BIOMARKERS AND METHODS FOR PREDICTING PRETERM BIRTH

BACKGROUND

According to the World Heath 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 an/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 sprectrometry, 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 prediciting 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 prediciting 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) substracting 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 R E, Butler A S, editors. Washington (D.C.): 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 prediciting 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 prediciting 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) substracting 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 prediciting 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 prediciting 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 meansurements, 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 sprectrometry, 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 prediciting 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 prediciting time to birth in a pregnant female. Specifically, he 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 prediciting 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 dislosed 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 (RIA). 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-immunoprecitipation-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 quantitiative 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), immunopercipitation, 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 (RIA), 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 radioactavely-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-sectrometry 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 inventon.

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 therof, 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 cacluclated 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 caluclated. 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 uni-

Transition
Protein
variate

ITLPDFTGDLR_624.34_920.4
LBP_HUMAN
0.006

ELLESYIDGR_597.8_710.3
THRB_HUMAN
0.006

TDAPDLPEENQAR_728.34_613.3
CO5_HUMAN
0.007

AFTECCVVASQLR_770.87_574.3
CO5_HUMAN
0.009

SFRPFVPR_335.86_272.2
LBP_HUMAN
0.011

ITLPDFTGDLR_624.34_288.2
LBP_HUMAN
0.012

SFRPFVPR_335.86_635.3
LBP_HUMAN
0.015

ELLESYIDGR_597.8_839.4
THRB_HUMAN
0.018

LEQGENVFLQATDK_796.4_822.4
C1QB_HUMAN
0.019

ETAASLLQAGYK_626.33_679.4
THRB_HUMAN
0.021

VTGWGNLK_437.74_617.3
THRB_HUMAN
0.021

EAQLPVIENK_570.82_699.4
PLMN_HUMAN
0.023

EAQLPVIENK_570.82_329.1
PLMN_HUMAN
0.023

FLQEQGHR_338.84_497.3
CO8G_HUMAN
0.025

IRPFFPQQ_516.79_661.4
FIBB_HUMAN
0.028

ETAASLLQAGYK_626.33_879.5
THRB_HUMAN
0.029

AFTECCVVASQLR_770.87_673.4
CO5_HUMAN
0.030

TLLPVSKPEIR_418.26_288.2
CO5_HUMAN
0.030

LSSPAVITDK_515.79_743.4
PLMN_HUMAN
0.033

YEVQGEVFTKPQLWP_910.96_392.2
CRP_HUMAN
0.036

LQGTLPVEAR_542.31_571.3
CO5_HUMAN
0.036

VRPQQLVK_484.31_609.3
ITIH4_HUMAN
0.036

IEEIAAK_387.22_531.3
CO5_HUMAN
0.041

TLLPVSKPEIR_418.26_514.3
CO5_HUMAN
0.042

VQEAHLTEDQIFYFPK_655.66_701.4
CO8G_HUMAN
0.047

ISLLLIESWLEPVR_834.49_371.2
CSH_HUMAN
0.048

ALQDQLVLVAAK_634.88_289.2
ANGT_HUMAN
0.048

YEFLNGR_449.72_293.1
PLMN_HUMAN
0.049

TABLE 2

Transitions selected by the Cox stepwise AIC analysis

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

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

ITLPDFTGDLR_624.34_920.4
2.02E+00
7.52E+00
1.14E+00
1.77
0.07667

TPSAAYLWVGTGASEAEK_919.45_849.4
2.85E+01
2.44E+12
3.06E+00
9.31
<2e−16

TATSEYQTFFNPR_781.37_386.2
5.14E+00
1.70E+02
6.26E−01
8.21
2.20E−16

TASDFITK_441.73_781.4
−1.25E+00
2.86E−01
1.58E+00
−0.79
0.42856

IITGLLEFEVYLEYLQNR_738.4_530.3
1.30E+01
4.49E+05
1.45E+00
9
<2e−16

IIGGSDADIK_494.77_762.4
−6.43E+01
1.16E−28
6.64E+00
−9.68
<2e−16

YTTEIIK_434.25_603.4
6.96E+01
1.75E+30
7.06E+00
9.86
<2e−16

EDTPNSVWEPAK_686.82_315.2
7.91E+00
2.73E+03
2.66E+00
2.98
0.00293

LYYGDDEK_501.72_726.3
8.74E+00
6.23E+03
1.57E+00
5.57
2.50E−08

VRPQQLVK_484.31_609.3
4.64E+01
1.36E+20
3.97E+00
11.66
<2e−16

GGEIEGFR_432.71_379.2
−3.33E+00
3.57E−02
2.19E+00
−1.52
0.12792

DGSPDVTTADIGANTPDATK_973.45_844.4
−1.52E+01
2.51E−07
1.41E+00
−10.8
<2e−16

VQEAHLTEDQIFYFPK_655.66_391.2
−2.02E+01
1.77E−09
2.45E+00
−8.22
2.20E−16

VEIDTK_352.7_476.3
7.06E+00
1.17E+03
1.45E+00
4.86
1.20E−06

AVLTIDEK_444.76_605.3
7.85E+00
2.56E+03
9.46E−01
8.29
<2e−16

FSVVYAK_407.23_579.4
−2.44E+01
2.42E−11
3.08E+00
−7.93
2.20E−15

YYLQGAK_421.72_516.3
−1.82E+01
1.22E−08
2.45E+00
−7.44
1.00E−13

EENFYVDETTVVK_786.88_259.1
−1.90E+01
5.36E−09
2.71E+00
−7.03
2.00E−12

YGFYTHVFR_397.2_421.3
1.90E+01
1.71E+08
2.73E+00
6.93
4.20E−12

HTLNQIDEVK_598.82_951.5
1.03E+01
3.04E+04
2.11E+00
4.89
9.90E−07

AFIQLWAFDAVK_704.89_836.4
1.08E+01
4.72E+04
2.59E+00
4.16
3.20E−05

SGFSFGFK_438.72_585.3
1.35E+01
7.32E+05
2.56E+00
5.27
1.40E−07

GWVTDGFSSLK_598.8_854.4
−3.12E+00
4.42E−02
9.16E−01
−3.4
0.00066

ITENDIQIALDDAK_779.9_632.3
1.91E+00
6.78E+00
1.36E+00
1.4
0.16036

TABLE 3

Transitions selected by Cox lasso model

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

Collection.Window.GA.in.Days
0.0233
1.02357
0.00928
2.51
0.012

AFTECCVVASQLR_770.87_574.3
1.07568
2.93198
0.84554
1.27
0.203

ELLESYIDGR_597.8_710.3
1.3847
3.99365
0.70784
1.96
0.05

ITLPDFTGDLR_624.34_920.4
0.814
2.25691
0.40652
2
0.045

TABLE 4

Area under the ROC (AUROC) curve for individual

analytes to discriminate pre-term birth subjects

from non-pre-term birth subjects. The 77

transitions with the highest AUROC area are shown.

Transition
AUROC

ELLESYIDGR_597.8_710.3
0.71

AFTECCVVASQLR_770.87_574.3
0.70

ITLPDFTGDLR_624.34_920.4
0.70

IRPFFPQQ_516.79_661.4
0.68

TDAPDLPEENQAR_728.34_613.3
0.67

ITLPDFTGDLR_624.34_288.2
0.67

ELLESYIDGR_597.8_839.4
0.67

SFRPFVPR_335.86_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

LQGTLPVEAR_542.31_571.3
0.66

VEPLYELVTATDFAYSSTVR_754.38_712.4
0.66

DPDQTDGLGLSYLSSHIANVER_796.39_328.1
0.66

VTGWGNLK_437.74_617.3
0.65

ALQDQLVLVAAK_634.88_289.2
0.65

EAQLPVIENK_570.82_329.1
0.65

VRPQQLVK_484.31_609.3
0.65

AFTECCVVASQLR_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

LSSPAVITDK_515.79_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

VILGAHQEVNLEPHVQEIEVSR_832.78_860.4
0.62

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

DPDQTDGLGLSYLSSHIANVER_796.39_456.2
0.61

FNAVLTNPQGDYDTSTGK_964.46_262.1
0.61

SPEQQETVLDGNLIIR_906.48_685.4
0.61

ALNHLPLEYNSALYSR_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

AITPPHPASQANIIFDITEGNLR_825.77_459.3
0.60

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

FTFTLHLETPKPSISSSNLNPR_829.44_787.4
0.60

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
ELLESYIDGR_597.8_710.3
AFTECCVVASQL
AFTECCVVASQ
ALQDQLVLVA

R_770.87_574.3
LR_770.87_574.3
AK_634.88_289.2

2
TATSEYQTFF
DPDQTDGLGLSY
ISLLLIESWLEP
AVLTIDEK_444.76_605.3

NPR_781.37_386.2
LSSHIANVER_796.39_328.1
VR_834.49_371.2

3
ITLPDFTGDLR_624.34_920.4
ELLESYIDGR_597.8_710.3
LPTAVVPLR_483.31_385.3
Collection.Window.G

A.in.Days

4
AFTECCVVAS
TATSEYQTFFNPR_781.37_386.2
ALQDQLVLVA
AHYDLR_387.7_566.3

QLR_770.87_574.3

AK_634.88_289.2

5
VEPLYELVTA
ITLPDFTGDLR_624.34_920.4
ETAASLLQAG
AEAQAQYSAAVA

TDFAYSSTVR_754.38_712.4

YK_626.33_679.4
K_654.33_908.5

6
GSFALSFPVES
GGEIEGFR_432.71_379.2
IITGLLEFEVYLEYL
AEAQAQYSAAVA

DVAPIAR_931.99_363.2

QNR_738.4_530.3
K_654.33_709.4

7
VGEYSLYIGR_578.8_871.5
ALQDQLVLVAAK_634.88_289.2
ADSQAQLLLSTVV
ADSQAQLLLSTVV

GVFTAPGLHLK_822.46_983.6
GVFTAPGLHLK_822.46_983.6

8
SFRPFVPR_335.86_635.3
VGEYSLYIGR_578.8_871.5
SLPVSDSVLSGFEQ
AITPPHPASQANIIF

R_810.92_723.3
DITEGNLR_825.77_459.3

9
ALQDQLVLVA
VEPLYELVTATD
SFRPFVPR_335.86_272.2
ADSQAQLLLSTVV

AK_634.88_289.2
FAYSSTVR_754.38_712.4

GVFTAPGLHLK_822.46_664.4

10
EDTPNSVWEP
SPEQQETVLDGN
IIGGSDADIK_494.77_260.2
AYSDLSR_406.2_375.2

AK_686.82_315.2
LIIR_906.48_685.4

11
YGFYTHVFR_397.2_421.3
YEFLNGR_449.72_293.1
NADYSYSVWK_616.78_333.2
DALSSVQESQVAQ

QAR_572.96_672.4

12
DPDQTDGLGL
LEQGENVFLQAT
GSFALSFPVESDVA
ANRPFLVFIR_411.58_435.3

SYLSSHIANVE
DK_796.4_822.4
PIAR_931.99_456.3

R_796.39_328.1

13
LEQGENVFLQ
LQGTLPVEAR_542.31_571.3
LSSPAVITDK_515.79_743.4
DALSSVQESQVAQ

ATDK_796.4_822.4

QAR_572.96_502.3

14
LQGTLPVEAR_542.31_571.3
ISLLLIESWLEP
ELPEHTVK_476.76_347.2
ALEQDLPVNIK_620.35_570.4

VR_834.49_371.2

15
SFRPFVPR_335.86_272.2
TASDFITK_441.73_781.4
EAQLPVIENK_570.82_699.4
AVLTIDEK_444.76_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:

AFTECCVVASQLR_770.87_574.3

ELLESYIDGR_597.8_710.3

ITLPDFTGDLR_624.34_920.4

TDAPDLPEENQAR_728.34_613.3

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

Transition
Protein
Annotation
p-value

DFHINLFQVLPWLK
CFAB_HUMAN
Complement factor B
0.006729512

ITLPDFTGDLR
LBP_HUMAN
Lipopolysaccharide-
0.012907017

binding protein

WWGGQPLWITATK
ENPP2_HUMAN
Ectonucleotide
0.013346

pyrophosphatase/

phosphodiesterase family

member 2

TASDFITK
GELS_HUMAN
Gelsolin
0.013841221

AGLLRPDYALLGHR
PGRP2_HUMAN
N-acetylmuramoyl-L-
0.014241979

alanine amidase

FLQEQGHR
CO8G_HUMAN
Complement
0.014339596

component C8 gamma

chain

FLNWIK
HABP2_HUMAN
Hyaluronan-binding
0.014790418

protein 2

EKPAGGIPVLGSLVNTVLK
BPIB1_HUMAN
BPI fold-containing
0.019027746

family B member 1

ITGFLKPGK
LBP_HUMAN
Lipopolysaccharide-
0.019836986

binding protein

YGLVTYATYPK
CFAB_HUMAN
Complement factor B
0.019927774

SLLQPNK
CO8A_HUMAN
Complement
0.020930939

component C8 alpha

chain

DISEVVTPR
CFAB_HUMAN
Complement factor B
0.021738046

VQEAHLTEDQIFYFPK
CO8G_HUMAN
Complement
0.021924548

component C8 gamma

chain

SPELQAEAK
APOA2_HUMAN
Apolipoprotein A-II
0.025944285

TYLHTYESEI
ENPP2_HUMAN
Ectonucleotide
0.026150038

pyrophosphatase/

phosphodiesterase family

member 2

DSPSVWAAVPGK
PROF1_HUMAN
Profilin-1
0.026607371

HYINLITR
NPY_HUMAN
Pro-neuropeptide Y
0.027432804

SLPVSDSVLSGFEQR
CO8G_HUMAN
Complement
0.029647857

component C8 gamma

chain

IPGIFELGISSQSDR
CO8B_HUMAN
Complement
0.030430996

component C8 beta

chain

IQTHSTTYR
F13B_HUMAN
Coagulation factor XIII
0.031667664

B chain

DGSPDVTTADIGANTPDA
PGRP2_HUMAN
N-acetylmuramoyl-L-
0.034738338

TK

alanine amidase

QLGLPGPPDVPDHAAYHPF
ITIH4_HUMAN
Inter-alpha-trypsin
0.043130591

inhibitor heavy chain

H4

FPLGSYTIQNIVAGSTYLF
LCAP_HUMAN
Leucyl-cystinyl
0.044698045

STK

aminopeptidase

AHYDLR
FETUA_HUMAN
Alpha-2-HS-
0.046259201

glycoprotein

SFRPFVPR
LBP_HUMAN
Lipopolysaccharide-
0.047948847

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

Protein Description
Uniprot ID (name)
Peptide
S_AUC

5′-AMP-activated
Q9UGI9 (AAKG3_HUMAN)
K.LVIFDTM*LEIK.K
0.78

protein kinase

subunit gamma-3

afamin precursor
P43652 (AFAM_HUMAN)
K. FIEDNIEYITIIAFAQYVQEATFEEME
0.79

K.L

afamin precursor
P43652 (AFAM_HUMAN)
K.IAPQLSTEELVSLGEK.M
0.71

afamin precursor
P43652 (AFAM_HUMAN)
K.LKHELTDEELQSLFTNFANVVDK.C
0.60

afamin precursor
P43652 (AFAM_HUMAN)
K.LPNNVLQEK.I
0.60

afamin precursor
P43652 (AFAM_HUMAN)
K.SDVGFLPPFPTLDPEEK.C
0.71

afamin precursor
P43652 (AFAM_HUMAN)
K.VMNHICSK.Q
0.68

afamin precursor
P43652 (AFAM_HUMAN)
R.ESLLNHFLYEVAR.R
0.69

afamin precursor
P43652 (AFAM_HUMAN)
R.LCFFYNKK.S
0.69

alpha-1-
P01011 (AACT_HUMAN)
K.AVLDVFEEGTEASAATAVK.I
0.72

antichymotrypsin

precursor

alpha-1-
P01011 (AACT_HUMAN)
K.EQLSLLDR.F
0.65

antichymotrypsin

precursor

alpha-1-
P01011 (AACT_HUMAN)
K.EQLSLLDRFTEDAK.R
0.64

antichymotrypsin

precursor

alpha-1-
P01011 (AACT_HUMAN)
K.EQLSLLDRFTEDAKR.L
0.60

antichymotrypsin

precursor

alpha-1-
P01011 (AACT_HUMAN)
K.ITDLIKDLDSQTMM*VLVNYIFFK.A
0.65

antichymotrypsin

precursor

alpha-1-
P01011 (AACT_HUMAN)
K.ITLLSALVETR.T
0.62

antichymotrypsin

precursor

alpha-1-
P01011 (AACT_HUMAN)
K.RLYGSEAFATDFQDSAAAK.K
0.62

antichymotrypsin

precursor

alpha-1-
P01011 (AACT_HUMAN)
R.EIGELYLPK.F
0.65

antichymotrypsin

precursor

alpha-1B-
P04217 (A1BG_HUMAN)
R.CEGPIPDVTFELLR.E
0.67

glycoprotein

precursor

alpha-1B-
P04217 (A1BG_HUMAN)
R.FALVR.E
0.79

glycoprotein

precursor

alpha-2-antiplasmin
P08697 (A2AP_HUMAN)
K.SPPGVCSR.D
0.81

isoform a precursor

alpha-2-antiplasmin
P08697 (A2AP_HUMAN)
R.DSFHLDEQFTVPVEMMQAR.T
0.69

isoform a precursor

alpha-2-HS-
P02765 (FETUA_HUMAN)
K.CNLLAEK.Q
0.67

glycoprotein

preproprotein

alpha-2-HS-
P02765 (FETUA_HUMAN)
K.EHAVEGDCDFQLLK.L
0.67

glycoprotein

preproprotein

alpha-2-HS-
P02765 (FETUA_HUMAN)
K.HTLNQIDEVKVWPQQPSGELFEIEID
0.64

glycoprotein

TLETTCHVLDPTPVAR.C

preproprotein

alpha-2-
P01023 (A2MG_HUMAN)
K.MVSGFIPLKPTVK.M
0.73

macroglobulin

precursor

alpha-2-
P01023 (A2MG_HUMAN)
R.AFQPFFVELTM*PYSVIR.G
0.68

macroglobulin

precursor

alpha-2-
P01023 (A2MG_HUMAN)
R.AFQPFFVELTMPYSVIR.G
0.62

macroglobulin

precursor

alpha-2-
P01023 (A2MG_HUMAN)
R.NQGNTWLTAFVLK.T
0.73

macroglobulin

precursor

angiotensinogen
P01019 (ANGT_HUMAN)
K.IDRFMQAVTGWK.T
0.81

preproprotein

angiotensinogen
P01019 (ANGT_HUMAN)
K.LDTEDKLR.A
0.72

preproprotein

angiotensinogen
P01019 (ANGT_HUMAN)
K.TGCSLMGASVDSTLAFNTYVHFQGK
0.64

preproprotein

.M

angiotensinogen
P01019 (ANGT_HUMAN)
R.AAMVGMLANFLGFR.I
0.62

preproprotein

antithrombin-III
P01008 (ANT3_HUMAN)
K.NDNDNIFLSPLSISTAFAMTK.L
0.64

precursor

antithrombin-III
P01008 (ANT3_HUMAN)
K.SKLPGIVAEGRDDLYVSDAFHK.A
0.81

precursor

antithrombin-III
P01008 (ANT3_HUMAN)
R.EVPLNTIIFMGR.V
0.61

precursor

antithrombin-III
P01008 (ANT3_HUMAN)
R.FATTFYQHLADSKNDNDNIFLSPLSIS
0.66

precursor

TAFAMTK.L

antithrombin-III
P01008 (ANT3_HUMAN)
R.ITDVIPSEAINELTVLVLVNTIYFK.G
0.60

precursor

antithrombin-III
P01008 (ANT3_HUMAN)
R.RVWELSK.A
0.63

precursor

antithrombin-III
P01008 (ANT3_HUMAN)
R.VAEGTQVLELPFKGDDITM*VLILPK
0.62

precursor

PEK.S

antithrombin-III
P01008 (ANT3_HUMAN)
R.VAEGTQVLELPFKGDDITMVLILPKP
0.62

precursor

EK.S

apolipoprotein A-II
P02652 (APOA2_HUMAN)
K.AGTELVNFLSYFVELGTQPATQ.-
0.61

preproprotein

apolipoprotein A-II
P02652 (APOA2_HUMAN)
K.EPCVESLVSQYFQTVTDYGK.D
0.63

preproprotein

apolipoprotein A-IV
P06727 (APOA4_HUMAN)
K.ALVQQMEQLR.Q
0.61

precursor

apolipoprotein A-IV
P06727 (APOA4_HUMAN)
K.LGPHAGDVEGHLSFLEK.D
0.61

precursor

apolipoprotein A-IV
P06727 (APOA4_HUMAN)
K.SELTQQLNALFQDK.L
0.71

precursor

apolipoprotein A-IV
P06727 (APOA4_HUMAN)
K.SLAELGGHLDQQVEEFRR.R
0.61

precursor

apolipoprotein A-IV
P06727 (APOA4_HUMAN)
K.VKIDQTVEELRR.S
0.75

precursor

apolipoprotein A-IV
P06727 (APOA4_HUMAN)
K.VNSFFSTFK.E
0.63

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
K.ATFQTPDFIVPLTDLR.I
0.65

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
K.AVSM*PSFSILGSDVR.V
0.65

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
K.AVSMPSFSILGSDVR.V
0.67

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
K.EQHLFLPFSYK.N
0.65

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
K.KIISDYHQQFR.Y
0.63

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
K.QVFLYPEKDEPTYILNIK.R
0.64

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
K.SPAFTDLHLR.Y
0.69

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
K.TILGTMPAFEVSLQALQK.A
0.62

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
K.VLADKFIIPGLK.L
0.72

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
K.YSQPEDSLIPFFEITVPESQLTVSQFTL
0.61

precursor

PK.S

apolipoprotein B-100
P04114 (APOB_HUMAN)
R.DLKVEDIPLAR.I
0.64

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
R.GIISALLVPPETEEAK.Q
0.81

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
R.ILGEELGFASLHDLQLLGK.L
0.62

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
R.LELELRPTGEIEQYSVSATYELQR.E
0.60

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
R.NIQEYLSILTDPDGK.G
0.68

precursor

apolipoprotein B-100
P04114 (APOB_HUMAN)
R.TFQIPGYTVPVVNVEVSPFTIEMSAF
0.75

precursor

GYVFPK.A

apolipoprotein B-100
P04114 (APOB_HUMAN)
R.TIDQMLNSELQWPVPDIYLR.D
0.70

precursor

apolipoprotein C-I
P02654 (APOC1_HUMAN)
K.MREWFSETFQK.V
0.61

precursor

apolipoprotein C-II
P02655 (APOC2_HUMAN)
K.STAAMSTYTGIFTDQVLSVLKGEE.-
0.61

precursor

apolipoprotein C-III
P02656 (APOC3_HUMAN)
R.GWVTDGFSSLK.D
0.62

precursor

apolipoprotein E
P02649 (APOE_HUMAN)
R.AATVGSLAGQPLQER.A
0.61

precursor

apolipoprotein E
P02649 (APOE_HUMAN)
R.LKSWFEPLVEDMQR.Q
0.65

precursor

apolipoprotein E
P02649 (APOE_HUMAN)
R.WVQTLSEQVQEELLSSQVTQELR.A
0.64

precursor

ATP-binding cassette
O14678 (ABCD4_HUMAN)
K.LCGGGRWELM*R.I
0.60

sub-family D member 4

ATP-binding cassette
Q9NUQ8 (ABCF3_HUMAN)
K.LPGLLK.R
0.73

sub-family F member 3

beta-2-glycoprotein 1
P02749 (APOH_HUMAN)
K.EHSSLAFWK.T
0.64

precursor

beta-2-glycoprotein 1
P02749 (APOH_HUMAN)
R.TCPKPDDLPFSTVVPLK.T
0.60

precursor

beta-2-glycoprotein 1
P02749 (APOH_HUMAN)
R.VCPFAGILENGAVR.Y
0.68

precursor

beta-Ala-His
Q96KN2 (CNDP1_HUMAN)
K.LFAAFFLEMAQLH.-
0.68

dipeptidase

precursor

biotinidase precursor
P43251 (BTD_HUMAN)
K.SHLIIAQVAK.N
0.62

carboxypeptidase B2
Q96IY4 (CBPB2_HUMAN)
K.NAIWIDCGIHAR.E
0.62

preproprotein

carboxypeptidase N
P15169 (CBPN_HUMAN)
R.EALIQFLEQVHQGIK.G
0.69

catalytic chain

precursor

carboxypeptidase N
P22792 (CPN2_HUMAN)
R.LLNIQTYCAGPAYLK.G
0.62

subunit 2 precursor

catalase
P04040 (CATA_HUMAN)
R.LCENIAGHLKDAQIFIQK.K
0.62

ceruloplasmin
P00450 (CERU_HUMAN)
K.AETGDKVYVHLK.N
0.61

precursor

ceruloplasmin
P00450 (CERU_HUMAN)
K.AGLQAFFQVQECNK.S
0.62

precursor

ceruloplasmin
P00450 (CERU_HUMAN)
K.DIASGLIGPLIICK.K
0.63

precursor

ceruloplasmin
P00450 (CERU_HUMAN)
K.DIFTGLIGPM*K.I
0.63

precursor

ceruloplasmin
P00450 (CERU_HUMAN)
K.DIFTGLIGPMK.I
0.68

precursor

ceruloplasmin
P00450 (CERU_HUMAN)
K.M*YYSAVDPTKDIFTGLIGPMK.I
0.62

precursor

ceruloplasmin
P00450 (CERU_HUMAN)
K.MYYSAVDPTKDIFTGLIGPM*K.I
0.63

precursor

ceruloplasmin
P00450 (CERU_HUMAN)
K.PVWLGFLGPIIK.A
0.63

precursor

ceruloplasmin
P00450 (CERU_HUMAN)
R.ADDKVYPGEQYTYMLLATEEQSPGE
0.64

precursor

GDGNCVTR.I

ceruloplasmin
P00450 (CERU_HUMAN)
R.DTANLFPQTSLTLHM*WPDTEGTF
0.71

precursor

NVECLTTDHYTGGMK.Q

ceruloplasmin
P00450 (CERU_HUMAN)
R.DTANLFPQTSLTLHMWPDTEGTFN
0.68

precursor

VECLTTDHYTGGMK.Q

ceruloplasmin
P00450 (CERU_HUMAN)
R.FNKNNEGTYYSPNYNPQSR.S
0.74

precursor

ceruloplasmin
P00450 (CERU_HUMAN)
R.IDTINLFPATLFDAYM*VAQNPGEW
0.75

precursor

M*LSCQNLNHLK.A

ceruloplasmin
P00450 (CERU_HUMAN)
R.IDTINLFPATLFDAYM*VAQNPGEW
0.86

precursor

MLSCQNLNHLK.A

ceruloplasmin
P00450 (CERU_HUMAN)
R.IDTINLFPATLFDAYMVAQNPGEW
0.60

precursor

M*LSCQNLNHLK.A

ceruloplasmin
P00450 (CERU_HUMAN)
R.KAEEEHLGILGPQLHADVGDKVK.I
0.71

precursor

ceruloplasmin
P00450 (CERU_HUMAN)
R.TTIEKPVWLGFLGPIIK.A
0.63

precursor

cholinesterase
P06276 (CHLE_HUMAN)
R.FWTSFFPK.V
0.76

precursor

clusterin
P10909 (CLUS_HUMAN)
K.LFDSDPITVTVPVEVSR.K
0.78

preproprotein

clusterin
P10909 (CLUS_HUMAN)
R.ASSIIDELFQDR.F
0.68

preproprotein

coagulation factor IX
P00740 (FA9_HUMAN)
K.WIVTAAHCVETGVK.I
0.60

preproprotein

coagulation factor VII
P08709 (FA7_HUMAN)
R.FSLVSGWGQLLDR.G
0.78

isoform a

preproprotein

coagulation factor X
P00742 (FA10_HUMAN)
K.ETYDFDIAVLR.L
0.75

preproprotein

coiled-coil domain-
Q8IYE1 (CCD13_HUMAN)
K.VRQLEMEIGQLNVHYLR.N
0.67

containing protein 13

complement C1q
P02745 (C1QA_HUMAN)
R.PAFSAIR.R
0.66

subcomponent

subunit A precursor

complement C1q
P02746 (C1QB_HUMAN)
K.VVTFCDYAYNTFQVTTGGMVLK.L
0.63

subcomponent

subunit B precursor

complement C1q
P02747 (C1QC_HUMAN)
K.FQSVFTVTR.Q
0.63

subcomponent

subunit C precursor

complement C1r
P00736 (C1R_HUMAN)
K.TLDEFTIIQNLQPQYQFR.D
0.62

subcomponent

precursor

complement C1r
P00736 (C1R_HUMAN)
R.MDVFSQNMFCAGHPSLK.Q
0.68

subcomponent

precursor

complement C1r
P00736 (C1R_HUMAN)
R.WILTAAHTLYPK.E
0.74

subcomponent

precursor

complement C1s
P09871 (C1S_HUMAN)
K.FYAAGLVSWGPQCGTYGLYTR.V
0.68

subcomponent

precursor

complement C1s
P09871 (C1S_HUMAN)
K.GFQVVVTLR.R
0.63

subcomponent

precursor

complement C2
P06681 (CO2_HUMAN)
R.GALISDQWVLTAAHCFR.D
0.61

isoform 3

complement C2
P06681 (CO2_HUMAN)
R.PICLPCTMEANLALR.R
0.66

isoform 3

complement C3
P01024 (CO3_HUMAN)
R.YYGGGYGSTQATFMVFQALAQYQK
0.75

precursor

.D

complement C4-A
P0C0L4 (CO4A_HUMAN)
K.GLCVATPVQLR.V
0.74

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
K.M*RPSTDTITVM*VENSHGLR.V
0.83

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
K.MRPSTDTITVM*VENSHGLR.V
0.72

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
K.VGLSGM*AIADVTLLSGFHALR.A
0.71

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
K.VLSLAQEQVGGSPEK.L
0.63

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.EMSGSPASGIPVK.V
0.65

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.GCGEQTM*IYLAPTLAASR.Y
0.75

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.GLQDEDGYR.M
0.75

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.GQIVFMNREPK.R
0.93

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.KKEVYM*PSSIFQDDFVIPDISEPGT
0.72

isoform 1

WK.I

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.LPMSVR.R
0.78

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.LTVAAPPSGGPGFLSIER.P
0.84

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.NFLVR.A
0.75

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.NGESVKLHLETDSLALVALGALDTAL
0.88

isoform 1

YAAGSK.S

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.QGSFQGGFR.S
0.60

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.TLEIPGNSDPNMIPDGDFNSYVR.V
0.69

isoform 1

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.VTASDPLDTLGSEGALSPGGVASLLR
0.63

isoform 1

.L

complement C4-A
P0C0L4 (CO4A_HUMAN)
R.YLDKTEQWSTLPPETK.D
0.67

isoform 1

complement C5
P01031 (CO5_HUMAN)
K.ADNFLLENTLPAQSTFTLAISAYALSL
0.63

preproprotein

GDK.T

complement C5
P01031 (CO5_HUMAN)
K.ALVEGVDQLFTDYQIK.D
0.63

preproprotein

complement C5
P01031 (CO5_HUMAN)
K.DGHVILQLNSIPSSDFLCVR.F
0.62

preproprotein

complement C5
P01031 (CO5_HUMAN)
K.DVFLEMNIPYSVVR.G
0.63

preproprotein

complement C5
P01031 (CO5_HUMAN)
K.EFPYRIPLDLVPK.T
0.60

preproprotein

complement C5
P01031 (CO5_HUMAN)
K.FQNSAILTIQPK.Q
0.67

preproprotein

complement C5
P01031 (CO5_HUMAN)
K.VFKDVFLEMNIPYSVVR.G
0.63

preproprotein

complement C5
P01031 (CO5_HUMAN)
R.VFQFLEK.S
0.61

preproprotein

complement
P13671 (CO6_HUMAN)
K.DLHLSDVFLK.A
0.60

component C6

precursor

complement
P13671 (CO6_HUMAN)
R.TECIKPVVQEVLTITPFQR.L
0.62

component C6

precursor

complement
P10643 (CO7_HUMAN)
K.SSGWHFVVK.F
0.61

component C7

precursor

complement
P10643 (CO7_HUMAN)
R.ILPLTVCK.M
0.75

component C7

precursor

complement
P07357 (CO8A_HUMAN)
R.ALDQYLMEFNACR.C
0.65

component C8 alpha

chain precursor

complement
P07360 (CO8G_HUMAN)
K.YGFCEAADQFHVLDEVR.R
0.60

component C8

gamma chain

precursor

complement
P02748 (CO9_HUMAN)
R.AIEDYINEFSVRK.0
0.69

component C9

precursor

complement
P02748 (CO9_HUMAN)
R.TAGYGINILGMDPLSTPFDNEFYNGL
0.69

component C9

CNR.D

precursor

complement factor B
P00751 (CFAB_HUMAN)
K.ALFVSEEEKK.L
0.64

preproprotein

complement factor B
P00751 (CFAB_HUMAN)
K.CLVNLIEK.V
0.70

preproprotein

complement factor B
P00751 (CFAB_HUMAN)
K.EAGIPEFYDYDVALIK.L
0.66

preproprotein

complement factor B
P00751 (CFAB_HUMAN)
K.VSEADSSNADWVTK.Q
0.73

preproprotein

complement factor B
P00751 (CFAB_HUMAN)
K.YGQTIRPICLPCTEGTTR.A
0.67

preproprotein

complement factor B
P00751 (CFAB_HUMAN)
R.DLEIEVVLFHPNYNINGK.K
0.71

preproprotein

complement factor B
P00751 (CFAB_HUMAN)
R.FLCTGGVSPYADPNTCR.G
0.64

preproprotein

complement factor H
P08603 (CFAH_HUMAN)
K.DGWSAQPTCIK.S
0.80

isoform a precursor

complement factor H
P08603 (CFAH_HUMAN)
K.EGWIHTVCINGR.W
0.67

isoform a precursor

complement factor H
P08603 (CFAH_HUMAN)
K.TDCLSLPSFENAIPMGEK.K
0.61

isoform a precursor

complement factor H
P08603 (CFAH_HUMAN)
R.DTSCVNPPTVQNAYIVSR.Q
0.60

isoform a precursor

complement factor H
P08603 (CFAH_HUMAN)
K.CTSTGWIPAPR.0
0.68

isoform b precursor

complement factor H
P08603 (CFAH_HUMAN)
K.IIYKENER.F
0.76

isoform b precursor

complement factor H
P08603 (CFAH_HUMAN)
K.IVSSAM*EPDREYHFGQAVR.F
0.75

isoform b precursor

complement factor H
P08603 (CFAH_HUMAN)
K.IVSSAMEPDREYHFGQAVR.F
0.68

isoform b precursor

complement factor H
P08603 (CFAH_HUMAN)
R.CTLKPCDYPDIK.H
0.81

isoform b precursor

complement factor H
P08603 (CFAH_HUMAN)
R.KGEWVALNPLR.K
0.60

isoform b precursor

complement factor H
P08603 (CFAH_HUMAN)
R.KGEWVALNPLRK.0
0.69

isoform b precursor

complement factor H
P08603 (CFAH_HUMAN)
R.RPYFPVAVGK.Y
0.68

isoform b precursor

complement factor
Q03591 (FHR1_HUMAN)
R.EIMENYNIALR.W
0.64

H-related protein 1

precursor

complement factor I
P05156 (CFAI_HUMAN)
K.DASGITCGGIYIGGCWILTAAHCLR.A
0.71

preproprotein

complement factor I
P05156 (CFAI_HUMAN)
K.VANYFDWISYHVGR.P
0.72

preproprotein

complement factor I
P05156 (CFAI_HUMAN)
R.IIFHENYNAGTYQNDIALIEMK.K
0.63

preproprotein

complement factor I
P05156 (CFAI_HUMAN)
R.YQIWTTVVDWIHPDLK.R
0.63

preproprotein

conserved oligomeric
Q9Y2V7 (COG6_HUMAN)
K.ISNLLK.F
0.65

Golgi complex

subunit 6 isoform

corticosteroid-
P08185 (CBG_HUMAN)
R.WSAGLTSSQVDLYIPK.V
0.62

binding globulin

precursor

C-reactive protein
P02741 (CRP_HUMAN)
K.YEVQGEVFTKPQLWP.-
0.60

precursor

dopamine beta-
P09172 (DOPO_HUMAN)
R.HVLAAWALGAK.A
0.88

hydroxylase

precursor

double-stranded
Q9NS39 (RED2_HUMAN)
R.AGLRYVCLAEPAER.R
0.75

RNA-specific editase

B2

dual oxidase 2
Q9NRD8 (DUOX2_HUMAN)
R.FTQLCVKGGGGGGNGIR.D
0.65

precursor

FERM domain-
Q9BZ67 (FRMD8_HUMAN)
R.VQLGPYQPGRPAACDLR.E
0.65

containing protein 8

fetuin-B precursor
Q9UGM5 (FETUB_HUMAN)
R.GGLGSLFYLTLDVLETDCHVLR.K
0.83

ficolin-3 isoform 1
O75636 (FCN3_HUMAN)
R.ELLSQGATLSGWYHLCLPEGR.A
0.69

precursor

gastric intrinsic factor
P27352 (IF_HUMAN)
K.KTTDM*ILNEIKQGK.F
0.60

precursor

gelsolin isoform d
P06396 (GELS_HUMAN)
K.NWRDPDQTDGLGLSYLSSHIANVER
0.72

.V

gelsolin isoform d
P06396 (GELS_HUMAN)
K.TPSAAYLWVGTGASEAEK.T
0.80

gelsolin isoform d
P06396 (GELS_HUMAN)
R.VEKFDLVPVPTNLYGDFFTGDAYVIL
0.60

K.T

gelsolin isoform d
P06396 (GELS_HUMAN)
R.VPFDAATLHTSTAMAAQHGMDDD
0.67

GTGQK.Q

glutathione
P22352 (GPX3_HUMAN)
K.FYTFLK.N
0.63

peroxidase 3

precursor

hemopexin precursor
P02790 (HEMO_HUMAN)
K.GDKVWVYPPEKK.E
0.65

hemopexin precursor
P02790 (HEMO_HUMAN)
K.LLQDEFPGIPSPLDAAVECHR.G
0.71

hemopexin precursor
P02790 (HEMO_HUMAN)
K.SGAQATWTELPWPHEK.V
0.64

hemopexin precursor
P02790 (HEMO_HUMAN)
K.SGAQATWTELPWPHEKVDGALCM
0.61

EK.S

hemopexin precursor
P02790 (HEMO_HUMAN)
K.VDGALCMEK.S
0.66

hemopexin precursor
P02790 (HEMO_HUMAN)
R.DYFMPCPGR.G
0.68

hemopexin precursor
P02790 (HEMO_HUMAN)
R.EWFWDLATGTM*K.E
0.64

hemopexin precursor
P02790 (HEMO_HUMAN)
R.QGHNSVFLIK.G
0.71

heparin cofactor 2
P05546 (HEP2_HUMAN)
K.HQGTITVNEEGTQATTVTTVGFMPL
0.60

precursor

STQVR.F

heparin cofactor 2
P05546 (HEP2_HUMAN)
K.YEITTIHNLFR.K
0.62

precursor

heparin cofactor 2
P05546 (HEP2_HUMAN)
R.LNILNAK.F
0.68

precursor

heparin cofactor 2
P05546 (HEP2_HUMAN)
R.NFGYTLR.S
0.64

precursor

heparin cofactor 2
P05546 (HEP2_HUMAN)
R.VLKDQVNTFDNIFIAPVGISTAMGM
0.63

precursor

*ISLGLK.G

hepatocyte cell
Q14CZ8 (HECAM_HUMAN)
K.PLLNDSRMLLSPDQK.V
0.61

adhesion molecule

precursor

hepatocyte growth
Q04756 (HGFA_HUMAN)
R.VQLSPDLLATLPEPASPGR.Q
0.82

factor activator

preproprotein

histidine-rich
P04196 (HRG_HUMAN)
R.DGYLFQLLR.I
0.63

glycoprotein

precursor

hyaluronan-binding
Q14520 (HABP2_HUMAN)
K.FLNWIK.A
0.82

protein 2 isoform 1

preproprotein

hyaluronan-binding
Q14520 (HABP2_HUMAN)
K.LKPVDGHCALESK.Y
0.61

protein 2 isoform 1

preproprotein

hyaluronan-binding
Q14520 (HABP2_HUMAN)
K.RPGVYTQVTK.F
0.74

protein 2 isoform 1

preproprotein

inactive caspase-12
Q6UXS9 (CASPC_HUMAN)
K.AGADTHGRLLQGNICNDAVTK.A
0.74

insulin-degrading
P14735 (IDE_HUMAN)
K.KIIEKM*ATFEIDEK.R
0.85

enzyme isoform 1

insulin-like growth
P35858 (ALS_HUMAN)
R.SFEGLGQLEVLTLDHNQLQEVK.A
0.62

factor-binding

protein complex acid

labile subunit isoform

2 precursor

inter-alpha-trypsin
P19827 (ITIH1_HUMAN)
K.ELAAQTIKK.S
0.81

inhibitor heavy chain

H1 isoform a

precursor

inter-alpha-trypsin
P19827 (ITIH1_HUMAN)
K.GSLVQASEANLQAAQDFVR.G
0.71

inhibitor heavy chain

H1 isoform a

precursor

inter-alpha-trypsin
P19827 (ITIH1_HUMAN)
K.QLVHHFEIDVDIFEPQGISK.L
0.70

inhibitor heavy chain

H1 isoform a

precursor

inter-alpha-trypsin
P19827 (ITIH1_HUMAN)
K.QYYEGSEIVVAGR.I
0.83

inhibitor heavy chain

H1 isoform a

precursor

inter-alpha-trypsin
P19827 (ITIH1_HUMAN)
R.EVAFDLEIPKTAFISDFAVTADGNAFI
0.70

inhibitor heavy chain

GDIK.D

H1 isoform a

precursor

inter-alpha-trypsin
P19827 (ITIH1_HUMAN)
R.GMADQDGLKPTIDKPSEDSPPLEM*
0.63

inhibitor heavy chain

LGPR.R

H1 isoform a

precursor

inter-alpha-trypsin
P19827 (ITIH1_HUMAN)
R.GMADQDGLKPTIDKPSEDSPPLEML
0.60

inhibitor heavy chain

GPR.R

H1 isoform a

precursor

inter-alpha-trypsin
P19823 (ITIH2_HUMAN)
K.FDPAKLDQIESVITATSANTQLVLETL
0.80

inhibitor heavy chain

AQM*DDLQDFLSK.D

H2 precursor

inter-alpha-trypsin
P19823 (ITIH2_HUMAN)
K.KFYNQVSTPLLR.N
0.76

inhibitor heavy chain

H2 precursor

inter-alpha-trypsin
P19823 (ITIH2_HUMAN)
K.NILFVIDVSGSM*WGVK.M
0.68

inhibitor heavy chain

H2 precursor

inter-alpha-trypsin
P19823 (ITIH2_HUMAN)
K.NILFVIDVSGSMWGVK.M
0.62

inhibitor heavy chain

H2 precursor

inter-alpha-trypsin
P19823 (ITIH2_HUMAN)
R.KLGSYEHR.I
0.72

inhibitor heavy chain

H2 precursor

inter-alpha-trypsin
P19823 (ITIH2_HUMAN)
R.LSNENHGIAQR.I
0.66

inhibitor heavy chain

H2 precursor

inter-alpha-trypsin
P19823 (ITIH2_HUMAN)
R.MATTMIQSK.V
0.60

inhibitor heavy chain

H2 precursor

inter-alpha-trypsin
P19823 (ITIH2_HUMAN)
R.SILQM*SLDHHIVTPLTSLVIENEAG
0.63

inhibitor heavy chain

DER.M

H2 precursor

inter-alpha-trypsin
P19823 (ITIH2_HUMAN)
R.SILQMSLDHHIVTPLTSLVIENEAGDE
0.65

inhibitor heavy chain

R.M

H2 precursor

inter-alpha-trypsin
P19823 (ITIH2_HUMAN)
R.TEVNVLPGAK.V
0.69

inhibitor heavy chain

H2 precursor

inter-alpha-trypsin
Q14624 (ITIH4_HUMAN)
K.NVVFVIDK.S
0.68

inhibitor heavy chain

H4 isoform 1

precursor

inter-alpha-trypsin
Q14624 (ITIH4_HUMAN)
K.WKETLFSVMPGLK.M
0.65

inhibitor heavy chain

H4 isoform 1

precursor

inter-alpha-trypsin
Q14624 (ITIH4_HUMAN)
K.YIFHNFM*ER.L
0.67

inhibitor heavy chain

H4 isoform 1

precursor

inter-alpha-trypsin
Q14624 (ITIH4_HUMAN)
R.FAHTVVTSR.V
0.63

inhibitor heavy chain

H4 isoform 1

precursor

inter-alpha-trypsin
Q14624 (ITIH4_HUMAN)
R.FKPTLSQQQK.S
0.60

inhibitor heavy chain

H4 isoform 1

precursor

inter-alpha-trypsin
Q14624 (ITIH4_HUMAN)
R.IHEDSDSALQLQDFYQEVANPLLTA
0.64

inhibitor heavy chain

VTFEYPSNAVEEVTQNNFR.L

H4 isoform 1

precursor

inter-alpha-trypsin
Q14624 (ITIH4_HUMAN)
R.MNFRPGVLSSR.Q
0.63

inhibitor heavy chain

H4 isoform 1

precursor

inter-alpha-trypsin
Q14624 (ITIH4_HUMAN)
R.NVHSAGAAGSR.M
0.62

inhibitor heavy chain

H4 isoform 1

precursor

inter-alpha-trypsin
Q14624 (ITIH4_HUMAN)
R.NVHSGSTFFK.Y
0.75

inhibitor heavy chain

H4 isoform 1

precursor

inter-alpha-trypsin
Q14624 (ITIH4_HUMAN)
R.RLGVYELLLK.V
0.66

inhibitor heavy chain

H4 isoform 1

precursor

kallistatin precursor
P29622 (KAIN_HUMAN)
K.KLELHLPK.F
0.78

kallistatin precursor
P29622 (KAIN_HUMAN)
R.EIEEVLTPEMLMR.W
0.60

kininogen-1 isoform 2
P01042 (KNG1_HUMAN)
K.AATGECTATVGKR.S
0.67

precursor

kininogen-1 isoform 2
P01042 (KNG1_HUMAN)
K.LGQSLDCNAEVYVVPWEK.K
0.72

precursor

kininogen-1 isoform 2
P01042 (KNG1_HUMAN)
K.YNSQNQSNNQFVLYR.I
0.62

precursor

kininogen-1 isoform 2
P01042 (KNG1_HUMAN)
R.QVVAGLNFR.I
0.64

precursor

leucine-rich alpha-2-
P02750 (A2GL_HUMAN)
K.DLLLPQPDLR.Y
0.64

glycoprotein

precursor

leucine-rich alpha-2-
P02750 (A2GL_HUMAN)
R.LHLEGNKLQVLGK.D
0.76

glycoprotein

precursor

leucine-rich alpha-2-
P02750 (A2GL_HUMAN)
R.TLDLGENQLETLPPDLLR.G
0.61

glycoprotein

precursor

lipopolysaccharide-
P18428 (LBP_HUMAN)
K.GLQYAAQEGLLALQSELLR.I
0.82

binding protein

precursor

lipopolysaccharide-
P18428 (LBP_HUMAN)
K.LAEGFPLPLLK.R
0.66

binding protein

precursor

lumican precursor
P51884 (LUM_HUMAN)
K.SLEYLDLSFNQIAR.L
0.65

lumican precursor
P51884 (LUM_HUMAN)
R.LKEDAVSAAFK.G
0.74

m7GpppX
Q96C86 (DCPS_HUMAN)
R.IVFENPDPSDGFVLIPDLK.W
0.62

diphosphatase

matrix
Q99542 (MMP19_HUMAN)
R.VYFFK.G
0.63

metalloproteinase-19

isoform 1

preproprotein

MBT domain-
Q05BQ5 (MBTD1_HUMAN)
K.WFDYLR.E
0.65

containing protein 1

monocyte
P08571 (CD14_HUMAN)
R.LTVGAAQVPAQLLVGALR.V
0.66

differentiation

antigen CD14

precursor

pappalysin-1
Q13219 (PAPP1_HUMAN)
R.VSFSSPLVAISGVALR.S
0.66

preproprotein

phosphatidylinositol-
P80108 (PHLD_HUMAN)
K.GIVAAFYSGPSLSDKEK.L
0.71

glycan-specific

phospholipase D

precursor

phosphatidylinositol-
P80108 (PHLD_HUMAN)
R.WYVPVKDLLGIYEK.L
0.71

glycan-specific

phospholipase D

precursor

pigment epithelium-
P36955 (PEDF_HUMAN)
K.LQSLFDSPDFSK.I
0.61

derived factor

precursor

pigment epithelium-
P36955 (PEDF_HUMAN)
R.ALYYDLISSPDIHGTYK.E
0.72

derived factor

precursor

plasma kallikrein
P03952 (KLKB1_HUMAN)
R.CLLFSFLPASSINDMEKR.F
0.60

preproprotein

plasma protease C1
P05155 (IC1_HUMAN)
K.FQPTLLTLPR.I
0.70

inhibitor precursor

plasma protease C1
P05155 (IC1_HUMAN)
K.GVTSVSQIFHSPDLAIR.D
0.66

inhibitor precursor

plasminogen isoform
P00747 (PLMN_HUMAN)
K.VIPACLPSPNYVVADR.T
0.63

1 precursor

plasminogen isoform
P00747 (PLMN_HUMAN)
R.FVTWIEGVMR.N
0.60

1 precursor

plasminogen isoform
P00747 (PLMN_HUMAN)
R.HSIFTPETNPR.A
0.63

1 precursor

platelet basic protein
P02775 (CXCL7_HUMAN)
K.GKEESLDSDLYAELR.C
0.70

preproprotein

platelet glycoprotein
P40197 (GPV_HUMAN)
K.MVLLEQLFLDHNALR.G
0.66

V precursor

platelet glycoprotein
P40197 (GPV_HUMAN)
R.LVSLDSGLLNSLGALTELQFHR.N
0.88

V precursor

pregnancy zone
P20742 (PZP_HUMAN)
K.ALLAYAFSLLGK.Q
0.66

protein precursor

pregnancy zone
P20742 (PZP_HUMAN)
K.DLFHCVSFTLPR.I
0.86

protein precursor

pregnancy zone
P20742 (PZP_HUMAN)
K.MLQITNTGFEMK.L
0.84

protein precursor

pregnancy zone
P20742 (PZP_HUMAN)
R.NELIPLIYLENPRR.N
0.65

protein precursor

pregnancy zone
P20742 (PZP_HUMAN)
R.SYIFIDEAHITQSLTWLSQMQK.D
0.68

protein precursor

pregnancy-specific
P11465 (PSG2_HUMAN)
R.SDPVTLNLLHGPDLPR.I
0.66

beta-1-glycoprotein 2

precursor

pregnancy-specific
Q16557 (PSG3_HUMAN)
R.TLFLFGVTK.Y
0.62

beta-1-glycoprotein 3

precursor

pregnancy-specific
Q15238 (PSG5_HUMAN)
R.ILILPSVTR.N
0.76

beta-1-glycoprotein 5

precursor

pregnancy-specific
Q00889 (PSG6_HUMAN)
R.SDPVTLNLLPK.L
0.63

beta-1-glycoprotein 6

isoform a

progesterone-
Q8WXW3 (PIBF1_HUMAN)
R.VLQLEK.Q
0.71

induced-blocking

factor 1

protein AMBP
P02760 (AMBP_HUMAN)
R.VVAQGVGIPEDSIFTMADR.G
0.60

preproprotein

protein CBFA2T2
O43439 (MTG8R_HUMAN)
R.LTEREWADEWKHLDHALNCIMEM
0.70

isoform MTGR1b

VEK.T

protein FAM98C
Q17RN3 (FA98C_HUMAN)
R.ALCGGDGAAALREPGAGLR.L
0.75

protein NLRC3
Q7RTR2 (NLRC3_HUMAN)
K.ALM*DLLAGKGSQGSQAPQALDR.T
0.92

protein Z-dependent
Q9UK55 (ZPI_HUMAN)
K.MGDHLALEDYLTTDLVETWLR.N
0.60

protease inhibitor

precursor

prothrombin
P00734 (THRB_HUMAN)
K.SPQELLCGASLISDR.W
0.84

preproprotein

prothrombin
P00734 (THRB_HUMAN)
R.LAVTTHGLPCLAWASAQAK.A
0.62

preproprotein

prothrombin
P00734 (THRB_HUMAN)
R.SEGSSVNLSPPLEQCVPDR.G
0.70

preproprotein

prothrombin
P00734 (THRB_HUMAN)
R.SGIECQLWR.S
0.68

preproprotein

prothrombin
P00734 (THRB_HUMAN)
R.TATSEYQTFFNPR.T
0.60

preproprotein

prothrombin
P00734 (THRB_HUMAN)
R.VTGWGNLKETWTANVGK.G
0.69

preproprotein

putative
Q5T013 (HYI_HUMAN)
R.IHLM*AGR.V
0.69

hydroxypyruvate

isomerase isoform 1

putative
Q5T013 (HYI_HUMAN)
R.IHLMAGR.V
0.66

hydroxypyruvate

isomerase isoform 1

ras-like protein family
Q92737 (RSLAA_HUMAN)
R.PAHPALR.L
0.71

member 10A

precursor

ras-related GTP-
Q7L523 (RRAGA_HUMAN)
K.ISNIIK.Q
0.82

binding protein A

retinol-binding
P02753 (RET4_HUMAN)
K.M*KYWGVASFLQK.G
0.73

protein 4 precursor

retinol-binding
P02753 (RET4_HUMAN)
R.FSGTWYAM*AK.K
0.63

protein 4 precursor

retinol-binding
P02753 (RET4_HUMAN)
R.LLNLDGTCADSYSFVFSR.D
0.79

protein 4 precursor

retinol-binding
P02753 (RET4_HUMAN)
R.LLNNWDVCADMVGTFTDTEDPAKF
0.77

protein 4 precursor

K.M

sex hormone-binding
P04278 (SHBG_HUMAN)
R.LFLGALPGEDSSTSFCLNGLWAQGQ
0.66

globulin isoform 1

R.L

precursor

sex hormone-binding
P04278 (SHBG_HUMAN)
K.DDWFMLGLR.D
0.60

globulin isoform 4

precursor

sex hormone-binding
P04278 (SHBG_HUMAN)
R.SCDVESNPGIFLPPGTQAEFNLR.G
0.64

globulin isoform 4

precursor

sex hormone-binding
P04278 (SHBG_HUMAN)
R.TWDPEGVIFYGDTNPKDDWFM*L
0.65

globulin isoform 4

GLR.D

precursor

sex hormone-binding
P04278 (SHBG_HUMAN)
R.TWDPEGVIFYGDTNPKDDWFMLGL
0.66

globulin isoform 4

R.D

precursor

signal transducer and
P52630 (STAT2_HUMAN)
R.KFCRDIQDPTQLAEMIFNLLLEEK.R
0.73

activator of

transcription 2

spectrin beta chain,
Q13813 (SPTN1_HUMAN)
R.NELIRQEKLEQLAR.R
0.60

non-erythrocytic 1

stabilin-1 precursor
Q9NY15 (STAB1_HUMAN)
R.KNLSER.W
0.88

succinate-
P51649 (SSDH_HUMAN)
R.KWYNLMIQNK.D
0.88

semialdehyde

dehydrogenase,

mitochondrial

tetranectin precursor
P05452 (TETN_HUMAN)
K.SRLDTLAQEVALLK.E
0.75

THAP domain-
Q8TBB0 (THAP6_HUMAN)
K.RLDVNAAGIWEPKK.G
0.69

containing protein 6

thyroxine-binding
P05543 (THBG_HUMAN)
R.SILFLGK.V
0.79

globulin precursor

tripartite motif-
Q9C035 (TRIM5_HUMAN)
R.ELISDLEHRLQGSVM*ELLQGVDGVI
0.60

containing protein 5

K.R

vitamin D-binding
P02774 (VTDB_HUMAN)
K.EDFTSLSLVLYSR.K
0.66

protein isoform 1

precursor

vitamin D-binding
P02774 (VTDB_HUMAN)
K.ELSSFIDKGQELCADYSENTFTEYK.K
0.67

protein isoform 1

precursor

vitamin D-binding
P02774 (VTDB_HUMAN)
K.ELSSFIDKGQELCADYSENTFTEYKK.K
0.66

protein isoform 1

precursor

vitamin D-binding
P02774 (VTDB_HUMAN)
K.EVVSLTEACCAEGADPDCYDTR.T
0.65

protein isoform 1

precursor

vitamin D-binding
P02774 (VTDB_HUMAN)
K.TAMDVFVCTYFMPAAQLPELPDVEL
0.84

protein isoform 1

PTNKDVCDPGNTK.V

precursor

vitamin D-binding
P02774 (VTDB_HUMAN)
R.RTHLPEVFLSK.V
0.69

protein isoform 1

precursor

vitamin D-binding
P02774 (VTDB_HUMAN)
R.VCSQYAAYGEK.K
0.66

protein isoform 1

precursor

vitronectin precursor
P04004 (VTNC_HUMAN)
K.LIRDVWGIEGPIDAAFTR.I
0.61

vitronectin precursor
P04004 (VTNC_HUMAN)
R.DVWGIEGPIDAAFTR.I
0.63

vitronectin precursor
P04004 (VTNC_HUMAN)
R.ERVYFFK.G
0.81

vitronectin precursor
P04004 (VTNC_HUMAN)
R.FEDGVLDPDYPR.N
0.64

vitronectin precursor
P04004 (VTNC_HUMAN)
R.IYISGM*APRPSLAK.K
0.75

zinc finger protein
P52746 (ZN142_HUMAN)
K.TRFLLR.T
0.66

142

TABLE 9

Significant peptides (AUC >0.6) for 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-
P02765
K.VWPQQPSGELFEIEIDTLETTCHVL
0.61

glycoprotein
(FETUA_HUMAN)
DPTPVAR.C

preproprotein

alpha-2-HS-
P02765
R.HTFMGVVSLGSPSGEVSHPR.K
0.68

glycoprotein
(FETUA_HUMAN)

preproprotein

alpha-2-HS-
P02765
R.Q*PNCDDPETEEAALVAIDYINQNL
0.69

glycoprotein
(FETUA_HUMAN)
PWGYK.H

preproprotein

alpha-2-HS-
P02765
R.QPNCDDPETEEAALVAIDYINQNLP
0.64

glycoprotein
(FETUA_HUMAN)
WGYK.H

preproprotein

alpha-2-HS-
P02765
R.TVVQPSVGAAAGPVVPPCPGR.I
0.64

glycoprotein
(FETUA_HUMAN)

preproprotein

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.DVDKEFYLFPTVFDENESLLLEDNIR
0.62

precursor
(CERU_HUMAN)
.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.STQDTVIALDALSAYWIASHTTEER.G
0.70

isoform 1
(CO4A_HUMAN)

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.YGGGFYSTQDTINAIEGLTEYSLLVK
0.62

preproprotein
(CO5_HUMAN)
.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.AGELEVFNGYFVHFFAPDNLDPIPK
0.64

inhibitor heavy chain
(ITIH2_HUMAN)
.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
0.64

4 precursor
(RET4_HUMAN)
.F

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.LAQKVPTADLEDVLPLAEDITNILSK.C
0.73

protein isoform 1
(VTDB_HUMAN)

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

Protein description
Uniprot ID (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
K.ITDLIKDLDSQ
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.ANRPFLVFIR.E
0.72
0.60

precursor
(ANT3_HUMAN)

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.WLNVQLSPR.Q
0.74
0.67

subunit 2 precursor
(CPN2_HUMAN)

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.IFFPGVSEFGK
0.70
0.63

(CHLE_HUMAN)
.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.ITHYNYLILSK
0.66
0.69

preproprotein
(CO5_HUMAN)
.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.SLECLHPGTK.F
0.64
0.81

preproprotein
(CFAI_HUMAN)

complement factor I
P05156
R.TMGYQDFAD
0.73
0.75

preproprotein
(CFAI_HUMAN)
VVCYTQK.A

extracellular matrix
Q16610
R.ELLALIQLER.E
0.69
0.65

protein 1 isoform 3
(ECM1_HUMAN)

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.IPKPEASFSPR.R
0.69
0.77

inhibitor heavy chain H4
(ITIH4_HUMAN)

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.GTHCNQVEVIATLK
0.60
0.61

preproprotein
(CXCL7_HUMAN)
.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.IQHPFTVEEFVLPK.F
0.66
0.74

precursor
(PZP_HUMAN)

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 methionies, 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 concensus 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] -
16389

1136.5695+[11]

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

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

365.5487+++
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
579.3173++
S [y9] - 902.4690+[1]
518001

inhibitor heavy chain H1
.A

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.LWAYLTIQELLA
781.4531++
W [b2] - 300.1707+[1]
602601

inhibitor heavy chain H1
K.R

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.HLEVDVWVIEP
597.3247+++
I [y7] - 812.4625+[1]
206996

inhibitor heavy chain H2
QGLR.F

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-
VSAPSGTGHLPGL
506.2755+++
T [b7] - 300.6530++[1]
25346

1-glycoprotein 3
NPL

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.LPIPYITINNLNP
819.4723++
P [b2] - 211.1441+[1]
18814*

1-glycoprotein 9
R.E

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.AQWANPFDPS
630.8040++
A [b4] - 457.2194+[1]
30802

globulin precursor
K.T

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.HPFLYAPTILLW
590.3348+++
I [y7] - 421.7660++[1]
11471

AAR.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 analye 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_798.4
ITIH3_HUMAN
0.800

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_967.5
PSG9_HUMAN
0.794

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_594.3
PSG9_HUMAN
0.771

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_640.4
ITIH3_HUMAN
0.759

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_929.5
LBP_HUMAN
0.752

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_858.5
LBP_HUMAN
0.746

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_906.5
AFAM_HUMAN
0.740

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_846.8_600.3
ENPP2_HUMAN
0.734

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_1047.0
KS6A3_HUMAN
0.725

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_854.4
AFAM_HUMAN
0.722

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_918.5
KS6A3_HUMAN
0.712

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
0.711

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_758.0_623.4
GELS_HUMAN
0.699

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_822.8_863.5
CO8G_HUMAN
0.694

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_391.2
CO8G_HUMAN
0.692

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_999.5
IL1RA_HUMAN
0.691

IPGIFELGISSQSDR_809.9_849.4
CO8B_HUMAN
0.691

ESDTSYVSLK_564.8_696.4
CRP_HUMAN
0.690

GAVHVVVAETDYQSFAVLYLER_822.8_580.3
CO8G_HUMAN
0.690

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_549.3
CO8B_HUMAN
0.682

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_301.1
IL1A_HUMAN
0.681

IQTHSTTYR_369.5_540.3
F13B_HUMAN
0.681

IHPSYTNYR_575.8_598.3
PSG2_HUMAN
0.681

TEFLSNYLTNVDDITLVPGTLGR_846.8_699.4
ENPP2_HUMAN
0.681

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_1332.8
F13B_HUMAN
0.668

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_545.3
PAI1_HUMAN
0.664

VSAPSGTGHLPGLNPL_506.3_300.7
PSG3_HUMAN
0.664

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
0.664

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_299.2
ITIH4_HUMAN
0.658

TLAFVR_353.7_492.3
FA7_HUMAN
0.656

NSDQEIDFK_548.3_294.2
S10A5_HUMAN
0.656

YHFEALADTGISSEFYDNANDLLSK_940.8_874.5
CO8A_HUMAN
0.656

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_909.4
CXA1_HUMAN
0.653

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_263.1
ITIH4_HUMAN
0.646

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_849.4
GELS_HUMAN
0.641

NQSPVLEPVGR_598.3_866.5
KS6A3_HUMAN
0.641

FNAVLTNPQGDYDTSTGK_964.5_333.2
C1QC_HUMAN
0.641

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_940.8_301.1
CO8A_HUMAN
0.637

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_996.6
PAI1_HUMAN
0.636

VPGLYYFTYHASSR_554.3_420.2
C1QB_HUMAN
0.636

AALAAFNAQNNGSNFQLEEISR_789.1_746.4
FETUA_HUMAN
0.636

ETLLQDFR_511.3_565.3
AMBP_HUMAN
0.635

IVLSLDVPIGLLQILLEQAR_735.1_503.3
UCN2_HUMAN
0.635

ENPAVIDFELAPIVDLVR_670.7_811.5
CO6_HUMAN
0.635

LQLSETNR_480.8_355.2
PSG8_HUMAN
0.635

DPDQTDGLGLSYLSSHIANVER_796.4_456.2
GELS_HUMAN
0.635

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_545.3
FA12_HUMAN
0.632

QNYHQDSEAAINR_515.9_544.3
FRIH_HUMAN
0.632

EKPAGGIPVLGSLVNTVLK_631.4_930.6
BPIB1_HUMAN
0.632

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_601.4
CO6_HUMAN
0.630

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_806.4
ITIH1_HUMAN
0.629

FLYHK_354.2_447.2
AMBP_HUMAN
0.627

FNAVLTNPQGDYDTSTGK_964.5_262.1
C1QC_HUMAN
0.627

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_634.4
CSH_HUMAN
0.620

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_951.1_877.5
TNFA_HUMAN
0.614

LNIGYIEDLK_589.3_837.4
PAI2_HUMAN
0.614

NVNQSLLELHK_432.2_543.3
FRIH_HUMAN
0.613

ILLLGTAVESAWGDEQSAFR_721.7_910.6
CXA1_HUMAN
0.613

AALAAFNAQNNGSNFQLEEISR_789.1_633.3
FETUA_HUMAN
0.613

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_357.2
ECE1_HUMAN
0.612

AEHPTWGDEQLFQTTR_639.3_765.4
PGH1_HUMAN
0.612

VEPLYELVTATDFAYSSTVR_754.4_712.4
CO8B_HUMAN
0.611

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_317.2
IL1A_HUMAN
0.610

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_961.5_262.2
C1R_HUMAN
0.608

DPDQTDGLGLSYLSSHIANVER_796.4_328.1
GELS_HUMAN
0.608

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_521.2
TSP1_HUMAN
0.605

LEPLYSASGPGLRPLVIK_637.4_260.2
CAA60698
0.605

QRPPDLDTSSNAVDLLFFTDESGDSR_961.5_866.3
C1R_HUMAN
0.604

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_821.5
FA12_HUMAN
0.603

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_764.4_685.4
ANT3_HUMAN
0.600

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_430.3
TGFB1_HUMAN
0.683

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.683

VLEPTLK_400.3_458.3
VTDB_HUMAN
0.681

LHEAFSPVSYQHDLALLR_699.4_251.2
FA12_HUMAN
0.681

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_758.0_574.3
GELS_HUMAN
0.644

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_826.5
EGLN_HUMAN
0.643

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_310.2
PSG7_HUMAN
0.634

TLLPVSKPEIR_418.3_288.2
CO5_HUMAN
0.633

NKPGVYTDVAYYLAWIR_677.0_821.5
FA12_HUMAN
0.630

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_791.5_598.4
SHBG_HUMAN
0.624

FGFGGSTDSGPIR_649.3_946.5
ADA12_HUMAN
0.623

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_768.5
SHBG_HUMAN
0.622

YGIEEHGK_311.5_341.2
CXA1_HUMAN
0.621

LHEAFSPVSYQHDLALLR_699.4_380.2
FA12_HUMAN
0.621

AHYDLR_387.7_566.3
FETUA_HUMAN
0.620

FSVVYAK_407.2_381.2
FETUA_HUMAN
0.618

ALALPPLGLAPLLNLWAKPQGR_770.5_256.2
SHBG_HUMAN
0.618

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_545.3
FA12_HUMAN
0.613

WSAGLTSSQVDLYIPK_883.0_515.3
CBG_HUMAN
0.612

TPSAAYLWVGTGASEAEK_919.5_849.4
GELS_HUMAN
0.612

ALALPPLGLAPLLNLWAKPQGR_770.5_457.3
SHBG_HUMAN
0.612

QLGLPGPPDVPDHAAYHPF_676.7_299.2
ITIH4_HUMAN
0.612

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_758.0_623.4
GELS_HUMAN
0.607

LQVNTPLVGASLLR_741.0_925.6
BPIA1_HUMAN
0.607

VPSHAVVAR_312.5_345.2
TRFL_HUMAN
0.607

SLQNASAIESILK_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_263.1
ITIH4_HUMAN
0.605

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_375.2
TPA_HUMAN
0.603

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_261.2
C1S_HUMAN
0.601

AHYDLR_387.7_288.2
FETUA_HUMAN
0.600

DGSPDVTTADIGANTPDATK_973.5_844.4
PGRP2_HUMAN
0.600

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

SVVLIPLGAVDDGEHSQNEK_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_789.1_746.4
FETUA_HUMAN
0.684

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

SVVLIPLGAVDDGEHSQNEK_703.0_798.4
CNDP1_HUMAN
0.632

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_758.0_574.3
GELS_HUMAN
0.626

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

SVVLIPLGAVDDGEHSQNEK_703.0_286.2
CNDP1_HUMAN
0.620

TLAFVR_353.7_492.3
FA7_HUMAN
0.619

AVDIPGLEAATPYR_736.9_286.1
TENA_HUMAN
0.619

TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3
ENPP2_HUMAN
0.618

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
CO6_HUMAN
0.615

IEVNESGTVASSSTAVIVSAR_693.0_545.3
PAI1_HUMAN
0.615

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_822.8_863.5
CO8G_HUMAN
0.612

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_837.1_360.2
ECE1_HUMAN
0.611

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_822.8_580.3
CO8G_HUMAN
0.608

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_955.5_650.3
PSG1_HUMAN
0.607

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

YNQLLR_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_964.5_262.1
C1QC_HUMAN
0.602

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_829.4_874.4
PSG1_HUMAN
0.601

DLYHYITSYVVDGEIIIYGPAYSGR_955.5_707.3
PSG1_HUMAN
0.601

SPQAFYR_434.7_684.4
REL3_HUMAN
0.616

TPSAAYLWVGTGASEAEK_919.5_849.4
GELS_HUMAN
0.615

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.615

IEVNESGTVASSSTAVIVSAR_693.0_545.3
PAI1_HUMAN
0.615

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_822.8_863.5
CO8G_HUMAN
0.612

FQLPGQK_409.2_276.1
PSG1_HUMAN
0.612

DLYHYITSYVVDGEIIIYGPAYSGR_955.5_707.3
PSG1_HUMAN
0.601

TABLE 16

Lasso Early 32

Variable
Protein
Coefficient

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

Variable
Protein
Coefficient

LIQDAVTGLTVNGQITGDK_972.0_798.4
ITIH3_HUMAN
6.56

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
6.51

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_573.0_672.4
APOC3_HUMAN
1.3

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
0.83

FLPCENK_454.2_550.2
IL10_HUMAN
0.39

ELIEELVNITQNQK_557.6_517.3
IL13_HUMAN
0.3

TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3
ENPP2_HUMAN
0.29

VSEADSSNADWVTK_754.9_347.2
CFAB_HUMAN
0.27

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN
0.13

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
0.04

TASDFITK_441.7_781.4
GELS_HUMAN
−5.91

LIQDAVTGLTVNGQITGDK_972.0_798.4
ITIH3_HUMAN
6.56

TABLE 18

Lasso Protein Early Window

Variable
Protein
Coefficient

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
7.17

LIQDAVTGLTVNGQITGDK_972.0_798.4
ITIH3_HUMAN
6.06

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_573.0_672.4
APOC3_HUMAN
2.51

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

Variable
Protein
Coefficient

FLNWIK_410.7_560.3
HABP2_HUMAN
3.74

AHYDLR_387.7_288.2
FETUA_HUMAN
0.07

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
6.07

LIQDAVTGLTVNGQITGDK_972.0_798.4
ITIH3_HUMAN
8.85

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_549.6_977.5
FLNA_HUMAN
0.17

LIENGYFHPVK_439.6_343.2
F13B_HUMAN
1.7

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
−0.93

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_573.0_672.4
APOC3_HUMAN
1.15

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
0.09

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_996.0_777.4
FBLN1_HUMAN
1.14

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_919.5_849.4
GELS_HUMAN
−2.69

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

Transition
Protein
SumBestCoefs

LIQDAVTGLTVNGQITGDK_972.0_798.4
ITIH3_HUMAN
1173.723955

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
811.0150364

ELIEELVNITQNQK_557.6_618.3
IL13_HUMAN
621.9659363

VQTAHFK_277.5_431.2
CO8A_HUMAN
454.178544

NIQSVNVK_451.3_674.4
GROA_HUMAN
355.9550674

TLFIFGVTK_513.3_811.5
PSG4_HUMAN
331.8629189

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_439.6_627.4
F13B_HUMAN
237.5320227

ECEELEEK_533.2_405.2
IL15_HUMAN
200.38281

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
194.6252869

QALEEFQK_496.8_680.3
CO8B_HUMAN
179.2518843

IIEVEEEQEDPYLNDR_996.0_777.4
FBLN1_HUMAN
177.7534111

TYLHTYESEI_628.3_908.4
ENPP2_HUMAN
164.9735228

ELIEELVNITQNQK_557.6_517.3
IL13_HUMAN
162.2414693

LEEHYELR_363.5_580.3
PAI2_HUMAN
152.9262386

ISQGEADINIAFYQR_575.6_684.4
MMP8_HUMAN
144.2445011

HPWIVHWDQLPQYQLNR_744.0_918.5
KS6A3_HUMAN
140.2287926

AHYDLR_387.7_288.2
FETUA_HUMAN
137.9737525

GFQALGDAADIR_617.3_288.2
TIMP1_HUMAN
130.4945567

SWNEPLYHLVTEVR_581.6_716.4
PRL_HUMAN
127.442646

SGVDLADSNQK_567.3_591.3
VGFR3_HUMAN
120.5149446

YENYTSSFFIR_713.8_293.1
IL12B_HUMAN
117.0947487

FFQYDTWK_567.8_840.4
IGF2_HUMAN
109.8569617

HYFIAAVER_553.3_658.4
FA8_HUMAN
106.9426543

ITGFLKPGK_320.9_429.3
LBP_HUMAN
103.8056505

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
98.50490812

SGVDLADSNQK_567.3_662.3
VGFR3_HUMAN
97.19989285

ALDLSLK_380.2_575.3
ITIH3_HUMAN
94.84900337

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
92.52335783

HPWIVHWDQLPQYQLNR_744.0_1047.0
KS6A3_HUMAN
91.77547608

LIQDAVTGLTVNGQITGDK_972.0_640.4
ITIH3_HUMAN
83.6483639

LNIGYIEDLK_589.3_837.4
PAI2_HUMAN
83.50221521

IALGGLLFPASNLR_481.3_657.4
SHBG_HUMAN
79.33146741

LPATEKPVLLSK_432.6_460.3
HYOU1_HUMAN
78.89429168

FQLSETNR_497.8_605.3
PSG2_HUMAN
78.13445824

NEIVFPAGILQAPFYTR_968.5_357.2
ECE1_HUMAN
75.12145257

ALDLSLK_380.2_185.1
ITIH3_HUMAN
63.05454715

DLHLSDVFLK_396.2_366.2
CO6_HUMAN
58.26831142

TQILEWAAER_608.8_761.4
EGLN_HUMAN
57.29461621

FSVVYAK_407.2_381.2
FETUA_HUMAN
54.78436389

VSEADSSNADWVTK_754.9_347.2
CFAB_HUMAN
54.40003244

DPNGLPPEAQK_583.3_669.4
RET4_HUMAN
53.89169348

VQEAHLTEDQIFYFPK_655.7_701.4
CO8G_HUMAN
53.33747599

LSSPAVITDK_515.8_830.5
PLMN_HUMAN
53.22513181

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN
51.5477235

AVLHIGEK_289.5_292.2
THBG_HUMAN
49.73092632

GEVTYTTSQVSK_650.3_750.4
EGLN_HUMAN
45.14743629

GYVIIKPLVWV_643.9_854.6
SAMP_HUMAN
44.05164273

TGVAVNKPAEFTVDAK_549.6_977.5
FLNA_HUMAN
42.99898046

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
42.90897411

ILDGGNK_358.7_490.2
CXCL5_HUMAN
42.60771281

FLPCENK_454.2_390.2
IL10_HUMAN
42.56799651

GFQALGDAADIR_617.3_717.4
TIMP1_HUMAN
38.68456017

SDGAKPGPR_442.7_213.6
COLI_HUMAN
38.47800265

NTGVISVVTTGLDR_716.4_662.4
CADH1_HUMAN
32.62953675

SERPPIFEIR_415.2_288.2
LRP1_HUMAN
31.48248968

DFHINLFQVLPWLK_885.5_400.2
CFAB_HUMAN
31.27286268

DALSSVQESQVAQQAR_573.0_672.4
APOC3_HUMAN
31.26972354

ELCLDPK_437.7_359.2
IL8_HUMAN
29.91108737

ILNIFGVIK_508.8_790.5
TFR1_HUMAN
29.88784921

TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3
ENPP2_HUMAN
29.42327998

GAVHVVVAETDYQSFAVLYLER_822.8_863.5
CO8G_HUMAN
26.70286929

AVLHIGEK_289.5_348.7
THBG_HUMAN
25.78703299

TFLTVYWTPER_706.9_401.2
ICAM1_HUMAN
24.73090242

AGITIPR_364.2_486.3
IL17_HUMAN
23.84580477

GAVHVVVAETDYQSFAVLYLER_822.8_580.3
CO8G_HUMAN
23.81167843

SLQAFVAVAAR_566.8_487.3
IL23A_HUMAN
23.61468839

SWNEPLYHLVTEVR_581.6_614.3
PRL_HUMAN
23.2538221

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
22.70115313

TAHISGLPPSTDFIVYLSGLAPSIR_871.5_800.5
TENA_HUMAN
22.42695892

QNYHQDSEAAINR_515.9_544.3
FRIH_HUMAN
21.96827269

AHQLAIDTYQEFEETYIPK_766.0_634.4
CSH_HUMAN
21.75765717

GDTYPAELYITGSILR_885.0_274.1
F13B_HUMAN
20.89751398

AHYDLR_387.7_566.3
FETUA_HUMAN
20.67629529

IALGGLLFPASNLR_481.3_412.3
SHBG_HUMAN
19.28973033

ATNATLDPR_479.8_272.2
PAR1_HUMAN
18.77604574

FSVVYAK_407.2_579.4
FETUA_HUMAN
17.81136564

HTLNQIDEVK_598.8_951.5
FETUA_HUMAN
17.29763288

DIPHWLNPTR_416.9_373.2
PAPP1_HUMAN
17.00562521

LYYGDDEK_501.7_563.2
CO8A_HUMAN
16.78897272

AALAAFNAQNNGSNFQLEEISR_789.1_633.3
FETUA_HUMAN
16.41986569

IQTHSTTYR_369.5_627.3
F13B_HUMAN
15.78335174

GPITSAAELNDPQSILLR_632.4_826.5
EGLN_HUMAN
15.3936876

QTLSWTVTPK_580.8_818.4
PZP_HUMAN
14.92509259

AVGYLITGYQR_620.8_737.4
PZP_HUMAN
13.9795325

DIIKPDPPK_511.8_342.2
IL12B_HUMAN
13.76508282

YNQLLR_403.7_288.2
ENOA_HUMAN
12.61733711

GNGLTWAEK_488.3_634.3
C163B_HUMAN
12.5891421

QVFAVQR_424.2_473.3
ELNE_HUMAN
12.57709327

FLQEQGHR_338.8_497.3
CO8G_HUMAN
12.51843475

HVVQLR_376.2_515.3
IL6RA_HUMAN
11.83747559

DVLLLVHNLPQNLTGHIWYK_791.8_883.0
PSG7_HUMAN
11.69074708

TFLTVYWTPER_706.9_502.3
ICAM1_HUMAN
11.63709776

VELAPLPSWQPVGK_760.9_400.3
ICAM1_HUMAN
10.79897269

TLFIFGVTK_513.3_215.1
PSG4_HUMAN
10.2831751

AYSDLSR_406.2_375.2
SAMP_HUMAN
10.00461148

HATLSLSIPR_365.6_472.3
VGFR3_HUMAN
9.967933028

LQGTLPVEAR_542.3_571.3
CO5_HUMAN
9.963760572

NTVISVNPSTK_580.3_732.4
VCAM1_HUMAN
9.124228658

EVFSKPISWEELLQ_852.9_260.2
FA40A_HUMAN
8.527980294

SLQNASAIESILK_687.4_860.5
IL3_HUMAN
8.429061621

IQHPFTVEEFVLPK_562.0_861.5
PZP_HUMAN
7.996504258

GVTGYFTFNLYLK_508.3_683.9
PSG5_HUMAN
7.94396229

VFQYIDLHQDEFVQTLK_708.4_361.2
CNDP1_HUMAN
7.860590049

ILDDLSPR_464.8_587.3
ITIH4_HUMAN
7.593889262

LIENGYFHPVK_439.6_343.2
F13B_HUMAN
7.05838337

VFQFLEK_455.8_811.4
CO5_HUMAN
6.976884759

AFTECCVVASQLR_770.9_574.3
CO5_HUMAN
6.847474286

WWGGQPLWITATK_772.4_929.5
ENPP2_HUMAN
6.744837357

IQTHSTTYR_369.5_540.3
F13B_HUMAN
6.71464509

IAQYYYTFK_598.8_395.2
F13B_HUMAN
6.540497911

YGFYTHVFR_397.2_421.3
THRB_HUMAN
6.326347548

YHFEALADTGISSEFYDNANDLLSK_940.8_874.5
CO8A_HUMAN
6.261787525

ANDQYLTAAALHNLDEAVK_686.4_301.1
IL1A_HUMAN
6.217191651

FSLVSGWGQLLDR_493.3_403.2
FA7_HUMAN
6.1038295

GWVTDGFSSLK_598.8_854.4
APOC3_HUMAN
6.053494609

TLEAQLTPR_514.8_814.4
HEP2_HUMAN
5.855967278

VSAPSGTGHLPGLNPL_506.3_300.7
PSG3_HUMAN
5.625944609

EAQLPVIENK_570.8_699.4
PLMN_HUMAN
5.407703773

SPEAEDPLGVER_649.8_670.4
Z512B_HUMAN
5.341420139

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_365.6_272.2
VGFR3_HUMAN
4.334242077

NSDQEIDFK_548.3_294.2
S10A5_HUMAN
4.25302369

LPNNVLQEK_527.8_844.5
AFAM_HUMAN
4.183602548

ELANTIK_394.7_475.3
S10AC_HUMAN
4.13558153

LSIPQITTK_500.8_687.4
PSG5_HUMAN
3.966238797

TLNAYDHR_330.5_312.2
PAR3_HUMAN
3.961140111

WWGGQPLWITATK_772.4_373.2
ENPP2_HUMAN
3.941476057

ELLESYIDGR_597.8_710.4
THRB_HUMAN
3.832723338

ATLSAAPSNPR_542.8_570.3
CXCL2_HUMAN
3.82834767

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4
SHBG_HUMAN
3.80737887

NADYSYSVWK_616.8_333.2
CO5_HUMAN
3.56404167

ILILPSVTR_506.3_559.3
PSGx_HUMAN
3.526998593

ALEQDLPVNIK_620.4_798.5
CNDP1_HUMAN
3.410412424

QVCADPSEEWVQK_788.4_275.2
CCL3_HUMAN
3.30795151

SVQNDSQAIAEVLNQLK_619.7_914.5
DESP_HUMAN
3.259270741

QVFAVQR_424.2_620.4
ELNE_HUMAN
3.211482663

ALPGEQQPLHALTR_511.0_807.5
IBP1_HUMAN
3.211207158

LEPLYSASGPGLRPLVIK_637.4_260.2
CAA60698
3.203088951

GTYLYNDCPGPGQDTDCR_697.0_666.3
TNR1A_HUMAN
3.139418139

DAGLSWGSAR_510.2_576.3
NEUR4_HUMAN
3.005197927

YGFYTHVFR_397.2_659.4
THRB_HUMAN
2.985663918

NNQLVAGYLQGPNVNLEEK_700.7_357.2
IL1RA_HUMAN
2.866983196

EKPAGGIPVLGSLVNTVLK_631.4_930.6
BPIB1_HUMAN
2.798965142

FGSDDEGR_441.7_735.3
PTHR_HUMAN
2.743283546

IEVNESGTVASSSTAVIVSAR_693.0_545.3
PAI1_HUMAN
2.699725572

FATTFYQHLADSK_510.3_533.3
ANT3_HUMAN
2.615073729

DYWSTVK_449.7_347.2
APOC3_HUMAN
2.525459346

QLGLPGPPDVPDHAAYHPF_676.7_263.1
ITIH4_HUMAN
2.525383799

LSSPAVITDK_515.8_743.4
PLMN_HUMAN
2.522306831

TEFLSNYLTNVDDITLVPGTLGR_846.8_699.4
ENPP2_HUMAN
2.473366805

SILFLGK_389.2_201.1
THBG_HUMAN
2.472413913

VTFEYR_407.7_614.3
CRHBP_HUMAN
2.425338167

SVVLIPLGAVDDGEHSQNEK_703.0_798.4
CNDP1_HUMAN
2.421340244

HTLNQIDEVK_598.8_958.5
FETUA_HUMAN
2.419851187

ALNSIIDVYHK_424.9_661.3
S10A8_HUMAN
2.367904596

ETLALLSTHR_570.8_500.3
IL5_HUMAN
2.230076769

GLQYAAQEGLLALQSELLR_1037.1_858.5
LBP_HUMAN
2.205949216

TYNVDK_370.2_262.1
PPB1_HUMAN
2.11849772

FTITAGSK_412.7_576.3
FABPL_HUMAN
2.098589805

GIVEECCFR_585.3_900.3
IGF2_HUMAN
2.059942995

YGIEEHGK_311.5_599.3
CXA1_HUMAN
2.033828589

ALVLELAK_428.8_331.2
INHBE_HUMAN
1.993820617

ITLPDFTGDLR_624.3_920.5
LBP_HUMAN
1.968753183

HELTDEELQSLFTNFANVVDK_817.1_906.5
AFAM_HUMAN
1.916438806

EANQSTLENFLER_775.9_678.4
IL4_HUMAN
1.902033355

DADPDTFFAK_563.8_825.4
AFAM_HUMAN
1.882254674

LFIPQITR_494.3_727.4
PSG9_HUMAN
1.860649392

DPNGLPPEAQK_583.3_497.2
RET4_HUMAN
1.847702127

VEPLYELVTATDFAYSSTVR_754.4_549.3
CO8B_HUMAN
1.842159131

FQLSETNR_497.8_476.3
PSG2_HUMAN
1.834693717

FSLVSGWGQLLDR_493.3_516.3
FA7_HUMAN
1.790582748

NKPGVYTDVAYYLAWIR_677.0_545.3
FA12_HUMAN
1.777303353

FTGSQPFGQGVEHATANK_626.0_521.2
TSP1_HUMAN
1.736517431

DDLYVSDAFHK_655.3_704.3
ANT3_HUMAN
1.717534082

AFLEVNEEGSEAAASTAVVIAGR_764.4_685.4
ANT3_HUMAN
1.679420475

LPNNVLQEK_527.8_730.4
AFAM_HUMAN
1.66321148

IVLSLDVPIGLLQILLEQAR_735.1_503.3
UCN2_HUMAN
1.644983604

DPTFIPAPIQAK_433.2_556.3
ANGT_HUMAN
1.625411496

SDLEVAHYK_531.3_617.3
CO8B_HUMAN
1.543640117

QLYGDTGVLGR_589.8_501.3
CO8G_HUMAN
1.505242962

VNHVTLSQPK_374.9_459.3
B2MG_HUMAN
1.48233058

TLLPVSKPEIR_418.3_288.2
CO5_HUMAN
1.439531341

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
1.424401638

YGIEEHGK_311.5_341.2
CXA1_HUMAN
1.379872204

DAGLSWGSAR_510.3_390.2
NEUR4_HUMAN
1.334272677

AEHPTWGDEQLFQTTR_639.3_569.3
PGH1_HUMAN
1.30549273

FQSVFTVTR_542.8_623.4
C1QC_HUMAN
1.302847429

VPGLYYFTYHASSR_554.3_420.2
C1QB_HUMAN
1.245565877

AYSDLSR_406.2_577.3
SAMP_HUMAN
1.220777002

ALEQDLPVNIK_620.4_570.4
CNDP1_HUMAN
1.216612522

NAVVQGLEQPHGLVVHPLR_688.4_890.6
LRP1_HUMAN
1.212935735

TSDQIHFFFAK_447.6_659.4
ANT3_HUMAN
1.176238265

GTYLYNDCPGPGQDTDCR_697.0_335.2
TNR1A_HUMAN
1.1455649

TSYQVYSK_488.2_787.4
C163A_HUMAN
1.048896429

ALNSIIDVYHK_424.9_774.4
S10A8_HUMAN
1.028522516

VELAPLPSWQPVGK_760.9_342.2
ICAM1_HUMAN
0.995831393

LSETNR_360.2_330.2
PSG1_HUMAN
0.976094717

HFQNLGK_422.2_527.2
AFAM_HUMAN
0.956286531

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
0.947931674

LPATEKPVLLSK_432.6_347.2
HYOU1_HUMAN
0.932537153

SPEAEDPLGVER_649.8_314.1
Z512B_HUMAN
0.905955419

DEIPHNDIALLK_459.9_510.8
HABP2_HUMAN
0.9032484

FFQYDTWK_567.8_712.3
IGF2_HUMAN
0.884340285

LIEIANHVDK_384.6_498.3
ADA12_HUMAN
0.881493383

AGFAGDDAPR_488.7_701.3
ACTB_HUMAN
0.814836556

YEFLNGR_449.7_606.3
PLMN_HUMAN
0.767373087

VIAVNEVGR_478.8_284.2
CHL1_HUMAN
0.721519592

SLSQQIENIR_594.3_531.3
CO1A1_HUMAN
0.712051082

EWVAIESDSVQPVPR_856.4_486.2
CNDP1_HUMAN
0.647712421

YGLVTYATYPK_638.3_843.4
CFAB_HUMAN
0.618499569

SVVLIPLGAVDDGEHSQNEK_703.0_286.2
CNDP1_HUMAN
0.606626346

NSDQEIDFK_548.3_409.2
S10A5_HUMAN
0.601928175

NVNQSLLELHK_432.2_543.3
FRIH_HUMAN
0.572008792

IAQYYYTFK_598.8_884.4
F13B_HUMAN
0.495062844

GPITSAAELNDPQSILLR_632.4_601.4
EGLN_HUMAN
0.47565795

YTTEIIK_434.2_704.4
C1R_HUMAN
0.433318952

GYVIIKPLVWV_643.9_304.2
SAMP_HUMAN
0.427905264

LDFHFSSDR_375.2_464.2
INHBC_HUMAN
0.411898116

IPSNPSHR_303.2_496.3
FBLN3_HUMAN
0.390037291

APLTKPLK_289.9_357.2
CRP_HUMAN
0.38859469

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
0.371359974

YENYTSSFFIR_713.8_756.4
IL12B_HUMAN
0.346336267

SPQAFYR_434.7_556.3
REL3_HUMAN
0.345901234

SVDEALR_395.2_488.3
PRDX2_HUMAN
0.307518869

FVFGTTPEDILR_697.9_742.4
TSP1_HUMAN
0.302313589

FTFTLHLETPKPSISSSNLNPR_829.4_787.4
PSG1_HUMAN
0.269826678

VGEYSLYIGR_578.8_708.4
SAMP_HUMAN
0.226573173

ILPSVPK_377.2_244.2
PGH1_HUMAN
0.225429414

LFIPQITR_494.3_614.4
PSG9_HUMAN
0.18285533

TGYYFDGISR_589.8_857.4
FBLN1_HUMAN
0.182474114

HYGGLTGLNK_530.3_759.4
PGAM1_HUMAN
0.152397007

NQSPVLEPVGR_598.3_866.5
KS6A3_HUMAN
0.128963949

IGKPAPDFK_324.9_294.2
PRDX2_HUMAN
0.113383235

TSESTGSLPSPFLR_739.9_716.4
PSMG1_HUMAN
0.108159874

ESDTSYVSLK_564.8_347.2
CRP_HUMAN
0.08569303

ETPEGAEAKPWYEPIYLGGVFQLEK_951.1_877.5
TNFA_HUMAN
0.039781728

TSDQIHFFFAK_447.6_512.3
ANT3_HUMAN
0.008064465

TABLE 21

Lasso32 Middle Window

Co-

effi-

Variable
UniProt_ID
cient

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_699.4_251.2
FA12_HUMAN
2.14

QGHNSVFLIK_381.6_520.4
HEMO_HUMAN
0.25

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
−2.81

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
−3.46

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
−6.61

TABLE 22

Lasso100 Middle Window

Co-

effi-

Variable
UniProt_ID
cient

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_699.4_251.2
FA12_HUMAN
1.8

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_573.0_502.3
APOC3_HUMAN
0.41

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_791.8_310.2
PSG7_HUMAN
−1.54

VPLALFALNR_557.3_620.4
PEPD_HUMAN
−1.91

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
−2.3

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
−3.6

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
−3.96

TABLE 23

Lasso Protein Middle Window

Co-

effi-

Variable
UniProt_ID
cient

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_699.4_251.2
FA12_HUMAN
1.62

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_760.9_342.2
ICAM1_HUMAN
1.13

QVFAVQR_424.2_473.3
ELNE_HUMAN
0.79

ANLINNIFELAGLGK_793.9_299.2
LCAP_HUMAN
0.23

DVLLLVHNLPQNLTGHIWYK_791.8_310.2
PSG7_HUMAN
−0.61

VEHSDLSFSK_383.5_234.1
B2MG_HUMAN
−0.69

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
−0.85

VPLALFALNR_557.3_620.4
PEPD_HUMAN
−1.45

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
−1.9

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
−2.07

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
−2.32

TABLE 24

Lasso All Middle Window

Co-

effi-

Variable
UniProt_ID
cient

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_699.4_251.2
FA12_HUMAN
0.21

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
−0.62

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
−1.28

TABLE 25

Lasso32 Middle-Late Window

Variable
UniProt_ID
Coefficient

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_758.0_574.3
GELS_HUMAN
−2.08

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_538.3
CO6_HUMAN
1.4

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_666.3
TNR1A_HUMAN
0.51

SLQNASAIESILK_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_789.1_746.4
FETUA_HUMAN
−0.09

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
−0.52

TSYQVYSK_488.2_787.4
C163A_HUMAN
−0.62

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
−1.29

TAHISGLPPSTDFIVYLSGLAPSIR_871.5_472.3
TENA_HUMAN
−1.53

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
−1.73

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
−1.95

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_696.4
CO6_HUMAN
3.06

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

SLQNASAIESILK_687.4_860.5
IL3_HUMAN
0.59

ANLINNIFELAGLGK_793.9_299.2
LCAP_HUMAN
0.55

GTYLYNDCPGPGQDTDCR_697.0_666.3
TNR1A_HUMAN
0.55

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_758.0_574.3
GELS_HUMAN
−1.31

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
−1.59

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
−1.73

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
−2.02

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
−3

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
−3.15

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_538.3
CO6_HUMAN
2.38

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_758.0_574.3
GELS_HUMAN
−0.02

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
−0.05

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
−0.12

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
−0.17

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_375.2
CNDP1_HUMAN
2.55

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_666.3
TNR1A_HUMAN
0.45

TSYQVYSK_488.2_787.4
C163A_HUMAN
−1.73

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
−2.56

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_789.1_746.4
FETUA_HUMAN
−6.55

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_375.2
CNDP1_HUMAN
1.57

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_693.0_545.3
PAI1_HUMAN
0.36

GTYLYNDCPGPGQDTDCR_697.0_666.3
TNR1A_HUMAN
0.21

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_837.1_360.2
ECE1_HUMAN
−0.65

AYSDLSR_406.2_375.2
SAMP_HUMAN
−1.23

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
−1.63

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

AALAAFNAQNNGSNFQLEEISR_789.1_746.4
FETUA_HUMAN
−6.49

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_697.0_666.3
TNR1A_HUMAN
0.26

IEVNESGTVASSSTAVIVSAR_693.0_545.3
PAI1_HUMAN
0.18

LTTVDIVTLR_565.8_815.5
IL2RB_HUMAN
0.18

TSYQVYSK_488.2_787.4
C163A_HUMAN
−0.11

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
−0.89

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_789.1_746.4
FETUA_HUMAN
−5.71

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

AALAAFNAQNNGSNFQLEEISR_789.1_746.4
FETUA_HUMAN
−1.57

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_517.3
IL13_HUMAN
3.224369171

AHYDLR_387.7_288.2
FETUA_HUMAN
1.869007658

FSVVYAK_407.2_381.2
FETUA_HUMAN
1.770198171

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN
1.710936472

ITGFLKPGK_320.9_301.2
LBP_HUMAN
1.623922439

ITGFLKPGK_320.9_429.3
LBP_HUMAN
1.408035272

ELIEELVNITQNQK_557.6_618.3
IL13_HUMAN
1.345412168

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_288.2
CO5_HUMAN
1.291280928

LIQDAVTGLTVNGQITGDK_972.0_798.4
ITIH3_HUMAN
1.28622301

QALEEFQK_496.8_680.3
CO8B_HUMAN
1.191731825

FSVVYAK_407.2_579.4
FETUA_HUMAN
1.078909138

ITLPDFTGDLR_624.3_920.5
LBP_HUMAN
1.072613747

AHYDLR_387.7_566.3
FETUA_HUMAN
1.029562263

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
1.00992071

DVLLLVHNLPQNLPGYFWYK_810.4_967.5
PSG9_HUMAN
1.007095529

SFRPFVPR_335.9_635.3
LBP_HUMAN
0.970312536

SDLEVAHYK_531.3_617.3
CO8B_HUMAN
0.967904893

VQEAHLTEDQIFYFPK_655.7_701.4
CO8G_HUMAN
0.960398254

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_810.4_594.3
PSG9_HUMAN
0.756797142

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_517.3
IL13_HUMAN
1.709778508

LPNNVLQEK_527.8_844.5
AFAM_HUMAN
0.961692716

AHYDLR_387.7_288.2
FETUA_HUMAN
0.901586746

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN
0.879119498

IEGNLIFDPNNYLPK_874.0_414.2
APOB_HUMAN
0.842483095

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_972.0_798.4
ITIH3_HUMAN
0.624325189

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_288.2
CO5_HUMAN
0.5670608

ELIEELVNITQNQK_557.6_618.3
IL13_HUMAN
0.555624783

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_920.5
LBP_HUMAN
0.51667902

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_857.5_333.2
AFAM_HUMAN
0.459595701

WWGGQPLWITATK_772.4_929.5
ENPP2_HUMAN
0.44680777

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.434157773

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
0.432484862

TABLE 35

Random Forest Protein Early Window

Variable
Protein
MeanDecreaseGini

ELIEELVNITQNQK_557.6_517.3
IL13_HUMAN
2.881452809

LPNNVLQEK_527.8_844.5
AFAM_HUMAN
1.833987752

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN
1.608843881

IEGNLIFDPNNYLPK_874.0_414.2
APOB_HUMAN
1.594658208

VFQFLEK_455.8_811.4
CO5_HUMAN
1.290134412

LIQDAVTGLTVNGQITGDK_972.0_798.4
ITIH3_HUMAN
1.167981736

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_810.4_967.5
PSG9_HUMAN
0.929633865

WWGGQPLWITATK_772.4_929.5
ENPP2_HUMAN
0.905895642

VQEAHLTEDQIFYFPK_655.7_701.4
CO8G_HUMAN
0.883887371

NNQLVAGYLQGPNVNLEEK_700.7_999.5
IL1RA_HUMAN
0.806472085

SLLQPNK_400.2_599.4
CO8A_HUMAN
0.783623222

DALSSVQESQVAQQAR_573.0_672.4
APOC3_HUMAN
0.774365756

NIQSVNVK_451.3_674.4
GROA_HUMAN
0.767963386

HPWIVHWDQLPQYQLNR_744.0_1047.0
KS6A3_HUMAN
0.759960139

TTSDGGYSFK_531.7_860.4
INHA_HUMAN
0.732813448

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.718779092

LSSPAVITDK_515.8_743.4
PLMN_HUMAN
0.699547739

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
0.693159192

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_289.2
ANGT_HUMAN
0.579034994

ALVLELAK_428.8_672.4
INHBE_HUMAN
0.573458483

TABLE 36

Random Forest All Early Window

Variable
Protein
MeanDecreaseGini

ELIEELVNITQNQK_557.6_517.3
IL13_HUMAN
0.730972421

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_618.3
IL13_HUMAN
0.334303166

LPNNVLQEK_527.8_844.5
AFAM_HUMAN
0.329800706

IEGNLIFDPNNYLPK_874.0_414.2
APOB_HUMAN
0.325596677

ITGFLKPGK_320.9_429.3
LBP_HUMAN
0.31473104

FLNWIK_410.7_560.3
HABP2_HUMAN
0.299810081

LIQDAVTGLTVNGQITGDK_972.0_798.4
ITIH3_HUMAN
0.295613448

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_810.4_967.5
PSG9_HUMAN
0.218613384

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_473.3
APOB_HUMAN
0.211319422

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
0.206574494

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
IL1A_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
C1S_HUMAN
27.34493617

HPWIVHWDQLPQYQLNR_744.0_918.5
KS6A3_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_730.1_430.3
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_612.8_788.4
CO6_HUMAN
2.09649626

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
1.27664656

VFQFLEK_455.8_811.4
CO5_HUMAN
1.243884833

ANLINNIFELAGLGK_793.9_299.2
LCAP_HUMAN
1.231814882

VEHSDLSFSK_383.5_234.1
B2MG_HUMAN
1.188808078

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
1.185075445

LNIGYIEDLK_589.3_950.5
PAI2_HUMAN
1.122351536

VLEPTLK_400.3_458.3
VTDB_HUMAN
1.062664798

VPLALFALNR_557.3_620.4
PEPD_HUMAN
1.019466776

TLAFVR_353.7_492.3
FA7_HUMAN
0.98797064

TLFIFGVTK_513.3_811.5
PSG4_HUMAN
0.980159531

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
0.960286027

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
0.947091926

YGIEEHGK_311.5_599.3
CXA1_HUMAN
0.946937719

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
0.916262164

LHEAFSPVSYQHDLALLR_699.4_251.2
FA12_HUMAN
0.891310053

SLDFTELDVAAEK_719.4_316.2
ANGT_HUMAN
0.884498494

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.869043942

HFQNLGK_422.2_527.2
AFAM_HUMAN
0.865435217

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
0.844842109

TLNAYDHR_330.5_312.2
PAR3_HUMAN
0.792615068

DVLLLVHNLPQNLTGHIWYK_791.8_310.2
PSG7_HUMAN
0.763629346

GPITSAAELNDPQSILLR_632.4_826.5
EGLN_HUMAN
0.762305265

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4
SHBG_HUMAN
0.706312721

SLQNASAIESILK_687.4_860.5
IL3_HUMAN
0.645503581

HYINLITR_515.3_301.1
NPY_HUMAN
0.62631682

VELAPLPSWQPVGK_760.9_342.2
ICAM1_HUMAN
0.608991877

LQVNTPLVGASLLR_741.0_925.6
BPIA1_HUMAN
0.607801279

TLEAQLTPR_514.8_814.4
HEP2_HUMAN
0.597771074

SDGAKPGPR_442.7_459.2
COLI_HUMAN
0.582773073

TABLE 41

Random Forest All Middle Window

Variable
UniProt_ID
MeanDecreaseGini

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
0.493373282

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
0.382180772

VFQFLEK_455.8_811.4
CO5_HUMAN
0.260292083

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
0.243156718

NADYSYSVWK_616.8_769.4
CO5_HUMAN
0.242388196

VLEPTLK_400.3_458.3
VTDB_HUMAN
0.238171849

VEHSDLSFSK_383.5_234.1
B2MG_HUMAN
0.236873731

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
0.224727161

VLEPTLK_400.3_587.3
VTDB_HUMAN
0.222105614

TLFIFGVTK_513.3_811.5
PSG4_HUMAN
0.210807574

ANLINNIFELAGLGK_793.9_299.2
LCAP_HUMAN
0.208714978

LNIGYIEDLK_589.3_950.5
PAI2_HUMAN
0.208027555

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
0.197362212

VNHVTLSQPK_374.9_244.2
B2MG_HUMAN
0.195728091

YGIEEHGK_311.5_599.3
CXA1_HUMAN
0.189969499

HFQNLGK_422.2_527.2
AFAM_HUMAN
0.189572857

AGITIPR_364.2_486.3
IL17_HUMAN
0.188351054

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
0.185069517

SLDFTELDVAAEK_719.4_316.2
ANGT_HUMAN
0.173688295

TLAFVR_353.7_492.3
FA7_HUMAN
0.170636045

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
0.170608352

TLLIANETLR_572.3_703.4
IL5_HUMAN
0.16745571

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.161514946

LHEAFSPVSYQHDLALLR_699.4_251.2
FA12_HUMAN
0.15852146

DGSPDVTTADIGANTPDATK_973.5_844.4
PGRP2_HUMAN
0.154028378

VPLALFALNR_557.3_620.4
PEPD_HUMAN
0.153725879

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
0.150920884

YGIEEHGK_311.5_341.2
CXA1_HUMAN
0.150319671

FSLVSGWGQLLDR_493.3_403.2
FA7_HUMAN
0.144781622

IEEIAAK_387.2_660.4
CO5_HUMAN
0.141983196

TABLE 42

Random Forest 32 Middle-Late Window

Variable
UniProt_ID
MeanDecreaseGini

VPLALFALNR_557.3_620.4
PEPD_HUMAN
4.566619475

VFQFLEK_455.8_811.4
CO5_HUMAN
3.062474666

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
3.033740627

LIEIANHVDK_384.6_498.3
ADA12_HUMAN
2.825082394

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
2.787777983

TLAFVR_353.7_492.3
FA7_HUMAN
2.730532075

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
2.671290375

AVYEAVLR_460.8_587.4
PEPD_HUMAN
2.621357053

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
2.57568964

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
2.516708906

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
2.497348374

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
2.457401462

YGIEEHGK_311.5_599.3
CXA1_HUMAN
2.396824268

VLEPTLK_400.3_587.3
VTDB_HUMAN
2.388105564

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
2.340473883

WSAGLTSSQVDLYIPK_883.0_515.3
CBG_HUMAN
2.332007976

FGFGGSTDSGPIR_649.3_946.5
ADA12_HUMAN
2.325669514

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
2.31761671

QINSYVK_426.2_496.3
CBG_HUMAN
2.245221163

QINSYVK_426.2_610.3
CBG_HUMAN
2.212307699

TEQAAVAR_423.2_615.4
FA12_HUMAN
2.105860336

AVYEAVLR_460.8_750.4
PEPD_HUMAN
2.098321893

TEQAAVAR_423.2_487.3
FA12_HUMAN
2.062684763

DFNQFSSGEK_386.8_333.2
FETA_HUMAN
2.05160689

SLQAFVAVAAR_566.8_804.5
IL23A_HUMAN
1.989521006

SLDFTELDVAAEK_719.4_316.2
ANGT_HUMAN
1.820628782

DPTFIPAPIQAK_433.2_556.3
ANGT_HUMAN
1.763514326

DPTFIPAPIQAK_433.2_461.2
ANGT_HUMAN
1.760870392

VLEPTLK_400.3_458.3
VTDB_HUMAN
1.723389354

YENYTSSFFIR_713.8_756.4
IL12B_HUMAN
1.63355187

TABLE 43

Random Forest 100 Middle-Late Window

Variable
UniProt_ID
MeanDecreaseGini

VPLALFALNR_557.3_620.4
PEPD_HUMAN
1.995805024

VFQFLEK_455.8_811.4
CO5_HUMAN
1.235926416

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
1.187464899

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
1.166642578

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
1.146077071

TLAFVR_353.7_492.3
FA7_HUMAN
1.143038275

ANLINNIFELAGLGK_793.9_299.2
LCAP_HUMAN
1.130656591

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
1.098305298

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
1.096715712

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
1.086171713

YGIEEHGK_311.5_341.2
CXA1_HUMAN
1.071880823

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
1.062278869

TQILEWAAER_608.8_761.4
EGLN_HUMAN
1.059019017

AVYEAVLR_460.8_587.4
PEPD_HUMAN
1.057920661

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
1.038388955

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
1.028275728

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
1.026032369

LIEIANHVDK_384.6_498.3
ADA12_HUMAN
1.015065282

YGIEEHGK_311.5_599.3
CXA1_HUMAN
0.98667651

VLEPTLK_400.3_587.3
VTDB_HUMAN
0.970330675

DVLLLVHNLPQNLTGHIWYK_791.8_883.0
PSG7_HUMAN
0.934747674

TAHISGLPPSTDFIVYLSGLAPSIR_871.5_472.3
TENA_HUMAN
0.889111923

TLNAYDHR_330.5_312.2
PAR3_HUMAN
0.887605636

FGFGGSTDSGPIR_649.3_946.5
ADA12_HUMAN
0.884305889

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
0.880889836

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
0.863585472

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.849232356

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
0.843334824

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
0.842319271

TPSAAYLWVGTGASEAEK_919.5_849.4
GELS_HUMAN
0.828959173

TABLE 44

Random Forest Protein Middle-Late Window

Variable
UniProt_ID
MeanDecreaseGini

VPLALFALNR_557.3_620.4
PEPD_HUMAN
3.202123047

ANLINNIFELAGLGK_793.9_299.2
LCAP_HUMAN
2.100447309

VFQFLEK_455.8_811.4
CO5_HUMAN
2.096157529

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
2.052960939

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
2.046139797

TQILEWAAER_608.8_761.4
EGLN_HUMAN
1.99287941

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
1.920894959

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
1.917665697

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
1.883557705

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
1.870232155

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
1.869000136

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
1.825457092

VLEPTLK_400.3_587.3
VTDB_HUMAN
1.695327774

TEQAAVAR_423.2_615.4
FA12_HUMAN
1.685013152

LLAPSDSPEWLSFDVTGVVR_730.1_430.3
TGFB1_HUMAN
1.684068039

TLNAYDHR_330.5_312.2
PAR3_HUMAN
1.673758239

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
1.648896853

DVLLLVHNLPQNLTGHIWYK_791.8_883.0
PSG7_HUMAN
1.648146088

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
1.645833005

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
1.639121965

AGLLRPDYALLGHR_518.0_595.4
PGRP2_HUMAN
1.610227875

YGIEEHGK_311.5_599.3
CXA1_HUMAN
1.606978339

QINSYVK_426.2_496.3
CBG_HUMAN
1.554905578

LTTVDIVTLR_565.8_815.5
IL2RB_HUMAN
1.484081016

AALAAFNAQNNGSNFQLEEISR_789.1_746.4
FETUA_HUMAN
1.43173022

AEVIWTSSDHQVLSGK_586.3_300.2
PD1L1_HUMAN
1.394857397

ALEQDLPVNIK_620.4_570.4
CNDP1_HUMAN
1.393464547

DFNQFSSGEK_386.8_333.2
FETA_HUMAN
1.374296237

TSYQVYSK_488.2_787.4
C163A_HUMAN
1.36141387

TLEAQLTPR_514.8_685.4
HEP2_HUMAN
1.311118611

TABLE 45

Random Forest All Middle-Late Window

Variable
UniProt_ID
MeanDecreaseGini

VPLALFALNR_557.3_620.4
PEPD_HUMAN
0.685165163

VFQFLEK_455.8_811.4
CO5_HUMAN
0.426827804

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.409942379

YGIEEHGK_311.5_341.2
CXA1_HUMAN
0.406589512

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
0.402152062

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN
0.374861014

ANLINNIFELAGLGK_793.9_299.2
LCAP_HUMAN
0.367089422

TQILEWAAER_608.8_761.4
EGLN_HUMAN
0.353757524

AVYEAVLR_460.8_587.4
PEPD_HUMAN
0.350518668

TLAFVR_353.7_492.3
FA7_HUMAN
0.344669505

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
0.338752336

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
0.321850027

ELPQSIVYK_538.8_417.7
FBLN3_HUMAN
0.301819017

EVFSKPISWEELLQ_852.9_376.2
FA40A_HUMAN
0.299561811

LIEIANHVDK_384.6_498.3
ADA12_HUMAN
0.298253589

VLEPTLK_400.3_587.3
VTDB_HUMAN
0.296206088

YGIEEHGK_311.5_599.3
CXA1_HUMAN
0.295621408

DVLLLVHNLPQNLTGHIWYK_791.8_883.0
PSG7_HUMAN
0.292937475

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.275902848

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
0.275664578

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
0.27120436

AVDIPGLEAATPYR_736.9_399.2
TENA_HUMAN
0.266568271

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
0.262537889

TLNAYDHR_330.5_312.2
PAR3_HUMAN
0.259901193

IYLQPGR_423.7_329.2
ITIH2_HUMAN
0.259086112

AEVIWTSSDHQVLSGK_586.3_300.2
PD1L1_HUMAN
0.25722354

VPSHAVVAR_312.5_515.3
TRFL_HUMAN
0.256151812

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
0.251704855

FGFGGSTDSGPIR_649.3_946.5
ADA12_HUMAN
0.249400642

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
0.245930393

TABLE 46

Random Forest 32 Late Window

Variable
UniProt_ID
MeanDecreaseGini

AVYEAVLR_460.8_587.4
PEPD_HUMAN
1.889521223

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
1.75233545

AALAAFNAQNNGSNFQLEEISR_789.1_746.4
FETUA_HUMAN
1.676813493

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
1.600684153

AVYEAVLR_460.8_750.4
PEPD_HUMAN
1.462889662

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
1.364115361

VPLALFALNR_557.3_620.4
PEPD_HUMAN
1.324317148

QINSYVK_426.2_610.3
CBG_HUMAN
1.305932064

ITQDAQLK_458.8_702.4
CBG_HUMAN
1.263533228

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
1.245153376

LIEIANHVDK_384.6_498.3
ADA12_HUMAN
1.236529173

QINSYVK_426.2_496.3
CBG_HUMAN
1.221866266

YSHYNER_323.5_418.2
HABP2_HUMAN
1.169575572

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
1.126684146

VGVISFAQK_474.8_580.3
TFR2_HUMAN
1.075283855

VFQYIDLHQDEFVQTLK_708.4_375.2
CNDP1_HUMAN
1.07279097

SPEAEDPLGVER_649.8_314.1
Z512B_HUMAN
1.05759256

DEIPHNDIALLK_459.9_510.8
HABP2_HUMAN
1.028933332

ALEQDLPVNIK_620.4_798.5
CNDP1_HUMAN
1.014443799

ALEQDLPVNIK_620.4_570.4
CNDP1_HUMAN
1.010573267

ILDGGNK_358.7_603.3
CXCL5_HUMAN
0.992175141

TSYQVYSK_488.2_787.4
C163A_HUMAN
0.95649585

YENYTSSFFIR_713.8_756.4
IL12B_HUMAN
0.955085198

SETEIHQGFQHLHQLFAK_717.4_447.2
CBG_HUMAN
0.944726739

TLPFSR_360.7_506.3
LYAM1_HUMAN
0.944426109

VLSSIEQK_452.3_691.4
1433S_HUMAN
0.933902495

AEIEYLEK_497.8_389.2
LYAM1_HUMAN
0.891235263

GTYLYNDCPGPGQDTDCR_697.0_666.3
TNR1A_HUMAN
0.87187037

SGVDLADSNQK_567.3_662.3
VGFR3_HUMAN
0.869821307

SGVDLADSNQK_567.3_591.3
VGFR3_HUMAN
0.839946466

TABLE 47

Random Forest 100 Late Window

Variable
UniProt_ID
MeanDecreaseGini

AVYEAVLR_460.8_587.4
PEPD_HUMAN
0.971695767

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
0.920098693

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
0.786924487

AVYEAVLR_460.8_750.4
PEPD_HUMAN
0.772867983

AALAAFNAQNNGSNFQLEEISR_789.1_746.4
FETUA_HUMAN
0.744138513

AYSDLSR_406.2_375.2
SAMP_HUMAN
0.736078079

VPLALFALNR_557.3_620.4
PEPD_HUMAN
0.681784822

QINSYVK_426.2_610.3
CBG_HUMAN
0.585819307

LIEIANHVDK_384.6_498.3
ADA12_HUMAN
0.577161158

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
0.573055613

WSAGLTSSQVDLYIPK_883.0_515.3
CBG_HUMAN
0.569156128

ITQDAQLK_458.8_702.4
CBG_HUMAN
0.551017844

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
0.539330047

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
0.527652175

VFQYIDLHQDEFVQTLK_708.4_375.2
CNDP1_HUMAN
0.484155289

FQLPGQK_409.2_429.2
PSG1_HUMAN
0.480394031

AVDIPGLEAATPYR_736.9_286.1
TENA_HUMAN
0.475252565

QINSYVK_426.2_496.3
CBG_HUMAN
0.4728541

YISPDQLADLYK_713.4_277.2
ENOA_HUMAN
0.470079977

TLPFSR_360.7_506.3
LYAM1_HUMAN
0.46881451

SPEAEDPLGVER_649.8_314.1
Z512B_HUMAN
0.4658941

ALEQDLPVNIK_620.4_798.5
CNDP1_HUMAN
0.463604174

YSHYNER_323.5_418.2
HABP2_HUMAN
0.453076307

VGVISFAQK_474.8_580.3
TFR2_HUMAN
0.437768219

LQDAGVYR_461.2_680.3
PD1L1_HUMAN
0.428524689

AEIEYLEK_497.8_389.2
LYAM1_HUMAN
0.42041448

TSYQVYSK_488.2_787.4
C163A_HUMAN
0.419411932

SVVLIPLGAVDDGEHSQNEK_703.0_798.4
CNDP1_HUMAN
0.415325735

ALEQDLPVNIK_620.4_570.4
CNDP1_HUMAN
0.407951733

ILDGGNK_358.7_603.3
CXCL5_HUMAN
0.401059572

TABLE 48

Random Forest Protein Late Window

Variable
UniProt_ID
MeanDecreaseGini

AVYEAVLR_460.8_587.4
PEPD_HUMAN
1.836010146

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
1.739802548

AALAAFNAQNNGSNFQLEEISR_789.1_746.4
FETUA_HUMAN
1.455337749

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
1.395043941

AYSDLSR_406.2_375.2
SAMP_HUMAN
1.177349958

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
1.14243936

QINSYVK_426.2_496.3
CBG_HUMAN
1.05284482

ALEQDLPVNIK_620.4_798.5
CNDP1_HUMAN
0.971678206

YISPDQLADLYK_713.4_277.2
ENOA_HUMAN
0.902293734

AVDIPGLEAATPYR_736.9_286.1
TENA_HUMAN
0.893163413

SPEAEDPLGVER_649.8_314.1
Z512B_HUMAN
0.856551531

ILDGGNK_358.7_603.3
CXCL5_HUMAN
0.841485153

VGVISFAQK_474.8_580.3
TFR2_HUMAN
0.835256078

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
0.831195917

YSHYNER_323.5_418.2
HABP2_HUMAN
0.814479968

FQLPGQK_409.2_276.1
PSG1_HUMAN
0.77635168

YENYTSSFFIR_713.8_756.4
IL12B_HUMAN
0.761241391

TEQAAVAR_423.2_615.4
FA12_HUMAN
0.73195592

SGVDLADSNQK_567.3_662.3
VGFR3_HUMAN
0.72504131

VLSSIEQK_452.3_691.4
1433S_HUMAN
0.713380314

GTYLYNDCPGPGQDTDCR_697.0_666.3
TNR1A_HUMAN
0.704248586

TSYQVYSK_488.2_787.4
C163A_HUMAN
0.69026345

TLEAQLTPR_514.8_685.4
HEP2_HUMAN
0.654641588

AEVIWTSSDHQVLSGK_586.3_300.2
PD1L1_HUMAN
0.634751081

TAVTANLDIR_537.3_288.2
CHL1_HUMAN
0.619871203

ITENDIQIALDDAK_779.9_632.3
APOB_HUMAN
0.606313398

TASDFITK_441.7_781.4
GELS_HUMAN
0.593535076

SPQAFYR_434.7_556.3
REL3_HUMAN
0.592004045

NHYTESISVAK_624.8_415.2
NEUR1_HUMAN
0.588383911

LTTVDIVTLR_565.8_815.5
IL2RB_HUMAN
0.587343951

TABLE 49

Random Forest All Late Window

Variable
UniProt_ID
MeanDecreaseGini

AVYEAVLR_460.8_587.4
PEPD_HUMAN
0.437300283

AEIEYLEK_497.8_552.3
LYAM1_HUMAN
0.371624293

AALAAFNAQNNGSNFQLEEISR_789.1_746.4
FETUA_HUMAN
0.304039734

TGVAVNKPAEFTVDAK_549.6_258.1
FLNA_HUMAN
0.280588526

AVYEAVLR_460.8_750.4
PEPD_HUMAN
0.266788699

AYSDLSR_406.2_375.2
SAMP_HUMAN
0.247412666

VPLALFALNR_557.3_620.4
PEPD_HUMAN
0.229955358

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
0.218186524

ITQDAQLK_458.8_702.4
CBG_HUMAN
0.217646659

WSAGLTSSQVDLYIPK_883.0_515.3
CBG_HUMAN
0.213840705

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
0.212794469

LIEIANHVDK_384.6_498.3
ADA12_HUMAN
0.208620264

QINSYVK_426.2_610.3
CBG_HUMAN
0.202054546

QINSYVK_426.2_496.3
CBG_HUMAN
0.197235139

FQLPGQK_409.2_429.2
PSG1_HUMAN
0.188311102

VFQYIDLHQDEFVQTLK_708.4_375.2
CNDP1_HUMAN
0.180534913

ALEQDLPVNIK_620.4_798.5
CNDP1_HUMAN
0.178464358

YYGYTGAFR_549.3_450.3
TRFL_HUMAN
0.176050092

ALFLDALGPPAVTR_720.9_640.4
INHA_HUMAN
0.171492975

FQLPGQK_409.2_276.1
PSG1_HUMAN
0.167576198

SETEIHQGFQHLHQLFAK_717.4_447.2
CBG_HUMAN
0.162231844

ALEQDLPVNIK_620.4_570.4
CNDP1_HUMAN
0.162165399

VPSHAVVAR_312.5_515.3
TRFL_HUMAN
0.156742065

AVDIPGLEAATPYR_736.9_286.1
TENA_HUMAN
0.153681405

FTFTLHLETPKPSISSSNLNPR_829.4_874.4
PSG1_HUMAN
0.152042057

VGVISFAQK_474.8_580.3
TFR2_HUMAN
0.149034355

TLPFSR_360.7_506.3
LYAM1_HUMAN
0.143223501

SLDFTELDVAAEK_719.4_874.5
ANGT_HUMAN
0.141216186

SPEAEDPLGVER_649.8_314.1
Z512B_HUMAN
0.139843479

YGIEEHGK_311.5_341.2
CXA1_HUMAN
0.135236953

TABLE 50

Selected Transitions for Early Window

Transition
Parent Protein

LIQDAVTGLTVNGQITGDK_972.0_798.4
ITIH3_HUMAN

VQTAHFK_277.5_431.2
CO8A_HUMAN

FLNWIK_410.7_560.3
HABP2_HUMAN

ITGFLKPGK_320.9_429.3
LBP_HUMAN

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN

TYLHTYESEI_628.3_908.4
ENPP2_HUMAN

LIENGYFHPVK_439.6_627.4
F13B_HUMAN

AVLHIGEK_289.5_292.2
THBG_HUMAN

QALEEFQK_496.8_680.3
CO8B_HUMAN

TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3
ENPP2_HUMAN

TASDFITK_441.7_781.4
GELS_HUMAN

LPNNVLQEK_527.8_844.5
AFAM_HUMAN

AHYDLR_387.7_288.2
FETUA_HUMAN

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN

IEGNLIFDPNNYLPK_874.0_414.2
APOB_HUMAN

ITGFLKPGK_320.9_301.2
LBP_HUMAN

FSVVYAK_407.2_381.2
FETUA_HUMAN

ITGFLKPGK_320.9_429.3
LBP_HUMAN

VFQFLEK_455.8_811.4
CO5_HUMAN

LIQDAVTGLTVNGQITGDK_972.0_798.4
ITIH3_HUMAN

DADPDTFFAK_563.8_825.4
AFAM_HUMAN

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_557.3_620.4
PEPD_HUMAN

VFQFLEK_455.8_811.4
CO5_HUMAN

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
GELS_HUMAN

LIEIANHVDK_384.6_498.3
ADA12_HUMAN

TLAFVR_353.7_492.3
FA7_HUMAN

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN

AVYEAVLR_460.8_587.4
PEPD_HUMAN

SEPRPGVLLR_375.2_654.4
FA7_HUMAN

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN

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-
ADA12_HUMAN

containing 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 Disribution 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 Adjusted Peak Areas

(<37 vs >37 weeks)

Transition
Protein
pvalue

SPELQAEAK_486.8_659.4
APOA2_HUMAN
0.00246566

ALALPPLGLAPLLNLWAKPQGR_770.5_457.3
SHBG_HUMAN
0.002623332

ALALPPLGLAPLLNLWAKPQGR_770.5_256.2
SHBG_HUMAN
0.002822593

SPELQAEAK_486.8_788.4
APOA2_HUMAN
0.003183869

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_768.5
SHBG_HUMAN
0.004936049

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4
SHBG_HUMAN
0.005598977

DYWSTVK_449.7_347.2
APOC3_HUMAN
0.005680405

DYWSTVK_449.7_620.3
APOC3_HUMAN
0.006288693

WGAAPYR_410.7_634.3
PGRP2_HUMAN
0.006505238

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
0.007626246

DALSSVQESQVAQQAR_573.0_672.4
APOC3_HUMAN
0.008149335

LSIPQITTK_500.8_687.4
PSG5_HUMAN
0.009943955

GWVTDGFSSLK_598.8_854.4
APOC3_HUMAN
0.010175055

IALGGLLFPASNLR_481.3_657.4
SHBG_HUMAN
0.010784167

AKPALEDLR_506.8_813.5
APOA1_HUMAN
0.011331968

WGAAPYR_410.7_577.3
PGRP2_HUMAN
0.011761088

VPLALFALNR_557.3_620.4
PEPD_HUMAN
0.014050395

FSLVSGWGQLLDR_493.3_447.3
FA7_HUMAN
0.014271151

LSIPQITTK_500.8_800.5
PSG5_HUMAN
0.014339942

TLAFVR_353.7_274.2
FA7_HUMAN
0.014459876

DVLLLVHNLPQNLPGYFWYK_810.4_960.5
PSG9_HUMAN
0.016720007

FSVVYAK_407.2_381.2
FETUA_HUMAN
0.016792786

DVLLLVHNLPQNLPGYFWYK_810.4_215.1
PSG9_HUMAN
0.017335929

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
0.018147773

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.019056484

WNFAYWAAHQPWSR_607.3_545.3
PRG2_HUMAN
0.019190043

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
0.020218682

AQPVQVAEGSEPDGFWEALGGK_758.0_623.4
GELS_HUMAN
0.020226218

GWVTDGFSSLK_598.8_953.5
APOC3_HUMAN
0.023192703

IALGGLLFPASNLR_481.3_412.3
SHBG_HUMAN
0.023916911

WNFAYWAAHQPWSR_607.3_673.3
PRG2_HUMAN
0.026026975

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
0.027731407

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
0.031865281

DADPDTFFAK_563.8_302.1
AFAM_HUMAN
0.0335897

LFIPQITR_494.3_614.4
PSG9_HUMAN
0.034140767

DVLLLVHNLPQNLPGYFWYK_810.4_328.2
PSG9_HUMAN
0.034653304

TLAFVR_353.7_492.3
FA7_HUMAN
0.036441189

AVLHIGEK_289.5_292.2
THBG_HUMAN
0.038539433

IHPSYTNYR_384.2_452.2
PSG2_HUMAN
0.039733019

AGLLRPDYALLGHR_518.0_369.2
PGRP2_HUMAN
0.040916226

ILILPSVTR_506.3_559.3
PSGx_HUMAN
0.042460036

YYLQGAK_421.7_516.3
ITIH4_HUMAN
0.044511962

TPSAAYLWVGTGASEAEK_919.5_849.4
GELS_HUMAN
0.046362381

AGLLRPDYALLGHR_518.0_595.4
PGRP2_HUMAN
0.046572355

TYLHTYESEI_628.3_908.4
ENPP2_HUMAN
0.04754503

FSLVSGWGQLLDR_493.3_403.2
FA7_HUMAN
0.048642964

VNFTEIQK_489.8_765.4
FETA_HUMAN
0.04871392

LFIPQITR_494.3_727.4
PSG9_HUMAN
0.049288923

DISEVVTPR_508.3_787.4
CFAB_HUMAN
0.049458374

SEPRPGVLLR_375.2_454.3
FA7_HUMAN
0.049567047

TABLE 55

Univariate p-values for Adjusted Peak Areas

(<37 vs >40 weeks)

Transition
Protein
pvalue

SPELQAEAK_486.8_659.4
APOA2_HUMAN
0.001457796

DYWSTVK_449.7_347.2
APOC3_HUMAN
0.001619622

DYWSTVK_449.7_620.3
APOC3_HUMAN
0.002068704

DALSSVQESQVAQQAR_573.0_502.3
APOC3_HUMAN
0.00250563

GWVTDGFSSLK_598.8_854.4
APOC3_HUMAN
0.002543943

SPELQAEAK_486.8_788.4
APOA2_HUMAN
0.003108814

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
0.004035832

DALSSVQESQVAQQAR_573.0_672.4
APOC3_HUMAN
0.00434652

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
0.005306924

GWVTDGFSSLK_598.8_953.5
APOC3_HUMAN
0.005685534

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
0.005770384

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.005798991

ENPAVIDFELAPIVDLVR_670.7_601.4
CO6_HUMAN
0.006248095

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.006735817

TYLHTYESEI_628.3_908.4
ENPP2_HUMAN
0.007351774

AGLLRPDYALLGHR_518.0_369.2
PGRP2_HUMAN
0.009541521

AKPALEDLR_506.8_813.5
APOA1_HUMAN
0.009780371

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
0.010085363

FSLVSGWGQLLDR_493.3_447.3
FA7_HUMAN
0.010401836

WGAAPYR_410.7_634.3
PGRP2_HUMAN
0.011233623

ENPAVIDFELAPIVDLVR_670.7_811.5
CO6_HUMAN
0.012029564

DVLLLVHNLPQNLPGYFWYK_810.4_215.1
PSG9_HUMAN
0.014808277

LFIPQITR_494.3_614.4
PSG9_HUMAN
0.015879755

WGAAPYR_410.7_577.3
PGRP2_HUMAN
0.016562435

AGLLRPDYALLGHR_518.0_595.4
PGRP2_HUMAN
0.016793521

TLAFVR_353.7_492.3
FA7_HUMAN
0.016919708

FSLVSGWGQLLDR_493.3_403.2
FA7_HUMAN
0.016937583

WWGGQPLWITATK_772.4_373.2
ENPP2_HUMAN
0.019050115

GYVIIKPLVWV_643.9_304.2
SAMP_HUMAN
0.019675317

DVLLLVHNLPQNLPGYFWYK_810.4_960.5
PSG9_HUMAN
0.020387647

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
0.020458335

DVLLLVHNLPQNLPGYFWYK_810.4_328.2
PSG9_HUMAN
0.021488084

WWGGQPLWITATK_772.4_929.5
ENPP2_HUMAN
0.021709354

LDFHFSSDR_375.2_448.2
INHBC_HUMAN
0.022403383

LFIPQITR_494.3_727.4
PSG9_HUMAN
0.025561103

TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3
ENPP2_HUMAN
0.029344366

LSIPQITTK_500.8_800.5
PSG5_HUMAN
0.031361776

ALVLELAK_428.8_672.4
INHBE_HUMAN
0.031690737

SEPRPGVLLR_375.2_454.3
FA7_HUMAN
0.033067953

LSIPQITTK_500.8_687.4
PSG5_HUMAN
0.033972449

LDFHFSSDR_375.2_611.3
INHBC_HUMAN
0.034500249

LDFHFSSDR_375.2_464.2
INHBC_HUMAN
0.035166664

GAVHVVVAETDYQSFAVLYLER_822.8_580.3
CO8G_HUMAN
0.037334975

HELTDEELQSLFTNFANVVDK_817.1_854.4
AFAM_HUMAN
0.039258528

AYSDLSR_406.2_375.2
SAMP_HUMAN
0.04036485

YYLQGAK_421.7_516.3
ITIH4_HUMAN
0.042204165

ILPSVPK_377.2_264.2
PGH1_HUMAN
0.042397885

ELLESYIDGR_597.8_710.4
THRB_HUMAN
0.043053589

ALALPPLGLAPLLNLWAKPQGR_770.5_256.2
SHBG_HUMAN
0.045692283

VGEYSLYIGR_578.8_871.5
SAMP_HUMAN
0.04765767

ANDQYLTAAALHNLDEAVK_686.4_317.2
IL1A_HUMAN
0.048928376

YYGYTGAFR_549.3_551.3
TRFL_HUMAN
0.049568351

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 Adjusted Peak Areas in Gestation

Age at Birth Linear Model

Transition
Protein
pvalue

ENPAVIDFELAPIVDLVR_670.7_811.5
CO6_HUMAN
0.000117239

ENPAVIDFELAPIVDLVR_670.7_601.4
CO6_HUMAN
0.000130113

TYLHTYESEI_628.3_908.4
ENPP2_HUMAN
0.000160472

TYLHTYESEI_628.3_515.3
ENPP2_HUMAN
0.000175167

TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3
ENPP2_HUMAN
0.000219886

TEFLSNYLTNVDDITLVPGTLGR_846.8_699.4
ENPP2_HUMAN
0.000328416

WWGGQPLWITATK_772.4_373.2
ENPP2_HUMAN
0.000354644

WWGGQPLWITATK_772.4_929.5
ENPP2_HUMAN
0.000390821

SEYGAALAWEK_612.8_788.4
CO6_HUMAN
0.000511882

LDFHFSSDR_375.2_448.2
INHBC_HUMAN
0.000600637

ALVLELAK_428.8_672.4
INHBE_HUMAN
0.000732445

GLQYAAQEGLLALQSELLR_1037.1_929.5
LBP_HUMAN
0.000743924

DVLLLVHNLPQNLPGYFWYK_810.4_960.5
PSG9_HUMAN
0.000759173

FGFGGSTDSGPIR_649.3_745.4
ADA12_HUMAN
0.001224347

DVLLLVHNLPQNLPGYFWYK_810.4_328.2
PSG9_HUMAN
0.001241329

GYVIIKPLVWV_643.9_304.2
SAMP_HUMAN
0.001853785

SPELQAEAK_486.8_659.4
APOA2_HUMAN
0.001856303

GLQYAAQEGLLALQSELLR_1037.1_858.5
LBP_HUMAN
0.001978165

LDFHFSSDR_375.2_611.3
INHBC_HUMAN
0.002098948

LIEIANHVDK_384.6_683.4
ADA12_HUMAN
0.002212096

SFRPFVPR_335.9_272.2
LBP_HUMAN
0.002545286

SFRPFVPR_335.9_635.3
LBP_HUMAN
0.002620268

WSAGLTSSQVDLYIPK_883.0_515.3
CBG_HUMAN
0.002787272

DLHLSDVFLK_396.2_260.2
CO6_HUMAN
0.002954612

LIEIANHVDK_384.6_498.3
ADA12_HUMAN
0.002955081

DVLLLVHNLPQNLPGYFWYK_810.4_215.1
PSG9_HUMAN
0.003541011

LFIPQITR_494.3_614.4
PSG9_HUMAN
0.003750666

FGFGGSTDSGPIR_649.3_946.5
ADA12_HUMAN
0.003773696

YYLQGAK_421.7_516.3
ITIH4_HUMAN
0.004064026

SEYGAALAWEK_612.8_845.5
CO6_HUMAN
0.004208136

AITPPHPASQANIIFDITEGNLR_825.8_459.3
FBLN1_HUMAN
0.004709104

LDFHFSSDR_375.2_464.2
INHBC_HUMAN
0.005355741

HELTDEELQSLFTNFANVVDK_817.1_854.4
AFAM_HUMAN
0.005370567

ALNHLPLEYNSALYSR_621.0_696.4
CO6_HUMAN
0.005705922

ITQDAQLK_458.8_702.4
CBG_HUMAN
0.006762484

ITLPDFTGDLR_624.3_920.5
LBP_HUMAN
0.006993268

SILFLGK_389.2_577.4
THBG_HUMAN
0.007134146

WSAGLTSSQVDLYIPK_883.0_357.2
CBG_HUMAN
0.007670388

GVTSVSQIFHSPDLAIR_609.7_472.3
IC1_HUMAN
0.007742729

VGEYSLYIGR_578.8_871.5
SAMP_HUMAN
0.007778691

ITLPDFTGDLR_624.3_288.2
LBP_HUMAN
0.008179918

YYLQGAK_421.7_327.1
ITIH4_HUMAN
0.008404686

ALNHLPLEYNSALYSR_621.0_538.3
CO6_HUMAN
0.008601162

DYWSTVK_449.7_620.3
APOC3_HUMAN
0.008626786

TVQAVLTVPK_528.3_855.5
PEDF_HUMAN
0.008907523

ITGFLKPGK_320.9_301.2
LBP_HUMAN
0.009155417

LFIPQITR_494.3_727.4
PSG9_HUMAN
0.009571006

SPELQAEAK_486.8_788.4
APOA2_HUMAN
0.009776508

DYWSTVK_449.7_347.2
APOC3_HUMAN
0.00998356

ITGFLKPGK_320.9_429.3
LBP_HUMAN
0.010050264

FLNWIK_410.7_560.3
HABP2_HUMAN
0.010372454

DLHLSDVFLK_396.2_366.2
CO6_HUMAN
0.010806378

GVTSVSQIFHSPDLAIR_609.7_908.5
IC1_HUMAN
0.011035991

VEHSDLSFSK_383.5_468.2
B2MG_HUMAN
0.011113172

LLDSLPSDTR_558.8_276.2
IC1_HUMAN
0.011589013

LLDSLPSDTR_558.8_890.4
IC1_HUMAN
0.011629438

QALEEFQK_496.8_551.3
CO8B_HUMAN
0.011693839

LLDSLPSDTR_558.8_575.3
IC1_HUMAN
0.012159314

IIGGSDADIK_494.8_762.4
C1S_HUMAN
0.013080243

AFIQLWAFDAVK_704.9_650.4
AMBP_HUMAN
0.013462234

GFQALGDAADIR_617.3_717.4
TIMP1_HUMAN
0.014370997

LPNNVLQEK_527.8_730.4
AFAM_HUMAN
0.014424891

DTDTGALLFIGK_625.8_217.1
PEDF_HUMAN
0.014967952

VQTAHFK_277.5_502.3
CO8A_HUMAN
0.01524844

ILILPSVTR_506.3_559.3
PSGx_HUMAN
0.015263132

SILFLGK_389.2_201.1
THBG_HUMAN
0.015265233

TVQAVLTVPK_528.3_428.3
PEDF_HUMAN
0.015344052

VEPLYELVTATDFAYSSTVR_754.4_712.4
CO8B_HUMAN
0.015451068

FSLVSGWGQLLDR_493.3_447.3
FA7_HUMAN
0.015510454

GWVTDGFSSLK_598.8_854.4
APOC3_HUMAN
0.01610797

LSETNR_360.2_519.3
PSG1_HUMAN
0.016433362

TQILEWAAER_608.8_632.3
EGLN_HUMAN
0.01644844

SETEIHQGFQHLHQLFAK_717.4_318.1
CBG_HUMAN
0.016720367

TNLESILSYPK_632.8_936.5
IC1_HUMAN
0.017314185

TNLESILSYPK_632.8_807.5
IC1_HUMAN
0.017593786

AYSDLSR_406.2_375.2
SAMP_HUMAN
0.018531348

YEVQGEVFTKPQLWP_911.0_392.2
CRP_HUMAN
0.019111323

AYSDLSR_406.2_577.3
SAMP_HUMAN
0.019271266

QALEEFQK_496.8_680.3
CO8B_HUMAN
0.019429489

APLTKPLK_289.9_398.8
CRP_HUMAN
0.020110081

FQPTLLTLPR_593.4_276.1
IC1_HUMAN
0.020114306

ITQDAQLK_458.8_803.4
CBG_HUMAN
0.020401782

AVLHIGEK_289.5_292.2
THBG_HUMAN
0.02056597

ANDQYLTAAALHNLDEAVK_686.4_317.2
IL1A_HUMAN
0.020770124

VGEYSLYIGR_578.8_708.4
SAMP_HUMAN
0.021126414

TLYSSSPR_455.7_533.3
IC1_HUMAN
0.021306106

VEHSDLSFSK_383.5_234.1
B2MG_HUMAN
0.021640643

HELTDEELQSLFTNFANVVDK_817.1_906.5
AFAM_HUMAN
0.021921609

TLYSSSPR_455.7_696.3
IC1_HUMAN
0.022196181

GYVIIKPLVWV_643.9_854.6
SAMP_HUMAN
0.023126336

DEIPHNDIALLK_459.9_260.2
HABP2_HUMAN
0.023232158

ILILPSVTR_506.3_785.5
PSGx_HUMAN
0.023519909

WNFAYWAAHQPWSR_607.3_545.3
PRG2_HUMAN
0.023697087

FQPTLLTLPR_593.4_712.5
IC1_HUMAN
0.023751959

AQPVQVAEGSEPDGFWEALGGK_758.0_623.4
GELS_HUMAN
0.024262721

DEIPHNDIALLK_459.9_510.8
HABP2_HUMAN
0.024414348

GDSGGAFAVQDPNDK_739.3_716.3
C1S_HUMAN
0.025075028

FLNWIK_410.7_561.3
HABP2_HUMAN
0.025649617

APLTKPLK_289.9_357.2
CRP_HUMAN
0.025961162

ALDLSLK_380.2_185.1
ITIH3_HUMAN
0.026233504

GWVTDGFSSLK_598.8_953.5
APOC3_HUMAN
0.026291884

SETEIHQGFQHLHQLFAK_717.4_447.2
CBG_HUMAN
0.026457136

GDSGGAFAVQDPNDK_739.3_473.2
C1S_HUMAN
0.02727457

YEVQGEVFTKPQLWP_911.0_293.1
CRP_HUMAN
0.028244448

HVVQLR_376.2_614.4
IL6RA_HUMAN
0.028428028

DTDTGALLFIGK_625.8_818.5
PEDF_HUMAN
0.028773557

EVPLSALTNILSAQLISHWK_740.8_996.6
PAI1_HUMAN
0.029150774

AFTECCVVASQLR_770.9_574.3
CO5_HUMAN
0.029993325

TLAFVR_353.7_492.3
FA7_HUMAN
0.030064307

LWAYLTIQELLAK_781.5_300.2
ITIH1_HUMAN
0.030368674

DEIPHNDIALLK_459.9_245.1
HABP2_HUMAN
0.031972082

AGLLRPDYALLGHR_518.0_369.2
PGRP2_HUMAN
0.032057409

AVYEAVLR_460.8_587.4
PEPD_HUMAN
0.032527521

LPNNVLQEK_527.8_844.5
AFAM_HUMAN
0.033807082

GAVHVVVAETDYQSFAVLYLER_822.8_580.3
CO8G_HUMAN
0.034370139

WNFAYWAAHQPWSR_607.3_673.3
PRG2_HUMAN
0.0349737

EAQLPVIENK_570.8_329.2
PLMN_HUMAN
0.035304322

VQEAHLTEDQIFYFPK_655.7_701.4
CO8G_HUMAN
0.035704382

AFIQLWAFDAVK_704.9_836.4
AMBP_HUMAN
0.035914532

SGFSFGFK_438.7_585.3
CO8B_HUMAN
0.037168221

SGFSFGFK_438.7_732.4
CO8B_HUMAN
0.040182596

DADPDTFFAK_563.8_302.1
AFAM_HUMAN
0.041439744

EAQLPVIENK_570.8_699.4
PLMN_HUMAN
0.041447675

IIGGSDADIK_494.8_260.2
C1S_HUMAN
0.041683256

AVLTIDEK_444.8_718.4
A1AT_HUMAN
0.043221658

SEPRPGVLLR_375.2_654.4
FA7_HUMAN
0.044079127

YHFEALADTGISSEFYDNANDLLSK_940.8_874.5
CO8A_HUMAN
0.045313634

HFQNLGK_422.2_527.2
AFAM_HUMAN
0.047118971

LEQGENVFLQATDK_796.4_822.4
C1QB_HUMAN
0.047818928

NTVISVNPSTK_580.3_732.4
VCAM1_HUMAN
0.048102262

YYGYTGAFR_549.3_551.3
TRFL_HUMAN
0.048331316

ISLLLIESWLEPVR_834.5_500.3
CSH_HUMAN
0.049561581

LQVLGK_329.2_416.3
A2GL_HUMAN
0.049738493

TABLE 58

1/38 Univariate p-values for Peak Area Ratios (<37 vs >37 weeks)

UniProt_ID
Transition
pvalue

SHBG_HUMAN
IALGGLLFPASNLR_481.3_657.4
0.006134652

SHBG_HUMAN
IALGGLLFPASNLR_481.3_412.3
0.019049498

APOC3_HUMAN
DALSSVQESQVAQQAR_573.0_672.4
0.020688543

THBG_HUMAN
AVLHIGEK_289.5_292.2
0.0291698

PSG9_HUMAN
DVLLLVHNLPQNLPGYFWYK_810.4_960.5
0.033518454

APOC3_HUMAN
DALSSVQESQVAQQAR_573.0_502.3
0.043103265

PSG9_HUMAN
LFIPQITR_494.3_614.4
0.04655948

TABLE 59

Univariate p-values for Peak Area Ratios (<37 vs >40 weeks)

UniProt_ID
Transition
pvalue

APOC3_HUMAN
DALSSVQESQVAQQAR_573.0_672.4
0.011174438

APOC3_HUMAN
DALSSVQESQVAQQAR_573.0_502.3
0.015231617

PSG9_HUMAN
LFIPQITR_494.3_614.4
0.018308413

PSG9_HUMAN
LFIPQITR_494.3_727.4
0.027616871

PSG9_HUMAN
DVLLLVHNLPQNLPGYFWYK_810.4_960.5
0.028117582

THBG_HUMAN
AVLHIGEK_289.5_292.2
0.038899107

CO6_HUMAN
ALNHLPLEYNSALYSR_621.0_696.4
0.040662269

ENPP2_HUMAN
TYLHTYESEI_628.3_908.4
0.044545826

TABLE 60

Univariate p-values for Peak Area Ratios

in Time to Birth Linear Model

UniProt_ID
Transition
pvalue

ADA12_HUMAN
FGFGGSTDSGPIR_649.3_946.5
5.85E−27

ADA12_HUMAN
FGFGGSTDSGPIR_649.3_745.4
2.65E−24

PSG4_HUMAN
TLFIFGVTK_513.3_215.1
1.07E−20

PSG4_HUMAN
TLFIFGVTK_513.3_811.5
2.32E−20

PSGx_HUMAN
ILILPSVTR_506.3_785.5
8.25E−16

PSGx_HUMAN
ILILPSVTR_506.3_559.3
9.72E−16

PSG1_HUMAN
FQLPGQK_409.2_429.2
1.29E−12

PSG11_HUMAN
LFIPQITPK_528.8_261.2
2.11E−12

PSG1_HUMAN
FQLPGQK_409.2_276.1
2.33E−12

PSG11_HUMAN
LFIPQITPK_528.8_683.4
3.90E−12

PSG6_HUMAN
SNPVTLNVLYGPDLPR_585.7_817.4
5.71E−12

PSG6_HUMAN
SNPVTLNVLYGPDLPR_585.7_654.4
1.82E−11

VGFR3_HUMAN
SGVDLADSNQK_567.3_662.3
4.57E−11

INHBE_HUMAN
ALVLELAK_428.8_331.2
1.04E−08

PSG2_HUMAN
IHPSYTNYR_384.2_452.2
6.27E−08

PSG9_HUMAN
LFIPQITR_494.3_727.4
1.50E−07

VGFR3_HUMAN
SGVDLADSNQK_567.3_591.3
2.09E−07

PSG9_HUMAN
LFIPQITR_494.3_614.4
2.71E−07

PSG9_HUMAN
DVLLLVHNLPQNLPGYFWYK_810.4_960.5
3.10E−07

PSG2_HUMAN
IHPSYTNYR_384.2_338.2
2.55E−06

ITIH3_HUMAN
LIQDAVTGLTVNGQITGDK_972.0_640.4
2.76E−06

ENPP2_HUMAN
TYLHTYESEI_628.3_908.4
2.82E−06

ENPP2_HUMAN
WWGGQPLWITATK_772.4_373.2
3.75E−06

PSG9_HUMAN
DVLLLVHNLPQNLPGYFWYK_810.4_328.2
3.94E−06

B2MG_HUMAN
VEHSDLSFSK_383.5_468.2
5.42E−06

ENPP2_HUMAN
WWGGQPLWITATK_772.4_929.5
7.93E−06

ANGT_HUMAN
ALQDQLVLVAAK_634.9_289.2
1.04E−05

B2MG_HUMAN
VNHVTLSQPK_374.9_244.2
1.46E−05

AFAM_HUMAN
LPNNVLQEK_527.8_730.4
1.50E−05

AFAM_HUMAN
LPNNVLQEK_527.8_844.5
1.98E−05

THBG_HUMAN
AVLHIGEK_289.5_292.2
2.15E−05

ENPP2_HUMAN
TYLHTYESEI_628.3_515.3
2.17E−05

IL12B_HUMAN
DIIKPDPPK_511.8_342.2
3.31E−05

AFAM_HUMAN
DADPDTFFAK_563.8_302.1
6.16E−05

THBG_HUMAN
AVLHIGEK_289.5_348.7
8.34E−05

PSG9_HUMAN
DVLLLVHNLPQNLPGYFWYK_810.4_215.1
0.000104442

B2MG_HUMAN
VEHSDLSFSK_383.5_234.1
0.000140786

TRFL_HUMAN
YYGYTGAFR_549.3_450.3
0.000156543

HEMO_HUMAN
QGHNSVFLIK_381.6_260.2
0.000164578

A1BG_HUMAN
LLELTGPK_435.8_227.2
0.000171113

CO6_HUMAN
ALNHLPLEYNSALYSR_621.0_696.4
0.000242116

CO6_HUMAN
ALNHLPLEYNSALYSR_621.0_538.3
0.00024681

ALS_HUMAN
IRPHTFTGLSGLR_485.6_432.3
0.000314359

ITIH2_HUMAN
LSNENHGIAQR_413.5_544.3
0.0004877

PEDF_HUMAN
TVQAVLTVPK_528.3_855.5
0.000508174

AFAM_HUMAN
HFQNLGK_422.2_527.2
0.000522139

FLNA_HUMAN
TGVAVNKPAEFTVDAK_549.6_258.1
0.000594403

ANGT_HUMAN
ALQDQLVLVAAK_634.9_956.6
0.000640673

AFAM_HUMAN
HFQNLGK_422.2_285.1
0.000718763

HGFA_HUMAN
LHKPGVYTR_357.5_692.4
0.000753293

HGFA_HUMAN
LHKPGVYTR_357.5_479.3
0.000909298

HABP2_HUMAN
FLNWIK_410.7_561.3
0.001282014

FETUA_HUMAN
HTLNQIDEVK_598.8_951.5
0.001389792

AFAM_HUMAN
DADPDTFFAK_563.8_825.4
0.001498237

B2MG_HUMAN
VNHVTLSQPK_374.9_459.3
0.001559862

ALS_HUMAN
IRPHTFTGLSGLR_485.6_545.3
0.001612361

A1BG_HUMAN
LLELTGPK_435.8_644.4
0.002012656

F13B_HUMAN
LIENGYFHPVK_439.6_343.2
0.00275216

ITIH2_HUMAN
LSNENHGIAQR_413.5_519.8
0.00356561

APOC3_HUMAN
DALSSVQESQVAQQAR_573.0_672.4
0.00392745

F13B_HUMAN
LIENGYFHPVK_439.6_627.4
0.00434836

PEDF_HUMAN
TVQAVLTVPK_528.3_428.3
0.00482765

PLMN_HUMAN
YEFLNGR_449.7_293.1
0.007325436

HEMO_HUMAN
QGHNSVFLIK_381.6_520.4
0.009508516

FETUA_HUMAN
HTLNQIDEVK_598.8_958.5
0.010018936

CO5_HUMAN
LQGTLPVEAR_542.3_842.5
0.011140661

PLMN_HUMAN
YEFLNGR_449.7_606.3
0.01135322

CO5_HUMAN
TLLPVSKPEIR_418.3_288.2
0.015045275

HABP2_HUMAN
FLNWIK_410.7_560.3
0.01523134

APOC3_HUMAN
DALSSVQESQVAQQAR_573.0_502.3
0.01584708

CO5_HUMAN
LQGTLPVEAR_542.3_571.3
0.017298064

CFAB_HUMAN
DISEVVTPR_508.3_472.3
0.021743221

CERU_HUMAN
TTIEKPVWLGFLGPIIK_638.0_640.4
0.02376225

CO8G_HUMAN
SLPVSDSVLSGFEQR_810.9_723.3
0.041150397

CO8G_HUMAN
FLQEQGHR_338.8_497.3
0.042038143

CO5_HUMAN
VFQFLEK_455.8_811.4
0.043651929

CO8B_HUMAN
QALEEFQK_496.8_680.3
0.04761631

TABLE 61

Univariate p-values for Peak Area Ratios in Gestation

Age at Birth Linear Model

UniProt_ID
Transition
pvalue

PSG9_HUMAN
DVLLLVHNLPQNLPGYFWYK_810.4_960.5
0.000431547

B2MG_HUMAN
VEHSDLSFSK_383.5_468.2
0.000561148

PSG9_HUMAN
DVLLLVHNLPQNLPGYFWYK_810.4_328.2
0.000957509

ENPP2_HUMAN
TYLHTYESEI_628.3_908.4
0.001058809

THBG_HUMAN
AVLHIGEK_289.5_292.2
0.001180484

ENPP2_HUMAN
WWGGQPLWITATK_772.4_373.2
0.001524983

PSG9_HUMAN
LFIPQITR_494.3_614.4
0.001542932

ENPP2_HUMAN
WWGGQPLWITATK_772.4_929.5
0.002047607

ENPP2_HUMAN
TYLHTYESEI_628.3_515.3
0.003087492

PSG9_HUMAN
LFIPQITR_494.3_727.4
0.00477154

PSG9_HUMAN
DVLLLVHNLPQNLPGYFWYK_810.4_215.1
0.004824351

THBG_HUMAN
AVLHIGEK_289.5_348.7
0.006668084

AFAM_HUMAN
LPNNVLQEK_527.8_730.4
0.006877647

ADA12_HUMAN
FGFGGSTDSGPIR_649.3_745.4
0.011738104

PEDF_HUMAN
TVQAVLTVPK_528.3_855.5
0.013349511

A1BG_HUMAN
LLELTGPK_435.8_227.2
0.015793885

ITIH3_HUMAN
ALDLSLK_380.2_185.1
0.016080436

ADA12_HUMAN
FGFGGSTDSGPIR_649.3_946.5
0.017037089

B2MG_HUMAN
VEHSDLSFSK_383.5_234.1
0.017072093

CO6_HUMAN
ALNHLPLEYNSALYSR_621.0_696.4
0.024592775

TRFL_HUMAN
YYGYTGAFR_549.3_450.3
0.030890831

AFAM_HUMAN
DADPDTFFAK_563.8_302.1
0.033791429

CO6_HUMAN
ALNHLPLEYNSALYSR_621.0_538.3
0.034865341

AFAM_HUMAN
LPNNVLQEK_527.8_844.5
0.039880594

PEDF_HUMAN
TVQAVLTVPK_528.3_428.3
0.040854402

PLMN_HUMAN
EAQLPVIENK_570.8_329.2
0.041023812

LBP_HUMAN
ITLPDFTGDLR_624.3_920.5
0.042276813

CO8G_HUMAN
VQEAHLTEDQIFYFPK_655.7_701.4
0.042353851

PLMN_HUMAN
YEFLNGR_449.7_606.3
0.04416504

B2MG_HUMAN
VNHVTLSQPK_374.9_459.3
0.045458409

CFAB_HUMAN
DISEVVTPR_508.3_472.3
0.046493405

INHBE_HUMAN
ALVLELAK_428.8_331.2
0.04789353

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_ENPAVIDFELAPIVDLVR_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_IIGGSDADIK_494.8_260.2
59
218.069476

IL1A_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_WILTAAHTLYPK_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_IALGGLLFPASNLR_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_410.7_561.3
1
3501.905733

ADA12_FGFGGSTDSGPIR_649.3_946.5
2
3136.589992

A1BG_LLELTGPK_435.8_227.2
3
2387.891934

B2MG_VEHSDLSFSK_383.5_234.1
4
1431.31771

ADA12_FGFGGSTDSGPIR_649.3_745.4
5
1400.917331

B2MG_VEHSDLSFSK_383.5_468.2
6
1374.453629

APOB_IEGNLIFDPNNYLPK_874.0_414.2
7
1357.812445

PSG9_DVLLLVHNLPQNLPGYFWYK_810.4_960.5
8
1291.934596

A1BG_LLELTGPK_435.8_644.4
9
1138.712941

ITIH3_ALDLSLK_380.2_185.1
10
1137.127027

ENPP2_TYLHTYESEI_628.3_908.4
11
1041.036693

IL12B_YENYTSSFFIR_713.8_293.1
12
970.1662913

ENPP2_WWGGQPLWITATK_772.4_373.2
13
953.0631062

ENPP2_TYLHTYESEI_628.3_515.3
14
927.3512901

PSG9_LFIPQITR_494.3_614.4
15
813.9965357

MAGE
16
742.2425022

ENPP2_WWGGQPLWITATK_772.4_929.5
17
731.5206413

CERU_TTIEKPVWLGFLGPIIK_638.0_640.4
18
724.7745695

ITIH3_ALDLSLK_380.2_575.3
19
710.1982467

PSG2_IHPSYTNYR_384.2_452.2
20
697.4750893

ITIH1_LWAYLTIQELLAK_781.5_371.2
21
644.7416886

INHBE_ALVLELAK_428.8_331.2
22
643.008853

HGFA_LHKPGVYTR_357.5_692.4
23
630.8698445

TRFL_YYGYTGAFR_549.3_450.3
24
609.5866675

THBG_AVLHIGEK_289.5_348.7
25
573.9320948

GELS_TASDFITK_441.7_710.4
26
564.3288862

PSG9_LFIPQITR_494.3_727.4
27
564.1749327

VGFR3_SGVDLADSNQK_567.3_662.3
28
563.8087791

INHA_TTSDGGYSFK_531.7_860.4
29
554.210214

PSG9_DVLLLVHNLPQNLPGYFWYK_810.4_328.2
30
545.1743627

HYOU1_LPATEKPVLLSK_432.6_347.2
31
541.6208032

CO8G_VQEAHLTEDQIFYFPK_655.7_701.4
32
541.3193428

BMI
33
540.5028818

HGFA_LHKPGVYTR_357.5_479.3
34
536.6051948

PSG2_IHPSYTNYR_384.2_338.2
35
536.5363489

GELS_AQPVQVAEGSEPDGFWEALGGK_758.0_623.4
36
536.524931

PSG6_SNPVTLNVLYGPDLPR_585.7_654.4
37
520.108646

HABP2_FLNWIK_410.7_560.3
38
509.0707814

PGH1_ILPSVPK_377.2_527.3
39
503.593718

HYOU1_LPATEKPVLLSK_432.6_460.3
40
484.047422

CO6_ALNHLPLEYNSALYSR_621.0_696.4
41
477.8773179

INHBE_ALVLELAK_428.8_672.4
42
459.1998276

PLMN_LSSPAVITDK_515.8_743.4
43
452.9466414

PSG9_DVLLLVHNLPQNLPGYFWYK_810.4_215.1
44
431.8528248

BGH3_LTLLAPLNSVFK_658.4_875.5
45
424.2540315

AFAM_LPNNVLQEK_527.8_730.4
46
421.4953221

ITIH2_LSNENHGIAQR_413.5_519.8
47
413.1231437

GELS_TASDFITK_441.7_781.4
48
404.2679723

FETUA_AHYDLR_387.7_566.3
49
400.4711207

CERU_TTIEKPVWLGFLGPIIK_638.0_844.5
50
396.2873451

PSGx_ILILPSVTR_506.3_785.5
51
374.5672526

APOB_SVSLPSLDPASAK_636.4_885.5
52
371.1416438

FLNA_TGVAVNKPAEFTVDAK_549.6_258.1
53
370.4175588

PLMN_YEFLNGR_449.7_606.3
54
367.2768078

PSGx_ILILPSVTR_506.3_559.3
55
365.7704321

	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 (1)