

770.5_256.2

VGEYSLYIGR_
SAMP_HUMAN
0.04765767

578.8_871.5

ANDQYLTAAALHNL
ILIA_HUMAN
0.048928376

DEAVK_

686.4_317.2

YYGYTGAFR_
TRFL_HUMAN
0.049568351

549.3_551.3

TABLE 56

Univariate p-values for Adjusted Peak Areas in Time to Birth Linear

Model

Protein
pvalue

ADA12_HUMAN
0.003412707

ENPP2_HUMAN
0.003767393

ADA12_HUMAN
0.004194234

ENPP2_HUMAN
0.004298493

ADA12_HUMAN
0.004627197

ADA12_HUMAN
0.004918852

ENPP2_HUMAN
0.005792374

CO6_HUMAN
0.005858282

ENPP2_HUMAN
0.007123606

CO6_HUMAN
0.007162317

ENPP2_HUMAN
0.008228726

ENPP2_HUMAN
0.009168492

PSG9_HUMAN
0.011531192

PSG9_HUMAN
0.019389627

PSG9_HUMAN
0.023680865

INHBE_HUMAN
0.02581564

B2MG_HUMAN
0.026544689

LBP_HUMAN
0.031068274

PSG9_HUMAN
0.031091843

APOA2_HUMAN
0.033130498

INHBC_HUMAN
0.03395215

CBG_HUMAN
0.034710348

PSGx_HUMAN
0.035719227

CBG_HUMAN
0.036331871

CSH_HUMAN
0.039896611

CSH_HUMAN
0.04244001

SAMP_HUMAN
0.047112128

LBP_HUMAN
0.048141371

LBP_HUMAN
0.048433174

CO6_HUMAN
0.04850949

PSGx_HUMAN
0.049640167

TABLE 57

Univariate p-values for Ad_usted Peak Areas

in Gestation Age at Birth Linear Model

Transition
Protein
pvalue

ENPAVIDFELAPIVDLVR_
CO6_HUMAN
0.000117239

670.7_811.5

ENPAVIDFELAPIVDLVR_
CO6_HUMAN
0.000130113

670.7_601.4

TYLHTYESEI_
ENPP2_HUMAN
0.000160472

628.3_908.4

TYLHTYESEI_
ENPP2_HUMAN
0.000175167

628.3_515.3

TEFLSNYLTNVDDITLV
ENPP2_HUMAN
0.000219886

PGTLGR_

846.8_600.3

TEFLSNYLTNVDDITLV
ENPP2_HUMAN
0.000328416

PGTLGR_

846.8_699.4

WWGGQPLWITATK_
ENPP2_HUMAN
0.000354644

772.4_373.2

WWGGQPLWITATK_
ENPP2_HUMAN
0.000390821

772.4_929.5

SEYGAALAWEK_
CO6_HUMAN
0.000511882

612_8_788.4

LDFHFSSDR_
INHBC_HUMAN
0.000600637

375.2_448.2

ALVLELAK_
INHBE_HUMAN
0.000732445

428.8_672.4

GLQYAAQEGLLALQSE
LBP_HUMAN
0.000743924

LLR_

1037_1_929_5

DVLLLVHNLPQNLPGY
PSG9_HUMAN
0.000759173

FWYK_

810.4_960.5

FGFGGSTDSGPIR_
ADA12_HUMAN
0.001224347

649.3_745.4

DVLLLVHNLPQNLPGY
PSG9_HUMAN
0.001241526

FWYK_

810.4_328.2

GYVIIKPLVWV_
SAMP_HUMAN
0.001853785

643.9_304.2

SPELQAEAK_
APOA2_HUMAN
0.001856303

486.8_659.4

GLQYAAQEGLLALQSE
LBP_HUMAN
0.001978165

LLR_

1037.1_858_5

LDFHFSSDR_
INHBC_HUMAN
0.002098948

375.2_61_F3

LIEIANHVDK_
ADA12_HUMAN
0.002212096

384.6_683.4

SFRPFVPR_
LBP_HUMAN
0.002545286

335.9_272.2

SFRPFVPR_
LBP_HUMAN
0.002620268

335.9_635.3

WSAGLTSSQVDLYIPK_
CBG_HUMAN
0.002787272

883.0_515_3

DLHLSDVFLK_
CO6_HUMAN
0.002954612

396.2_260.2

LIEIANHVDK_
ADA12_HUMAN
0.002955081

384.6_498.3

DVLLLVHNLPQNLPG
PSG9_HUMAN
0.003541011

YFWYK_

810.4_215.1

LFIPQITR_
PSG9_HUMAN
0.003750666

494.3_614.4

FGFGGSTDSGPIR_
ADA12_HUMAN
0.003773696

649.3_946.5

YYLQGAK_
ITIH4_HUMAN
0.004064026

421.7_516.3

SEYGAALAWEK_
CO6_HUMAN
0.004208136

612.8_845.5

AITPPHPASQANIIF
FBLN1_HUMAN
0.004709104

DITEGNLR_

825.8_459.3

LDFHFSSDR_
INHBC_HUMAN
0.005355741

375.2_464.2

HELTDEELQSLFTNFA
AFAM_HUMAN
0.005370567

NVVDK_

817.1_854.4

ALNHLPLEYNSALYSR_
CO6_HUMAN
0.005705922

621.0_696.4

ITQDAQLK_
CBG_HUMAN
0.006762484

458.8_702.4

ITLPDFTGDLR_
LBP_HUMAN
0.006993268

624.3_920.5

SILFLGK_
THBG_HUMAN
0.007134146

389.2_577.4

WSAGLTSSQVDLYIPK_
CBG_HUMAN
0.007670388

883.0_357.2

GVTSVSQIFHSPDLAIR_
IC1_HUMAN
0.007742729

609.7_472.3

VGEYSLYIGR_
SAMP_HUMAN
0.007778691

578.8_871.5

ITLPDFTGDLR_
LBP_HUMAN
0.008179918

624_3_288_2

YYLQGAK_
ITIH4_HUMAN
0.008404686

421.7_327.1

ALNHLPLEYNSALYSR_
CO6_HUMAN
0.008601162

621.0_538_3

DYWSTVK_
APOC3_HUMAN
0.008626786

449.7_620.3

TVQAVLTVPK_
PEDF_HUMAN
0.008907523

528.3_855.5

ITGFLKPGK_
LBP_HUMAN
0.009155417

320.9_301.2

LFIPQITR_
PSG9_HUMAN
0.009571006

494.3_727.4

SPELQAEAK_
APOA2_HUMAN
0.009776508

486.8_788.4

DYWSTVK_
APOC3_HUMAN
0.00998356

449.7_347.2

ITGFLKPGK_
LBP_HUMAN
0.010050264

320.9_429.3

FLNWIK_
HABP2_HUMAN
0.010372454

410.7_560.3

DLHLSDVFLK_
CO6_HUMAN
0.010806378

396.2_366.2

GVTSVSQIFHSPDLAIR_
IC1_HUMAN
0.011035991

609.7_908.5

VEHSDLSFSK_
B2MG_HUMAN
0.011113172

383.5_468.2

LLDSLPSDTR_
IC1_HUMAN
0.011589013

558.8_276.2

LLDSLPSDTR_
IC1_HUMAN
0.011629438

558.8_890.4

QALEEFQK_
CO8B_HUMAN
0.011693839

496.8_551.3

LLDSLPSDTR_
IC1_HUMAN
0.012159314

558.8_575.3

IIGGSDADIK_
C1S_HUMAN
0.013080243

494.8_762.4

AFIQLWAFDAVK_
AMBP_HUMAN
0.013462234

704.9_650.4

GFQALGDAADIR_
TIMP1_HUMAN
0.014370997

617.3_717_4

LPNNVLQEK_
AFAM_HUMAN
0.014424891

527.8_730.4

DTDTGALLFIGK_
PEDF_HUMAN
0.014967952

625_8_217.1

VQTAHFK_
CO8A_HUMAN
0.01524844

277.5_502.3

ILILPSVTR_
PSG1_HUMAN
0.015263132

506.3_559.3

SILFLGK_
THBG_HUMAN
0.015265233

389.2_201.1

TVQAVLTVPK_
PEDF_HUMAN
0.015344052

528.3_428.3

VEPLYELVTATDFAYSSTVR_
CO8B_HUMAN
0.015451068

754.4_712.4

FSLVSGWGQLLDR_
FA7_HUMAN
0.015510454

493.3_447_3

GWVTDGFSSLK_
APOC3_HUMAN
0.01610797

598.8_854.4

LSETNR_
PSG1_HUMAN
0.016433362

360.2_519.3

TQILEWAAER_
EGLN_HUMAN
0.01644844

608.8_632.3

SETEIHQGFQHLHQLFAK_
CBG_HUMAN
0.016720367

717_4_318.1

TNLESILSYPK_
IC1_HUMAN
0.017314185

632.8_936.5

TNLESILSYPK_
IC1_HUMAN
0.017593786

632.8_807.5

AYSDLSR_
SAMP_HUMAN
0.018531348

406.2_375.2

YEVQGEVFTKPQLWP_
CRP_HUMAN
0.019111323

911_0_392.2

AYSDLSR_
SAMP_HUMAN
0.019271266

406.2_577.3

QALEEFQK_
CO8B_HUMAN
0.019429489

496.8_680.3

APLTKPLK_
CRP_HUMAN
0.020110081

289.9_398.8

FQPTLLTLPR_
IC1_HUMAN
0.020114306

593.4_276.1

ITQDAQLK_
CBG_HUMAN
0.020401782

453.8_803.4

AVLH1GEK_
THBG_HUMAN
0.02056597

289.5_292.2

ANDQYLTAAALHNLDE
ILIA_HUMAN
0.020770124

AVK_

686.4_317.2

VGEYSLYIGR_
SAMP_HUMAN
0.021126414

578.8_708.4

TLYSSSPR_
IC1_HUMAN
0.021306106

455.7_533.3

VEHSDLSFSK_
B2MG_HUMAN
0.021640643

383.5_234.1

HELTDEELQSLFTNFA
AFAM_HUMAN
0.021921609

NVVDK_

817.1_906.5

TLYSSSPR_
IC1_HUMAN
0.022196181

455.7_696.3

GYVIIKPLVWV_
SAMP_HUMAN
0.023126336

643.9_854.6

DEIPHNDIALLK_
HABP2_HUMAN
0.023232158

459.9_260.2

ILILPSVTR_
PSGx_HUMAN
0.023519909

506.3_785.5

WNFAYWAAHQPWSR_
PRG2_HUMAN
0.023697087

607.3_545.3

FQPTLLTLPR_
IC1_HUMAN
0.023751959

593.4_712.5

AQPVQVAEGSEPDGF
GELS_HUMAN
0.024262721

WEALGGK_

758.0_623.4

DEIPHNDIALLK_
HABP2_HUMAN
0.024414348

459.9_510.8

GDSGGAFAVQDPNDK_
C1S_HUMAN
0.025075028

739.3_716.3

FLNWIK_
HABP2_HUMAN
0.025649617

410.7_561.3

APLTKPLK_
CRP_HUMAN
0.025961162

289.9_357.2

ALDLSLK_
ITIH3_HUMAN
0.026233504

380.2_185.1

GWVTDGFSSLK_
APOC3_HUMAN
0.026291884

598_8_953.5

SETEIHQGFQHLHQLFAK_
CBG_HUMAN
0.026457136

717.4_447.2

GDSGGAFAVQDPNDK_
C1S_HUMAN
0.02727457

739.3_473.2

YEVQGEVFTKPQLWP_
CRP_HUMAN
0.028244448

911.0_293.1

HVVQLR_
IL6RA_HUMAN
0.028428028

376.2_614.4

DTDTGALLFIGK_
PEDF_HUMAN
0.028773557

625.8_818.5

EVPLSALTN1LSAQLI
PAI1_HUMAN
0.029150774

SHWK_

740.8_996.6

AFTECCVVASQLR_
CO5_HUMAN
0.029993325

770.9_574.3

TLAFVR_
FA7_HUMAN
0.030064307

353.7_492.3

LWAYLTIQELLAK_
ITIH1_HUMAN
0.030368674

781.5_300.2

DEIPHNDIALLK_
HABP2_HUMAN
0.031972082

459_9_245_1

AGLLRPDYALLGHR_
PGRP2_HUMAN
0.032057409

518.0_369.2

AVYEAVLR_
PEPD_HUMAN
0.032527521

460.8_587.4

LPNNVLQEK_
AFAM_HUMAN
0.033807082

527.8_844.5

GAVHVVVAETDYQSFA
CO8G_HUMAN
0.054370139

VLYLER_

822_8_580.3

WNFAYWAAHQPWSR_
PRG2_HUMAN
0.0349737

607.3_673.3

EAQLPVIENK_
PLMN_HUMAN
0.035304322

570.8_329.2

VQEAHLTEDQIFYFPK_
CO8G_HUMAN
0.035704382

655.7_701.4

AFIQLWAFDAVK_
AMBP_HUMAN
0.035914532

704.9_836.4

SGFSFGFK_
CO8B_HUMAN
0.037168221

438.7_585.3

SGFSFGFK_
CO8B_HUMAN
0.040182596

438.7_732.4

DADPDTFFAK_
AFAM_HUMAN
0.041439744

563.8_302.1

EAQLPV1ENK_
PLMN_HUMAN
0.041447675

570.8_699.4

IIGGSDADIK_
C1S_HUMAN
0.041683256

494.8_260.2

AVLT1DEK_
A1AT_HUMAN
0.043221658

444.8_718.4

SEPRPGVLLR_
FA7_HUMAN
0.044079127

375.2_654.4

YHFEALADTGISSEFY
CO8A_HUMAN
0.045313634

DNANDLLSK_

940.8_874.5

HFQNLGK_
AFAM_HUMAN
0.047118971

422.2_527.2

LEQGENVFLQATDK_
C1QB_HUMAN
0.047818928

796.4_822.4

NTVISVNPSTK_
VCAM1_HUMAN
0.048102262

580.3_732.4

YYGYTGAFR_
TRFL_HUMAN
0.048331316

549.3_551.3

ISLLLIESWLEPVR_
CSH_HUMAN
0.049561581

834.5_500.3

LQVLGK_
A2GL_HUMAN
0.049738493

329.2_416.3

TABLE 58

Univariate p-values for Peak Area Ratios

(<37 vs >37 weeks)

UniProt_ID
Transition
pvalue

SHBG_HUMAN
IALGGLLFPASN
0.006134652

LR_

481.3_

657.4

SHBG_HUMAN
IALGGLLFPASN
0.019049498

LR_

481.3_

412.3

APOC3_HUMAN
DALSSVQESQVAQ
0.020688543

QAR_

573.0_

672.4

THBG_HUMAN
AVLH1GEK_
0.0291698

289.5_

292.2

PSG9_HUMAN
DVLLLVHNLPQNL
0.033518454

PGYFWYK_

810.4_

960.5

APOC3_HUMAN
DALSSVQESQVAQ
0.043103265

QAR_

573.0_

502.3

PSG9_HUMAN
LFIPQITR_
0.04655948

494.3_

614.4

TABLE 59

Univariate p-values for Peak Area

Ratios (<37 vs >40 weeks)

UniProt_ID
Transition
pvalue

APOC3_
DALSSVQESQVA
0.011174438

HUMAN
QQAR_573.0_

672.4

APOC3_
DALSSVQESQVA
0.015231617

HUMAN
QQAR_573.0_

502.3

PSG9_
LFIPQITR_
0.018308413

HUMAN
494.3_614.4

PSG9_
LFIPQITR_
0.027616871

HUMAN
494.3_727.4

PSG9_
DVLLLVHNLPQN
0.028117582

HUMAN
LPGYFWYK_

810.4_960.5

THBG_
AVLHIGEK_
0.038899107

HUMAN
289.5_292.2

CO6_
ALNHLPLEYNSA
0.040662269

HUMAN
LYSR_621.0_

696.4

ENPP2_
TYLHTYESEI_
0.044545826

HUMAN
628.3_908.4

TABLE 60

Univariate p-values for Peak Area Ratios in

Time to Birth Linear Model

UniProt_ID
Transition
pvalue

ADA12_
FGFGGSTDSGPIR_
5.85E−27

HUMAN
649.3_946.5

ADA12_
FGFGGSTDSGPIR_
2.65E−24

HUMAN
649.3_745.4

PSG4_
TLF1FGVTK_
1.07E−20

HUMAN
513.3_215.1

PSG4_
TLFIFGVTK_
2.32E−20

HUMAN
513.3_811.5

PSGx_
ILILPSVTR_
8.25E−16

HUMAN
506.3_785.5

PSGx_
ILILPSVTR_
9.72E−16

HUMAN
506.3_559.3

PSG1_
FQLPGQK_
1.29E−12

HUMAN
409.2_429.2

PSG11_
LFIPQITPK_
2.11E−12

HUMAN
528.8_261.2

PSG1_
FQLPGQK_
2.33E−12

HUMAN
409.2_276.1

PSG11_
LFIPQITPK_
3.90E−12

HUMAN
528.8_683_4

PSG6_
SNPVTLNVLY
5.71E−12

HUMAN
GPDLPR_

585.7_817.4

PSG6_
SNPVTLNVLY
1.82E−11

HUMAN
GPDLPR_

585.7_654.4

VGFR3_
SGVDLADSNQK_
4.57E−11

HUMAN
567.3_662.3

INHBE_
ALVLELAK_
1.04E−08

HUMAN
428.8_331.2

PSG2_
IHPSYTNYR_
6.27E−08

HUMAN
384.2_452.2

PSG9_
LFIPQITR_
1.50E−07

HUMAN
494.3_727.4

VGFR3_
SGVDLADSNQK_
2.09E−07

HUMAN
567.3_591.3

PSG9_
LFIPQITR_
2.71E−07

HUMAN
494.3_614_4

PSG9_
DVLLLVHNLPQ
3.10E−07

HUMAN
NLPGYFWYK_

810.4_960.5

PSG2_
IHPSYTNYR_
2.55E−06

HUMAN
384.2_338.2

ITIH3_
LIQDAVTGLTV
2.76E−06

HUMAN
NGQITGDK_

972.0_640.4

ENPP2_
TYLHTYESEI_
2.82E−06

HUMAN
628.3_908_4

ENPP2_
WWGGQPLWI
3.75E−06

HUMAN
TATK_

772.4_373.2

PSG9_
DVLLLVHNLPQ
3.94E−06

HUMAN
NLPGYFWYK_

810.4_328.2

B2MG_
VEHSDLSFSK_
5.42E−06

HUMAN
383.5_468.2

ENPP2_
WWGGQPLW
7.93E−06

HUMAN
ITATK_

772.4_929.5

ANGT_
ALQDQLV
1.04E−05

HUMAN
LVAAK_

634.9_289.2

B2MG_
VNHVTLSQPK_
1.46E−05

HUMAN
374.9_244.2

AFAM_
LPNNVLQEK_
1.50E−05

HUMAN
527.8_730.4

AFAM_
LPNNVLQEK_
1.98E−05

HUMAN
527.8_844.5

THBG_
AVLHIGEK_
2.15E−05

HUMAN
289.5_292.2

ENPP2_
TYLHTYESEI_
2.17E−05

HUMAN
628.3_515.3

IL12B_
DIIKPDPPK_
3.31E−05

HUMAN
511.8_342.2

AFAM_
DADPDTFFAK_
6.16E−05

HUMAN
563.8_302.1

THBG_
AVLHIGEK_
8.34E−05

HUMAN
289.5_348.7

PSG9_
DVLLLVHNLPQ
0.000104442

HUMAN
NLPGYFWYK_

810.4_215.1

B2MG_
VEHSDLSFSK_
0.000140786

HUMAN
383.5_234.1

TRFL_
YYGYTGAFR_
0.000156543

HUMAN
549.3_450.3

HEMO_
QGHNSVFLIK_
0.000164578

HUMAN
381.6_260.2

A1BG_
LLELTGPK_
0.000171113

HUMAN
435.8_227.2

CO6_
ALNHLPLEYN
0.000242116

HUMAN
SALYSR_

621.0_696.4

CO6_
ALNHLPLEYN
0.00024681

HUMAN
SALYSR_

621.0_538.3

ALS_
IRPHTFTGLSGLR_
0.000314359

HUMAN
485.6_432.3

IT1H2_
LSNHNHGlAQR_
0.0004877

HUMAN
413.5_544_3

PEDF_
TVQAVLTVPK_
0.000508174

HUMAN
528.3_855.5

AFAM_
HFQNLGK_
0.000522139

HUMAN
422.2_527.2

FLNA_
TGVAVNKPAEFT
0.000594403

HUMAN
VDAK_

549.6_258.1

ANGT_
ALQDQLVLVAAK_
0.000640673

HUMAN
634.9_956.6

AFAM_
HFQNLGK_
0.000718763

HUMAN
422.2_285.1

HGFA_
LHKPGVYTR_
0.000753293

HUMAN
357.5_692.4

HGFA_
LHKPGVYTR_
0.000909298

HUMAN
357.5_479.3

HABP2_
FLNWIK_
0.001282014

HUMAN
410.7_561.3

FETUA_
HTLNQIDEVK_
0.001389792

HUMAN
598.8_951.5

AFAM_
DADPDIFFAK_
0.001498237

HUMAN
563.8_825.4

B2MG_
VNHVTLSQPK_
0.001559862

HUMAN
374.9_459.3

ALS_
IRPHTFTGLSGLR_
0.001612361

HUMAN
485.6_545.3

A1BG_
LLELTGPK_
0.002012656

HUMAN
435.8_644.4

F13B_
LIENGYFHPVK_
0.00275216

HUMAN
439.6_343.2

ITIH2_
LSNENHGIAQR_
0.00356561

HUMAN
413.5_519.8

APOC3_
DALSSVQESQVA
0.00392745

HUMAN
QQAR_573.0_

672.4

F13B_
LIENGYFHPVK_
0.00434836

HUMAN
439.6_627.4

PEDF_
TVQAVLTVPK_
0.00482765

HUMAN
528.3_428.3

PLMN_
YEFLNGR_
0.007325436

HUMAN
449.7_293.1

HEMO_
QGHNSVFLIK_
0.009508516

HUMAN
381.6_520.4

FETUA_
HTLNQIDEVK_
0.010018936

HUMAN
598.8_958.5

CO5_
LQGTLPVEAR_
0.011140661

HUMAN
542.3_842.5

PLMN_
YEFLNGR_
0.01135322

HUMAN
449.7_606.3

CO5_
TLLPVSKPE1R_
0.015045275

HUMAN
418.3_288.2

HABP2_
FLNWIK_
0.01523134

HUMAN
410.7_560.3

APOC3_
DALSSVQESQVA
0.01584708

HUMAN
QQAR_573.0_

502.3

CO5_
LQGTLPVEAR_
0.017298064

HUMAN
542.3_571.3

CFAB_
DISEWTPR_
0.021743221

HUMAN
508.3_472.3

CERU_
TTIEKPVWLG
0.02376225

FLGPIIK_

HUMAN
638.0_640.4

CO8G_
SLPVSDSVLSGFEQR_
0.041150397

HUMAN
810.9_723.3

CO8G_
FLQEQGHR_
0.042038143

HUMAN
338.8_497.3

CO5_
VFQFLEK_
0.043651929

HUMAN
455.8_811.4

CO8B_
QALEEFQK_
0.04761631

HUMAN
496.8_680.3

TABLE 61

Univariate p-values for Peak Area Ratios

in Gestation Age at Birth Linear Model

UniProt_ID
Transition
pvalue

PSG9_
DVLLLVHNLPQNLP
0.000431547

HUMAN
GYFWYK_

810.4_960.5

B2MG_
VEHSDLSFSK_
0.000561148

HUMAN
383.5_468.2

PSG9_
DVLLLVHNLPQNLP
0.000957509

HUMAN
GYFWYK_

810.4_328.2

ENPP2_
TYLHTYESEI_
0.001058809

HUMAN
628.3_908.4

THBG_
AVLHIGEK_
0.001180484

HUMAN
289.5_292.2

ENPP2_
WWGGQPLWITATK_
0.001524983

HUMAN
772.4_373.2

PSG9_
LFIPQITR_
0.001542932

HUMAN
494.3_614_4

ENPP2_
WWGGQPLWITATK_
0.002047607

HUMAN
772.4_929.5

ENPP2_
TYLHTYESEI_
0.003087492

HUMAN
628.3_515.3

PSG9_
LFIPQITR_
0.00477154

HUMAN
494.3_727.4

PSG9_
DVLLLVHNLPQ
0.004824351

HUMAN
NLPGYFWYK_

810.4_215.1

THBG_
AVLHIGEK_
0.006668084

HUMAN
289.5_348.7

AFAM_
LPNNVLQEK_
0.006877647

HUMAN
527.8_730.4

ADA12_
FGFGGSTDSGPIR_
0.011738104

HUMAN
649.3_745_4

PEDF_
TVQAVLTVPK_
0.013349511

HUMAN
528.3_855.5

A1BG_
LLELTGPK_
0.015793885

HUMAN
435.8_227.2

ITIH3_
ALDLSLK_
0.016080436

HUMAN
380.2_185.1

ADA12_
FGFGGSTDSGP
0.017037089

HUMAN
IR_

649.3_946.5

B2MG_
VEHSDLSFSK_
0.017072093

HUMAN
383.5_234.1

CO6_
ALNHLPLEYNS
0.024592775

HUMAN
ALYSR_

621.0_696.4

TRFL_
YYGYTGAFR_
0.030890831

HUMAN
549.3_450.3

AFAM_
DADPDTFFAK_
0.033791429

HUMAN
563.8_302.1

CO6_
ALNHLPLEYNS
0.034865341

HUMAN
ALYSR_

621.0_538.3

AFAM_
LPNNVLQEK_
0.039880594

HUMAN
527.8_844.5

PEDF_
TVQAVLTVPK_
0.040854402

HUMAN
528.3_428.3

PLMN_
EAQLPVIENK_
0.041023812

HUMAN
570.8_329.2

LBP_
ITLPDFTGDLR_
0.042276813

HUMAN
624.3_920.5

CO8G_
VQEAHLTEDQI
0.042353851

HUMAN
FYFPK_

655.7_701.4

PLMN_
YEFLNGR_
0.04416504

HUMAN
449.7_606.3

B2MG_
VNHVTLSQPK_
0.045458409

HUMAN
374.9_459.3

CFAB_
DISEVVTPR_
0.046493405

HUMAN
508.3_472.3

INHBE_
ALVLELAK_
0.04789353

HUMAN
428.8_331.2

TABLE 62

Random Forest Importance Values Using Adjusted Peak Areas

Transition
Rank
Importance

INHBE_ALVLELAK_428.8_672.4
1
2964.951571

EGLN_TQILEWAAER_608.8_761.4
2
1218.3406

FA7_SEPRPGVLLR_375.2_654.4
3
998.92897

CBG_ITQDAQLK_458.8_702.4
4
930.9931102

ITIH3_ALDLSLK_380.2_185.1
5
869.6315408

ENPP2_WWGGQPLWITATK_772.4_929.5
6
768.9182114

CBG_ITQDAQLK_458.8_803.4
7
767.8940452

PSG1_LSETNR_360.2_519.3
8
714.6160065

CAA60698_LEPLYSASGPGLRPLVIK_637.4_834.5
9
713.4086612

INHBC_LDFHFSSDR_375.2_611.3
11
681.2442909

CBG_QINSYVK_426.2_610.3
12
674.3363415

LBP_GLQYAAQEGLLALQSELLR_1037.1_858.5
13
603.197751

A1BG_LLELTGPK_435.8_644.4
14
600.9902818

CO6_DLHLSDVFLK_396.2_366.2
15
598.8214342

VCAM1_TQIDSPLSGK_523.3_816.5
16
597.4038769

LRP1_NAVVQGLEQPHGLVVHPLR_688.4_285.2
17
532.0500081

CBG_QINSYVK_426.2_496.3
18
516.5575201

CO6_ENPAVIDFELAP1VDLVR_670.7_811.5
19
501.4669261

ADA12_FGFGGSTDSGPIR_649.3_745.4
20
473.5510333

CO6_DLHLSDVFLK_396.2_260.2
21
470.5473702

ENPP2_TYLHTYESEI_628.3_908.4
22
444.7580726

A1BG_LLELTGPK_435.8_227.2
23
444.696292

FRIH_QNYHQDSEAAINR_515.9_544.3
24
439.2648872

ENPP2_TEFLSNYLTNVDDITLVPGTLGR_846_8_600.3
25
389.3769604

CBG_WSAGLTSSQVDLYIPK_883.0_515.3
26
374.0749768

C1QC_FQSVFTVTR_542.8_623.4
27
370.6957977

GELS_DPDQTDGLGLSYLSSHIANVER_796.4_456.2
28
353.1176588

A1BG_ATWSGAVLAGR_544.8_643.4
29
337.4580124

APOA1_AKPALEDLR_506.8_813.5
30
333.5742035

ENPP2_TYLHTYESEI_628.3_515.3
31
322.6339162

PEPD_AVYEAVLR_460.8_750.4
32
321.4377907

TIMP1_GFQALGDAADIR_617.3_717.4
33
310.0997949

ADA12_LIEIANHVDK_384.6_498.3
34
305.8803542

PGRP2_WGAAPYR_410.7_577.3
35
303.5539874

PSG9_LFIPQITR_494.3_614.4
36
300.7877317

HABP2_FLNWIK_410.7_560.3
37
298.3363186

CBG_WSAGLTSSQVDLYIPK_883.0_357.2
38
297.2474385

PSG2_IHPSYTNYR_384.2_452.2
39
292.6203405

PSG5_LSIPQITTK_500.8_800.5
40
290.2023364

HABP2_FLNWIK_410.7_561.3
41
289.5092933

CO6_SEYGAALAWEK_612.8_788.4
42
287.7634114

ADA12_LIEIANHVDK_384.6_683.4
43
286.5047372

EGLN_TQILEWAAER_608.8_632.3
44
284.5138846

CO6_ENPAVIDFELAPIVDLVR_670.7_601.4
45
273.5146272

FA7_FSLVSGWGQLLDR_493.3_447.3
46
271.7850098

ITIH3_ALDLSLK_380.2_575.3
47
269.9425709

ADA12_FGFGGSTDSGPIR_649.3_946.5
48
264.5698225

FETUA_AALAAFNAQNNGSNFQLEEISR_789.1_746.4
49
247.4728828

FBLN1_AITPPHPASQANIIFDITEGNLR_825.8_459.3
50
246.572102

TSP1_FVFGTTPEDILR_697.9_843.5
51
245.0459575

VCAM1_NTVISVNPSTK_580.3_732.4
52
240.576729

ENPP2_TEFLSNYLTNVDDITLVPGTLGR_846.8_699.4
53
240.1949512

FBLN3_ELPQSIVYK_538.8_409.2
55
233.6825304

ACTB_VAPEEHPVLLTEAPLNPK_652.0_892.5
56
226.9772749

TSP1_FVFGTTPEDILR_697.9_742.4
57
224.4627393

PLMN_EAQLPVIENK_570.8_699.4
58
221.4663735

C1S_IIGGSDADEK_494.8_260.2
59
218.069476

ILIA_ANDQYLTAAALHNLDEAVK_686.4_317.2
60
216.5531949

PGRP2_WGAAPYR_410.7_634.3
61
211.0918302

PSG5_LSIPQITTK_500.8_687.4
62
208.7871461

PSG6_SNPVTLNVLYGPDLPR_585.7_654.4
63
207.9294937

PRG2_WNFAYWAAHQPWSR_607.3_545.3
64
202.9494031

CXCL2_CQCLQTLQGIHLK_13p8RT_533.6_567.4
65
202.9051326

CXCL2_CQCLQTLQGIHLK_13p48RT_533.6_695.4
66
202.6561548

G6PE_LLDFEFSSGR_585.8_553.3
67
201.004611

GELS_TASDFITK_441.7_710.4
68
200.2704809

B2MG_VEHSDLSFSK_383.5_468.2
69
199.880987

CO8B_IPGIFELGISSQSDR_809.9_849.4
70
198.7563875

PSG8_LQLSETNR_480.8_606.3
71
197.6739966

LBP_GLQYAAQEGLLALQSELLR_1037.1_929.5
72
197.4094851

AFAM_LPNNVLQEK_527.8_844.5
73
196.8123228

MAGE
74
196.2410502

PSG2_IHPSYTNYR_384.2_338.2
75
196.2410458

PSG9_LFIPQITR_494.3_727.4
76
193.5329266

TFR1_YNSQLLSFVR_613.8_734.5
77
193.2711994

C1R_QRPPDLDTSSNAVDLLFFTDESGDSR_961.5_866.3
78
193.0625419

PGH1_ILPSVPK_377.2_264.2
79
190.0504508

FA7_SEPRPGVLLR_375.2_454.3
80
188.2718422

FA7_TLAFVR_353.7_274.2
81
187.6895294

PGRP2_DGSPDVTTADIGANTPDATK_973.5_844.4
82
185.6017519

C1S_IIGGSDADIK_494.8_762.4
83
184.5985543

PEPD_VPLALFALNR_557.3_620.4
84
184.3962957

C1S_EDTPNSVWEPAK_686.8_630.3
85
179.2043504

CHL1_TAVTANLDIR_537.3_802.4
86
174.9866792

CHL1_VIAVNEVGR_478.8_744.4
88
172.2053147

SDF1_ILNTPNCALQIVAR_791.9_341.2
89
171.4604557

PAI1_EVPLSALTNILSAQLISHWK_740.8_996.6
90
169.5635635

AMBP_AFIQLWAFDAVK_704.9_650.4
91
169.2124477

G6PE_LLDFEFSSGR_585.8_944.4
92
168.2398598

THBG_SILFLGK_389.2_577.4
93
166.3110206

PRDX2_GLFIIDGK_431.8_545.3
94
164.3125132

ENPP2_WWGGQPLWITATK_772.4_373.2
95
163.4011689

VGFR3_SGVDLADSNQK_567.3_662.3
96
162.8822352

C1S_EDTPNSVWEPAK_686.8_315.2
97
161.6140915

AFAM_DADPDTFFAK_563.8_302.1
98
159.5917449

CBG_SETEIHQGFQHLHQLFAK_717.4_447.2
99
156.1357404

C1S_LLEVPEGR_456.8_686.4
100
155.1763293

PTGDS_GPGEDFR_389.2_623.3
101
154.9205208

ITIH2_IYLQPGR_423.7_329.2
102
154.6552717

FA7_TLAFVR_353.7_492.3
103
152.5009422

FA7_FSLVSGWGQLLDR_493.3_403.2
104
151.9971204

SAMP_VGEYSLYIGR_578.8_871.5
105
151.4738449

APOH_EHSSLAFWK_552.8_267.1
106
151.0052645

PGRP2_AGLLRPDYALLGHR_518.0_595.4
107
150.4149907

C1QC_FNAVLTNPQGDYDTSTGK_964.5_333.2
108
149.2592827

PGRP2_AGLLRPDYALLGHR_518.0_369.2
109
147.3609354

PGRP2_TFTLLDPK_467.8_686.4
111
145.2145223

CO5_TDAPDLPEENQAR_728.3_843.4
112
144.5213118

THRB_ELLESYIDGR_597.8_839.4
113
143.924639

GELS_DPDQTDGLGLSYLSSHIANVER_796.4_328.1
114
142.8936101

TRFL_YYGYTGAFR_549.3_450.3
115
142.8651352

HEMO_QGHNSVFLIK_381.6_260.2
116
142.703845

C1S_GDSGGAFAVQDPNDK_739.3_716.3
117
142.2799122

B1A4H9_AHQLAIDTYQEFR_531.3_450.3
118
138.196407

C1S_SSNNPHSPIVEEFQVPYNK_729.4_261.2
119
136.7868935

HYOU1_LPATEKPVLLSK_432.6_347.2
120
136.1146437

FETA_GYQELLEK_490.3_502.3
121
135.2890322

LRP1_SERPPIFEIR_415.2_288.2
122
134.6569527

CO6_SEYGAALAWEK_612.8_845.5
124
132.8634704

CERU_TTIEKPVWLGFLGPIIK_638.0_844.5
125
132.1047746

IBP1_AQETSGEEISK_589.8_850.4
126
130.934446

SHBG_VVLSSGSGPGLDLPLVLGLPLQLK_791.5_768.5
127
128.2052287

CBG_SETEIHQGFQHLHQLFAK_717.4_318.1
128
127.9873837

A1AT_LSITGTYDLK_555.8_696.4
129
127.658818

PGRP2_DGSPDVTTADIGANTPDATK_973.5_531.3
130
126.5775806

C1QB_LEQGENVFLQATDK_796.4_675.4
131
126.1762726

EGLN_GPITSAAELNDPQSILLR_632.4_826.5
132
125.7658253

IL12B_YENYTSSFFIR_713.8_293.1
133
125.0476631

B2MG_VEHSDLSFSK_383.5_234.1
134
124.9154706

PGH1_AEHPTWGDEQLFQTTR_639.3_765.4
135
124.8913193

INHBE_ALVLELAK_428.8_331.2
136
124.0109276

HYOU1_LPATEKPVLLSK_432.6_460.3
137
123.1900369

CXCL2_CQCLQTLQGIHLK_13p48RT_533.6_567.4
138
122.8800873

PZP_AVGYLITGYQR_620.8_523.3
139
122.4733204

AFAM_IAPQLSTEELVSLGEK_857.5_333.2
140
122.4707849

ICAM1_VELAPLPSWQPVGK_760.9_400.3
141
121.5494206

CHL1_VIAVNEVGR_478.8_284.2
142
119.0877137

APOB_ITENDIQIALDDAK_779.9_632.3
143
118.0222045

SAMP_AYSDLSR_406.2_577.3
144
116.409429

AMBP_AFIQLWAFDAVK_704.9_836.4
145
116.1900846

EGLN_GPITSAAELNDPQSILLR_632.4_601.4
146
115.8438804

LRP1_NAVVQGLEQPHGLVVHPLR_688.4_890.6
147
114.539707

SHBG_VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4
148
113.1931134

IBP1_AQETSGEEISK_589.8_979.5
149
112.9902709

PSG6_SNPVTLNVLYGPDLPR_585.7_817.4
150
112.7910917

APOC3_DYWSTVK_449.7_347.2
151
112.544736

C1R_WILTAAHTLYPK_471.9_621.4
152
112.2199708

ANGT_ADSQAQLLLSTVVGVFTAPGLHLK_822.5_983.6
153
111.9634671

PSG9_DVLLLVHNLPQNLPGYFWYK_810.4_328.2
154
111.5743214

A1AT_AVLTIDEK_444.8_605.3
155
111.216651

PSGx_ILILPSVTR_506.3_785.5
156
110.8482935

THRB_ELLESYIDGR_597.8_710.4
157
110.7496103

SHBG_ALALPPLGLAPLLNLWAKPQGR_770.5_256.2
158
110.5091269

PZP_QTLSWTVTPK_580.8_545.3
159
110.4675104

SHBG_ALALPPLGLAPLLNLWAKPQGR_770.5_457.3
160
110.089808

PSG4_TLFIFGVTK_513.3_811.5
161
109.9039967

PLMN_YEFLNGR_449.7_293.1
162
109.6880397

PEPD_AVYEAVLR_460.8_587.4
163
109.3697285

PLMN_LSSPAVITDK_515.8_830.5
164
108.963353

FINC_SYTITGLQPGTDYK_772.4_352.2
165
108.452612

C1R_WILT_AAHTL_YPK_471.9_407.2
166
107.8348417

CHL1_TAVTANLDIR_537.3_288.2
167
107.7278897

TENA_AVDIPGLEAATPYR_736.9_286.1
168
107.6166195

CRP_YEVQGEVFTKPQLWP_911.0_293.1
169
106.9739589

APOB_SVSLPSLDPASAK_636.4_885.5
170
106.5901668

PRDX2_SVDEALR_395.2_488.3
171
106.2325046

CO8A_YHFEALADTGISSEFYDNANDLLSK_940.8_301.1
172
105.8963287

C1QC_FQSVFTVTR_542.8_722.4
173
105.4338742

PSGx_ILILPSVTR_506.3_559.3
174
105.1942655

VCAM1_TQIDSPLSGK_523.3_703.4
175
105.0091767

VCAM1_NTVISVNPSTK_580.3_845.5
176
104.8754444

CSH_ISLLLIESWLEPVR_834.5_500.3
177
104.6158295

HGFA_EALVPLVADHK_397.9_439.8
178
104.3383142

CGB1_CRPINATLAVEK_457.9_660.4
179
104.3378072

APOB_IEGNLIFDPNNYLPK_874.0_414.2
180
103.9849346

C1QB_LEQGENVFLQATDK_796.4_822.4
181
103.9153207

APOH_EHSSLAFWK_552.8_838.4
182
103.9052103

CO5_LQGTLPVEAR_542.3_842.5
183
103.1061869

SHBG_1ALGGLLFPASNLR_481.3_412.3
184
102.2490294

B2MG_VNHVTLSQPK_374.9_459.3
185
102.1204362

APOA2_SPELQAEAK_486.8_659.4
186
101.9166647

FLNA_TGVAVNKPAEFTVDAK_549.6_258.1
187
101.5207852

PLMN_YEFLNGR_449.7_606.3
188
101.2531011

TABLE 63

Random Forest Importance Values

Using Peak Area Ratios

Variable
Rank
Importance

HABP2_FLNWIK_
1
3501.905733

410.7_561.3

ADA12_FGFGGST
2
3136.589992

DSGPIR_

649.3_946.5

A1BG_
3
2387.891934

LLELTGPK_

435.8_227.2

B2MG_
4
1431.31771

VEHSDLSFSK_

383.5_234.1

ADA12_FGFGGST
5
1400.917331

DSGPIR_

649.3_745.4

B2MG_
6
1374.453629

VEHSDLSFSK_

383.5_468.2

APOB_
7
1357.812445

IEGNLIFDPNN

YLPK_

874.0_414.2

PSG9_DVLLLVHNL
8
1291.934596

PQNLPGYFWYK_

810.4_960.5

A1BG_
9
1138.712941

LLELTGPK_

435.8_644.4

ITIH3_ALDLSLK_
10
1137.127027

380.2_185.1

ENPP2_TYLHTYESEI_
11
1041.036693

628.3_908.4

IL12B_
12
970.1662913

YENYTSSFFIR_

713.8_293.1

ENPP2_WWGGQPL
13
953.0631062

WITATK_

772.4_373.2

ENPP2_TYLHTYESEI_
14
927.3512901

628.3_515.3

PSG9_LFIPQITR_
15
813.9965357

494.3_614.4

MAGE
16
742.2425022

ENPP2_WWGGQPL
17
731.5206413

WITATK_

772.4_929.5

CERU_
18
724.7745695

TTIEKPVWLGFL

GPIIK_

638.0_640.4

ITIH3_ALDLSLK_
19
710.1982467

380.2_575.3

PSG2_IHPSYTNYR_
20
697.4750893

384.2_452.2

ITIH1_LWAYLTI
21
644.7416886

QELLAK_

781.5_371.2

INHBE_
22
643.008853

ALVLELAK_

428.8_331.2

HGFA_
23
630.8698445

LHKPGVYTR_

357.5_692.4

TRFL_
24
609.5866675

YYGYTGAFR_

549.3_450.3

THBG_
25
573.9320948

AVLHIGEK_

289.5_348.7

GELS_
26
564.3288862

TASDFITK_

441.7_710.4

PSG9_LFIPQITR_
27
564.1749327

494.3_727.4

VGFR3_SGVDLA
28
563.8087791

DSNQK_

567.3_662.3

INHA_
29
554.210214

TTSDGGYSFK_

531.7_860.4

PSG9_DVLLLVHNL
30
545.1743627

PQNLPGYFWYK_

810.4_328.2

HYOU1_LPATEK
31
541.6208032

PVLLSK_

432.6_347.2

C08G_
32
541.3193428

VQEAHLTEDQIFYFPK_

655.7_701.4

BMI
33
540.5028818

HGFA_
34
536.6051948

LHKPGVYTR_

357.5_479.3

PSG2_IHPSYTNYR_
35
536.5363489

384.2_338.2

GELS_
36
536.524931

AQPVQVAEGSEPDGFW

EALGGK_

758.0_623.4

PSG6_SNPVTLNVLYG
37
520.108646

PDLPR_

585.7_654.4

HABP2_FLNWIK_
38
509.0707814

410.7_560.3

PGH1_ILPSVPK_
39
503.593718

377.2_527.3

HYOU1_LPATEKPVL
40
484.047422

LSK_

432.6_460.3

C06_ALNHLPLEYNSA
41
477.8773179

LYSR_

621.0_696.4

INHBE_
42
459.1998276

ALVLELAK_

428.8_672.4

PLMN_
43
452.9466414

LSSPAVITDK_

515.8_743.4

PSG9_DVLLLVHNLPQ
44
431.8528248

NLPGYFWYK_

810.4_215.1

BGH3_LTLLAPLNSVFK_
45
424.2540315

658.4_875.5

AFAM_
46
421.4953221

LPNNVLQEK_

527.8_730.4

ITIH2_LSNENHGIAQR_
47
413.1231437

413.5_519.8

GELS_
48
404.2679723

TASDFITK_

441.7_781.4

FETUA_
49
400.4711207

AHYDLR_

387.7_566.3

CERU_
50
396.2873451

TTIEKPVWLGFLGPI1K_

638.0_844.5

PSGx_ILILPSVTR_
51
374.5672526

506.3_785.5

APOB_
52
371.1416438

SVSLPSLDPASAK_

636.4_885.5

FLNA_
53
370.4175588

TGVAVNKPAEFTVDAK_

549.6_258.1

PLMN_
54
367.2768078

YEFLNGR_

449.7_606.3

PSGx_ILILPSVTR_
55
365.7704321

506.3_559.3

	Number	Date	Country
	61919586	Dec 2013	US
	61798504	Mar 2013	US

BIOMARKERS AND METHODS FOR PREDICTING PRETERM BIRTH

Information

Publication Number

Date Filed

Date Published

Inventors

Original Assignees

CPC

International Classifications

Abstract

Description

Claims

Parent Case Info

Provisional Applications (2)

Continuations (3)

	Number	Date	Country
Parent	16255757	Jan 2019	US
Child	17352898		US
Parent	15286486	Oct 2016	US
Child	16255757		US
Parent	14213861	Mar 2014	US
Child	15286486		US