BIOMARKERS AND METHODS FOR PREDICTING PREECLAMPSIA

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Jun. 6, 2014, is named 13271-012-999_SL.txt and is 191,037 bytes in size.

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

BACKGROUND

Preeclampsia (PE), a pregnancy-specific multi-system disorder characterized by hypertension and excess protein excretion in the urine, is a leading cause of maternal and fetal morbidity and mortality worldwide. Preeclampsia affects at least 5-8% of all pregnancies and accounts for nearly 18% of maternal deaths in the United States. The disorder is probably multifactorial, although most cases of preeclampsia are characterized by abnormal maternal uterine vascular remodeling by fetally derived placental trophoblast cells.

Complications of preeclampsia can include compromised placental blood flow, placental abruption, eclampsia, HELLP syndrome (hemolysis, elevated liver enzymes and low platelet count), acute renal failure, cerebral hemorrhage, hepatic failure or rupture, pulmonary edema, disseminated intravascular coagulation and future cardiovascular disease. Even a slight increase in blood pressure can be a sign of preeclampsia. While symptoms can include swelling, sudden weight gain, headaches and changes in vision, some women remain asymptomatic.

Management of preeclampsia consists of two options: delivery or observation. Management decisions depend on the gestational age at which preeclampsia is diagnosed and the relative state of health of the fetus. The only cure for preeclampsia is delivery of the fetus and placenta. However, the decision to deliver involves balancing the potential benefit to the fetus of further in utero development with fetal and maternal risk of progressive disease, including the development of eclampsia, which is preeclampsia complicated by maternal seizures.

There is a great need to identify women at risk for preeclampsia as most currently available tests fail to predict the majority of women who eventually develop preeclampsia. Women identified as high-risk can be scheduled for more intensive antenatal surveillance and prophylactic interventions. Reliable early detection of preeclampsia would enable planning appropriate monitoring and clinical management, potentially providing the early identification of disease complications. Such monitoring and management might include: more frequent assessment of blood pressure and urinary protein concentration, uterine artery doppler measurement, ultrasound assessment of fetal growth and prophylactic treatment with aspirin. Finally, reliable antenatal identification of preeclampsia 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 developing preeclampsia. Related advantages are provided as well.

SUMMARY

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

In one aspect, the invention provides a panel of isolated biomarkers comprising N of the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. 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 FSVVYAK (SEQ ID NO: 1), SPELQAEAK (SEQ ID NO: 2), VNHVTLSQPK (SEQ ID NO: 3), SSNNPHSPIVEEFQVPYNK (SEQ ID NO: 4), and VVGGLVALR (SEQ ID NO: 5). In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of LDFHFSSDR (SEQ ID NO: 6), TVQAVLTVPK (SEQ ID NO: 7), GPGEDFR (SEQ ID NO: 8), ETLLQDFR (SEQ ID NO: 9), ATVVYQGER (SEQ ID NO: 10), GFQALGDAADIR (SEQ ID NO: 11). In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of FSVVYAK (SEQ ID NO: 1), SPELQAEAK (SEQ ID NO: 2), VNHVTLSQPK (SEQ ID NO: 3), SSNNPHSPIVEEFQVPYNK (SEQ ID NO: 4), VVGGLVALR (SEQ ID NO: 5), LDFHFSSDR (SEQ ID NO: 6), TVQAVLTVPK (SEQ ID NO: 7), GPGEDFR (SEQ ID NO: 8), ETLLQDFR (SEQ ID NO: 9), ATVVYQGER (SEQ ID NO: 10), and GFQALGDAADIR (SEQ ID NO: 11).

In some embodiments, the invention provides a biomarker panel comprising at least two of the isolated biomarkers selected from the group consisting of alpha-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (C1S), and retinol binding protein 4 (RBP4 or RET4). In additional embodiments, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of alpha-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (C1S), and retinol binding protein 4 (RBP4 or RET4).

In some embodiments, the invention provides a biomarker panel comprising at least two of the isolated biomarkers selected from the group consisting of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha-1-microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG).

In other embodiments, the invention provides a biomarker panel comprising alpha-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, subcomponent (C1S), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to L1CAM (CHL1), complement component C5 (C5 or CO5), complement component C8 beta chain (C8B or CO8B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), and plasminogen (PLMN). In another aspect, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of alpha-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (C is), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to L1CAM (CHL1), complement component C5 (C5 or CO5), complement component C8 beta chain (C8B or CO8B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), and plasminogen (PLMN).

Also provided by the invention is a method of determining probability for preeclampsia in a pregnant female comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22 in a biological sample obtained from the pregnant female, and analyzing the measurable feature to determine the probability for preeclampsia in the pregnant female. In some embodiments, a measurable feature comprises fragments or derivatives of each of the N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. 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 2, 3, 4, 5 and 7 through 22, 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 preeclampsia in a pregnant female further encompass detecting a measurable feature for one or more risk indicia associated with preeclampsia.

In some embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprises 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 preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FSVVYAK (SEQ ID NO: 1), SPELQAEAK (SEQ ID NO: 2), VNHVTLSQPK (SEQ ID NO: 3), SSNNPHSPIVEEFQVPYNK (SEQ ID NO: 4), and VVGGLVALR (SEQ ID NO: 5).

In further embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of LDFHFSSDR (SEQ ID NO: 6), TVQAVLTVPK (SEQ ID NO: 7), GPGEDFR (SEQ ID NO: 8), ETLLQDFR (SEQ ID NO: 9), ATVVYQGER (SEQ ID NO: 10), GFQALGDAADIR (SEQ ID NO: 11).

In additional embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FSVVYAK (SEQ ID NO: 1), SPELQAEAK (SEQ ID NO: 2), VNHVTLSQPK (SEQ ID NO: 3), SSNNPHSPIVEEFQVPYNK (SEQ ID NO: 4), VVGGLVALR (SEQ ID NO: 5), LDFHFSSDR (SEQ ID NO: 6), TVQAVLTVPK (SEQ ID NO: 7), GPGEDFR (SEQ ID NO: 8), ETLLQDFR (SEQ ID NO: 9), ATVVYQGER (SEQ ID NO: 10), and GFQALGDAADIR (SEQ ID NO: 11).

In other embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of alpha-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (C1S), and retinol binding protein 4 (RBP4 or RET4).

In some embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha-1-microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG).

In further embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of alpha-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, subcomponent (C1S), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to L1CAM (CHL1), complement component C5 (C5 or CO5), complement component C8 beta chain (C8B or CO8B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), and plasminogen (PLMN).

In some embodiments of the methods of determining probability for preeclampsia in a pregnant female, the probability for preeclampsia in the pregnant female is calculated based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. In some embodiments, the disclosed methods for determining the probability of preeclampsia 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 preeclampsia in a pregnant female encompass an initial step of providing a biomarker panel comprising N of the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. In additional embodiments, the disclosed methods of determining probability for preeclampsia 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 preeclampsia 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 comprises one or more selected from the group of consisting of more frequent assessment of blood pressure and urinary protein concentration, uterine artery doppler measurement, ultrasound assessment of fetal growth and prophylactic treatment with aspirin.

In further embodiments, the disclosed methods of determining probability for preeclampsia 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 preeclampsia 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 preeclampsia in a pregnant female encompasses logistic regression.

In some embodiments, the invention provides a method of determining probability for preeclampsia in a pregnant female encompasses quantifying in a biological sample obtained from the pregnant female an amount of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22; multiplying the amount by a predetermined coefficient, and determining the probability for preeclampsia in the pregnant female comprising adding the individual products to obtain a total risk score that corresponds to the probability.

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

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 developing in the future or presently suffering from preeclampsia relative to matched controls. The present disclosure is further based, in part, on the unexepected discovery that panels combining one or more of these proteins and peptides can be utilized in methods of determining the probability for preeclampsia in a pregnant female with relatively high sensitivity and specificity. These proteins and peptides disclosed herein serve as biomarkers for classifying test samples, predicting a probability of preeclampsia, monitoring of progress of preeclampsia 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 preeclampsia in a pregnant female. One major advantage of the present disclosure is that risk of developing preeclampsia can be assessed early during pregnancy so that management of the condition can be initiated in a timely fashion. Sibai, Hypertension. In: Gabbe et al., eds. Obstetrics: Normal and Problem Pregnancies. 6th ed. Philadelphia, Pa.: Saunders Elsevier; 2012:chap 35. The present invention is of particular benefit to asymptomatic females 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 preeclampsia 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 preeclampsia, and inputting the dataset into an analytic process that uses the dataset to generate a result useful in determining probability for preeclampsia 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, 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 discover 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 preeclampsia in a pregnant female include, but are not limited to, one or more of the isolated biomarkers listed in Tables 2, 3, 4, 5, and 7 through 22. 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 age, race, ethnicity, medical history, past pregnancy history, and obstetrical history. Such additional markers can include, for example, age, prepregnancy weight, ethnicity, race; the presence, absence or severity of diabetes, hypertension, heart disease, kidney disease; the incidence and/or frequency of prior preeclampsia, prior preeclampsia; the presence, absence, frequency or severity of present or past smoking, illicit drug use, alcohol use; the presence, absence or severity of bleeding after the 12th gestational week; cervical cerclage and transvaginal cervical length. 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 2, 3, 4, 5, and 7 through 22. 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 preeclampsia in a pregnant female.

While certain of the biomarkers listed in Tables 2, 3, 4, 5, and 7 through 22 are useful alone for determining the probability for preeclampsia 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 LDFHFSSDR (SEQ ID NO: 6), TVQAVLTVPK (SEQ ID NO: 7), GPGEDFR (SEQ ID NO: 8), ETLLQDFR (SEQ ID NO: 9), ATVVYQGER (SEQ ID NO: 10), GFQALGDAADIR (SEQ ID NO: 11). In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, five of the isolated biomarkers consisting of an amino acid sequence selected from LDFHFSSDR (SEQ ID NO: 6), TVQAVLTVPK (SEQ ID NO: 7), GPGEDFR (SEQ ID NO: 8), ETLLQDFR (SEQ ID NO: 9), ATVVYQGER (SEQ ID NO: 10), GFQALGDAADIR (SEQ ID NO: 11).

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 FSVVYAK (SEQ ID NO: 1), SPELQAEAK (SEQ ID NO: 2), VNHVTLSQPK (SEQ ID NO: 3), SSNNPHSPIVEEFQVPYNK (SEQ ID NO: 4), VVGGLVALR (SEQ ID NO: 5), LDFHFSSDR (SEQ ID NO: 6), TVQAVLTVPK (SEQ ID NO: 7), GPGEDFR (SEQ ID NO: 8), ETLLQDFR (SEQ ID NO: 9), ATVVYQGER (SEQ ID NO: 10), and GFQALGDAADIR (SEQ ID NO: 11). In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, five of the isolated biomarkers consisting of an amino acid sequence selected from FSVVYAK (SEQ ID NO: 1), SPELQAEAK (SEQ ID NO: 2), VNHVTLSQPK (SEQ ID NO: 3), SSNNPHSPIVEEFQVPYNK (SEQ ID NO: 4), VVGGLVALR (SEQ ID NO: 5), LDFHFSSDR (SEQ ID NO: 6), TVQAVLTVPK (SEQ ID NO: 7), GPGEDFR (SEQ ID NO: 8), ETLLQDFR (SEQ ID NO: 9), ATVVYQGER (SEQ ID NO: 10), and GFQALGDAADIR (SEQ ID NO: 11).

In some embodiments, the panel of isolated biomarkers comprises one or more peptides comprising a fragment from alpha-1-microglobulin (AMBP) Traboni and Cortese, Nucleic Acids Res. 14 (15), 6340 (1986); ADP/ATP translocase 3 (ANT3) Cozens et al., J. Mol. Biol. 206 (2), 261-280 (1989) (NCBI Reference Sequence: NP_—001627.2); apolipoprotein A-II (APOA2) Fullerton et al., Hum. Genet. 111 (1), 75-87 (2002) GenBank: AY100524.1); apolipoprotein B (APOB) Knott et al., Nature 323, 734-738 (1986) (GenBank: EAX00803.1); apolipoprotein C-III (APOC3), Fullerton et al., Hum. Genet. 115 (1), 36-56 (2004)(GenBank: AAS68230.1); beta-2-microglobulin (B2MG) Cunningham et al., Biochemistry 12 (24), 4811-4822 (1973) (GenBank: AI686916.1); complement component 1, s subcomponent (C1S) Mackinnon et al., Eur. J. Biochem. 169 (3), 547-553 (1987), and retinol binding protein 4 (RBP4 or RET4) Rask et al., Ann. N.Y. Acad. Sci. 359, 79-90 (1981) (UniProtKB/Swiss-Prot: P02753.3).

In some embodiments, the panel of isolated biomarkers comprises one or more peptides comprising a fragment from cell adhesion molecule with homology to L1CAM (close homolog of L1) (CHL1) (GenBank: AAI43497.1), complement component C5 (C5 or CO5) Haviland, J. Immunol. 146 (1), 362-368 (1991)(GenBank: AAA51925.1); Complement component C8 beta chain (C8B or CO8B) Howard et al., Biochemistry 26 (12), 3565-3570 (1987) (NCBI Reference Sequence: NP_—000057.1), endothelin-converting enzyme 1 (ECE1) Xu et al., Cell 78 (3), 473-485 (1994) (NCBI Reference Sequence: NM_—001397.2; NP_—001388.1); coagulation factor XIII, B polypeptide (F13B) Grundmann et al., Nucleic Acids Res. 18 (9), 2817-2818 (1990) (NCBI Reference Sequence: NP_—001985.2); Interleukin 5 (IL5), Murata et al., J. Exp. Med. 175 (2), 341-351 (1992) (NCBI Reference Sequence: NP_—000870.1), Peptidase D (PEPD) Endo et al., J. Biol. Chem. 264 (8), 4476-4481 (1989) (UniProtKB/Swiss-Prot: P12955.3); Plasminogen (PLMN) Petersen et al., J. Biol. Chem. 265 (11), 6104-6111 (1990), (NCBI Reference Sequences: NP_—000292.1 NP_—001161810.1).

In additional embodiments, the invention provides a panel of isolated biomarkers comprising N of the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. 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 FSVVYAK (SEQ ID NO: 1), SPELQAEAK (SEQ ID NO: 2), VNHVTLSQPK (SEQ ID NO: 3), SSNNPHSPIVEEFQVPYNK (SEQ ID NO: 4), and VVGGLVALR (SEQ ID NO: 5).

In further embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of alpha-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (C1S), and retinol binding protein 4 (RBP4 or RET4). In another embodiment, the invention provides a biomarker panel comprising at least three isolated biomarkers selected from the group consisting of alpha-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (C1S), and retinol binding protein 4 (RBP4 or RET4).

In further embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha-1-microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG). In another embodiment, the invention provides a biomarker panel comprising at least three isolated biomarkers selected from the group consisting of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha-1-microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG).

In some embodiments, the invention provides a biomarker panel comprising alpha-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, subcomponent (C1S), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to L1CAM (CHL1), complement component C5 (C5 or CO5), complement component C8 beta chain (C8B or CO8B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), and plasminogen (PLMN). In another aspect, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of alpha-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (C1S), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to L1CAM (CHL1), complement component C5 (C5 or CO5), complement component C8 beta chain (C8B or CO8B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), and plasminogen (PLMN).

In some embodiments, the invention provides a biomarker panel comprising Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha-1-microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG). In another aspect, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha-1-microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG).

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.”

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

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 preeclampsia. 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 preeclampsia in a pregnant female, the method comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22 in a biological sample obtained from the pregnant female, and analyzing the measurable feature to determine the probability for preeclampsia 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 2, 3, 4, 5 and 7 through 22. 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 2, 3, 4, 5 and 7 through 22, combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female.

In some embodiments, the present invention describes a method for predicting the time to onset of preeclamspsia 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 2, 3, 4, 5 and 7 through 22 in said biological sample; (c) multiplying or thresholding said amount by a predetermined coefficient, (d) determining predicted onset of said preeclampsia in said pregnant female comprising adding said individual products to obtain a total risk score that corresponds to said predicted onset of said preeclampsia in said pregnant female. Although described and exemplified with reference to methods of determining probability for preeclampsia in a pregnant female, the present disclosure is similarly applicable to the method of predicting time to onset of 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 preeclampsia in a pregnant female comprises 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 preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FSVVYAK (SEQ ID NO: 1), SPELQAEAK (SEQ ID NO: 2), VNHVTLSQPK (SEQ ID NO: 3), SSNNPHSPIVEEFQVPYNK (SEQ ID NO: 4), and VVGGLVALR (SEQ ID NO: 5).

In further embodiments, the disclosed methods of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FSVVYAK (SEQ ID NO: 1), SPELQAEAK (SEQ ID NO: 2), VNHVTLSQPK (SEQ ID NO: 3), SSNNPHSPIVEEFQVPYNK (SEQ ID NO: 4), VVGGLVALR (SEQ ID NO: 5), LDFHFSSDR (SEQ ID NO: 6), TVQAVLTVPK (SEQ ID NO: 7), GPGEDFR (SEQ ID NO: 8), ETLLQDFR (SEQ ID NO: 9), ATVVYQGER (SEQ ID NO: 10), and GFQALGDAADIR (SEQ ID NO: 11)

In additional embodiments, the method of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of alpha-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (C1S), and retinol binding protein 4 (RBP4 or RET4).

In additional embodiments, the method of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha-1-microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG).

In further embodiments, the disclosed method of determining probability for preeclampsia in a pregnant female comprises detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of alpha-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, subcomponent (C1S), and retinol binding protein 4 (RBP4 or RET4) cell adhesion molecule with homology to L1CAM (CHL1), complement component C5 (C5 or CO5), complement component C8 beta chain (C8B or CO8B), endothelin-converting enzyme 1 (ECE1), coagulation factor XIII, B polypeptide (F13B), interleukin 5 (IL5), Peptidase D (PEPD), plasminogen (PLMN), of Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha-1-microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG).

In additional embodiments, the methods of determining probability for preeclampsia in a pregnant female further encompass detecting a measurable feature for one or more risk indicia associated with preeclampsia. In additional embodiments the risk indicia are selected form the group consisting of history of preeclampsia, first pregnancy, age, obesity, diabetes, gestational diabetes, hypertension, kidney disease, multiple pregnancy, interval between pregnancies, migraine headaches, rheumatoid arthritis, and lupus.

A “measurable feature” is any property, characteristic or aspect that can be determined and correlated with the probability for preeclampsia in a subject. 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 age, race, ethnicity, medical history, past pregnancy history, obstetrical history. For a risk indicium, a measurable feature can include, for example, age, prepregnancy weight, ethnicity, race; the presence, absence or severity of diabetes, hypertension, heart disease, kidney disease; the incidence and/or frequency of prior preeclampsia, prior preeclampsia; the presence, absence, frequency or severity of present or past smoking, illicit drug use, alcohol use; the presence, absence or severity of bleeding after the 12th gestational week; cervical cerclage and transvaginal cervical length.

In some embodiments of the disclosed methods of determining probability for preeclampsia in a pregnant female, the probability for preeclampsia in the pregnant female is calculated based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. In some embodiments, the disclosed methods for determining the probability of preeclampsia encompass detecting and/or quantifying one or more biomarkers using mass sprectrometry, a capture agent or a combination thereof.

In some embodiments, the method of determining probability for preeclampsia 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 preeclampsia. 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 Table 1. 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. 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.

Preeclampsia refers to a condition characterized by high blood pressure and excess protein in the urine (proteinuria) after 20 weeks of pregnancy in a woman who previously had normal blood pressure. Preeclampsia encompasses Eclampsia, a more severe form of preeclampsia that is further characterized by seizures. Preeclampsia can be further classified as mild or severe depending upon the severity of the clinical symptoms. While preeclampsia usually develops during the second half of pregnancy (after 20 weeks), it also can develop shortly after birth or before 20 weeks of pregnancy.

Preeclampsia has been characterized by some investigators as 2 different disease entities: early-onset preeclampsia and late-onset preeclampsia, both of which are intended to be encompassed by reference to preeclampsia herein. Early-onset preeclampsia is usually defined as preeclampsia that develops before 34 weeks of gestation, whereas late-onset preeclampsia develops at or after 34 weeks of gestation. Preclampsia also includes postpartum preeclampsia is a less common condition that occurs when a woman has high blood pressure and excess protein in her urine soon after childbirth. Most cases of postpartum preeclampsia develop within 48 hours of childbirth. However, postpartum preeclampsia sometimes develops up to four to six weeks after childbirth. This is known as late postpartum preeclampsia.

Clinical criteria for diagnosis of preeclampsia are well established, for example, blood pressure of at least 140/90 mm Hg and urinary excretion of at least 0.3 grams of protein in a 24-hour urinary protein excretion (or at least +1 or greater on dipstick testing), each on two occasions 4-6 hours apart. Severe preeclampsia generally refers to a blood pressure of at least 160/110 mm Hg on at least 2 occasions 6 hours apart and greater than 5 grams of protein in a 24-hour urinary protein excretion or persistent +3 proteinuria on dipstick testing. Preeclampsia can include HELLP syndrome (hemolysis, elevated liver enzymes, low platelet count). Other elements of preeclampsia can include in-utero growth restriction (IUGR) in less than the 10% percentile according to the US demographics, persistent neurologic symptoms (headache, visual disturbances), epigastric pain, oliguria (less than 500 mL/24 h), serum creatinine greater than 1.0 mg/dL, elevated liver enzymes (greater than two times normal), thrombocytopenia (<100,000 cells/μL).

In some embodiments, the pregnant female was between 17 and 28 weeks of gestation at the time the biological sample was collected. In other embodiments, the pregnant female was 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 was collected. In further embodiments, the pregnant female was 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 was 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 Table 1. 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 Table 1, 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 preeclampsia in a pregnant female is based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Table 1. 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) 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 b rapidly toggling between the different precarsor/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 (APC)-MS); APCI-MS/MS; APCI-(MS)_n; 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.

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 fluorescence-activated cell sorting (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, immunopercipitation, 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 invention.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Some embodiments disclosed herein relate to diagnostic and prognostic methods of determining the probability for preeclampsia 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 preeclampsia 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 preeclampsia, to monitor the progress of preeclampsia or the progress of treatment protocols, to assess the severity of preeclampsia, to forecast the outcome of preeclampsia and/or prospects of recovery or birth at full term, or to aid in the determination of a suitable treatment for preeclampsia.

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 preeclampsia in a pregnant female encompasses the use of a predictive model. In further embodiments, analyzing a measurable feature to determine the probability for preeclampsia 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 preeclampsia 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.

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. AUC (area under the 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 0.7, 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 preeclampsia, a robust data set, comprising known control samples and samples corresponding to the preeclampsia 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 preeclampsia 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 preeclampsia, and subjects with no event are considered censored at the time of giving birth. Given the specific pregnancy outcome (preeclampsia 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 preeclampsia. 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 preeclampsia and predicted time to a preeclampsia event in said pregnant female is provided. Also, algorithms provide information regarding the probability for preeclampsia in the pregnant female.

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 AUROC, 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 to predict Gestational

Age of time to event (preeclampsia).

SEQ ID NO:

TSDQIHFFFAK_447.56_512.3
13
0.00
ANT3_HUMAN

DPNGLPPEAQK_583.3_669.4
14
0.00
RET4_HUMAN

SVSLPSLDPASAK_636.35_885.5
15
0.00
APOB_HUMAN

SSNNPHSPIVEEFQVPYNK_729.36_261.2
4
0.00
C1S_HUMAN

IEGNLIFDPNNYLPK_873.96_414.2
16
0.00
APOB_HUMAN

YWGVASFLQK_599.82_849.5
17
0.00
RET4_HUMAN

ITENDIQIALDDAK_779.9_632.3
18
0.00
APOB_HUMAN

IEGNLIFDPNNYLPK_873.96_845.5
16
0.00
APOB_HUMAN

GWVTDGFSSLK_598.8_953.5
19
0.00
APOC3_HUMAN

TGISPLALIK_506.82_741.5
20
0.00
APOB_HUMAN

SVSLPSLDPASAK_636.35_473.3
15
0.00
APOB_HUMAN

IIGGSDADIK_494.77_762.4
21
0.00
C1S_HUMAN

TGISPLALIK_506.82_654.5
20
0.00
APOB_HUMAN

TLLIANETLR_572.34_703.4
22
0.00
IL5_HUMAN

YWGVASFLQK_599.82_350.2
17
0.00
RET4_HUMAN

VSALLTPAEQTGTWK_801.43_371.2
23
0.00
APOB_HUMAN

DPNGLPPEAQK_583.3_497.2
14
0.00
RET4_HUMAN

VNHVTLSQPK_561.82_673.4
3
0.00
B2MG_HUMAN

DALSSVQESQVAQQAR_572.96_502.3
24
0.00
APOC3_HUMAN

IAQYYYTFK_598.8_884.4
25
0.00
F13B_HUMAN

IEEIAAK_387.22_531.3
26
0.00
CO5_HUMAN

GWVTDGFSSLK_598.8_854.4
19
0.00
APOC3_HUMAN

VNHVTLSQPK_561.82_351.2
3
0.00
B2MG_HUMAN

ITENDIQIALDDAK_779.9_873.5
18
0.00
APOB_HUMAN

VSALLTPAEQTGTWK_801.43_585.4
23
0.00
APOB_HUMAN

VILGAHQEVNLEPHVQEIEVSR_832.78_860.4
27
0.00
PLMN_HUMAN

SPELQAEAK_486.75_788.4
2
0.00
APOA2_HUMAN

SPELQAEAK_486.75_659.4
2
0.00
APOA2_HUMAN

DYWSTVK_449.72_620.3
28
0.00
APOC3_HUMAN

VPLALFALNR_557.34_620.4
29
0.00
PEPD_HUMAN

TSDQIHFFFAK_447.56_659.4
13
0.00
ANT3_HUMAN

DALSSVQESQVAQQAR_572.96_672.4
24
0.00
APOC3_HUMAN

VIAVNEVGR_478.78_284.2
30
0.00
CHL1_HUMAN

LLEVPEGR_456.76_686.3
31
0.00
C1S_HUMAN

VEPLYELVTATDFAYSSTVR_754.38_549.3
32
0.00
CO8B_HUMAN

HHGPTITAK_321.18_275.1
33
0.01
AMBP_HUMAN

ALNFGGIGVVVGHELTHAFDDQ
34
0.01
ECE1_HUMAN

GR_837.09_299.2

ETLLQDFR_511.27_565.3
9
0.01
AMBP_HUMAN

HHGPTITAK_321.18_432.3
33
0.01
AMBP_HUMAN

IIGGSDADIK_494.77_260.2
21
0.01
C1S_HUMAN

TABLE 2

Top 40 transitions with p-values less than 0.05 in univariate

Cox Proportional Hazards to predict Gestational Age of time

to event (preeclampsia), sorted by protein ID.

SEQ ID
cox

Transition
NO:
pvalues
protein

HHGPTITAK_321.18_275.1
33
0.01
AMBP_HUMAN

ETLLQDFR_511.27_565.3
9
0.01
AMBP_HUMAN

HHGPTITAK_321.18_432.3
33
0.01
AMBP_HUMAN

TSDQIHFFFAK_447.56_512.3
13
0.00
ANT3_HUMAN

TSDQIHFFFAK_447.56_659.4
13
0.00
ANT3_HUMAN

SPELQAEAK_486.75_788.4
2
0.00
APOA2_HUMAN

SPELQAEAK_486.75_659.4
2
0.00
APOA2_HUMAN

SVSLPSLDPASAK_636.35_885.5
15
0.00
APOB_HUMAN

IEGNLIFDPNNYLPK_873.96_414.2
16
0.00
APOB_HUMAN

ITENDIQIALDDAK_779.9_632.3
18
0.00
APOB_HUMAN

IEGNLIFDPNNYLPK_873.96_845.5
16
0.00
APOB_HUMAN

TGISPLALIK_506.82_741.5
20
0.00
APOB_HUMAN

SVSLPSLDPASAK_636.35_473.3
15
0.00
APOB_HUMAN

TGISPLALIK_506.82_654.5
20
0.00
APOB_HUMAN

VSALLTPAEQTGTWK_801.43_371.2
23
0.00
APOB_HUMAN

ITENDIQIALDDAK_779.9_873.5
18
0.00
APOB_HUMAN

VSALLTPAEQTGTWK_801.43_585.4
23
0.00
APOB_HUMAN

GWVTDGFSSLK_598.8_953.5
19
0.00
APOC3_HUMAN

DALSSVQESQVAQQAR_572.96_502.3
24
0.00
APOC3_HUMAN

GWVTDGFSSLK_598.8_854.4
19
0.00
APOC3_HUMAN

DYWSTVK_449.72_620.3
28
0.00
APOC3_HUMAN

DALSSVQESQVAQQAR_572.96_672.4
24
0.00
APOC3_HUMAN

VNHVTLSQPK_561.82_673.4
3
0.00
B2MG_HUMAN

VNHVTLSQPK_561.82_351.2
3
0.00
B2MG_HUMAN

SSNNPHSPIVEEFQVPYNK_729.36_261.2
4
0.00
C1S_HUMAN

IIGGSDADIK_494.77_762.4
21
0.00
C1S_HUMAN

LLEVPEGR_456.76_686.3
31
0.00
C1S_HUMAN

IIGGSDADIK_494.77_260.2
21
0.01
C1S_HUMAN

VIAVNEVGR_478.78_284.2
30
0.00
CHL1_HUMAN

IEEIAAK_387.22_531.3
26
0.00
CO5_HUMAN

VEPLYELVTATDFAYSSTVR_754.38_549.3
32
0.00
CO8B_HUMAN

ALNFGGIGVVVGHELTHAFDDQGR_837.09_299.2
34
0.01
ECE1_HUMAN

IAQYYYTFK_598.8_884.4
25
0.00
F13B_HUMAN

TLLIANETLR_572.34_703.4
22
0.00
IL5_HUMAN

VPLALFALNR_557.34_620.4
29
0.00
PEPD_HUMAN

VILGAHQEVNLEPHVQEIEVSR_832.78_860.4
27
0.00
PLMN_HUMAN

DPNGLPPEAQK_583.3_669.4
14
0.00
RET4_HUMAN

YWGVASFLQK_599.82_849.5
17
0.00
RET4_HUMAN

YWGVASFLQK_599.82_350.2
17
0.00
RET4_HUMAN

DPNGLPPEAQK_583.3_497.2
14
0.00
RET4_HUMAN

TABLE 3

Transitions selected by Cox stepwise AIC analysis

SEQ ID

Transition
NO:
coef
exp(coef)
se(coef)
z
Pr(>|z|)

Collection.Window.GA.in.Days

0.43
1.54E+00
0.19
2.22
0.03

IIGGSDADIK_494.77_762.4
21
44.40
1.91E+19
18.20
2.44
0.01

GGEGTGYFVDFSVR_745.85_869.5
35
6.91
1.00E+03
2.76
2.51
0.01

SPEQQETVLDGNLIIR_906.48_685.4
36
17.28
3.21E+07
7.49
2.31
0.02

EPGLCTWQSLR_673.83_790.4
37
−2.08
1.25E−01
1.02
−2.05
0.04

TABLE 4

Transitions selected by Cox lasso analysis

SEQ ID

Transition
NO:
coef
exp(coef)
se(coef)
z
Pr(>|z|)

Collection.Window.GA.in.Days

0.05069
1.052
0.02348
2.159
0.0309

SPELQAEAK_486.75_788.4
2
0.68781
1.98936
0.4278
1.608
0.1079

SSNNPHSPIVEEFQVPYNK_729.36_261.2
4
2.63659
13.96553
1.69924
1.552
0.1208

TABLE 5

Area under the ROC curve for individual analytes

to discriminate preeclampsia subjects from

non-preeclampsia subjects. The 196 transitions

with the highest ROC area are shown.

SEQ

ID

Transition
NO:
ROC area

SPELQAEAK_486.75_788.4
2
0.92

SSNNPHSPIVEEFQVPYNK_729.36_261.2
4
0.88

VNHVTLSQPK_561.82_673.4
3
0.85

TLLIANETLR_572.34_703.4
22
0.84

SSNNPHSPIVEEFQVPYNK_729.36_521.3
4
0.83

IIGGSDADIK_494.77_762.4
21
0.82

VVGGLVALR_442.29_784.5
5
0.82

ALNFGGIGVVVGHELTHAFDDQGR_837.09_299.2
34
0.81

DYWSTVK_449.72_620.3
28
0.81

FSVVYAK_407.23_579.4
1
0.81

GWVTDGFSSLK_598.8_953.5
19
0.81

IIGGSDADIK_494.77_260.2
21
0.81

LLEVPEGR_456.76_356.2
31
0.81

DALSSVQESQVAQQAR_572.96_672.4
24
0.80

DPNGLPPEAQK_583.3_497.2
14
0.80

FSVVYAK_407.23_381.2
1
0.80

LLEVPEGR_456.76_686.3
31
0.80

SPELQAEAK_486.75_659.4
2
0.80

VVLSSGSGPGLDLPLVLGLPLQLK_791.48_598.4
38
0.79

ETLLQDFR_511.27_565.3
9
0.79

VNHVTLSQPK_561.82_351.2
3
0.79

VVGGLVALR_442.29_685.4
5
0.79

YTTEIIK_434.25_603.4
39
0.79

DPNGLPPEAQK_583.3_669.4
14
0.78

EDTPNSVWEPAK_686.82_315.2
40
0.78

GWVTDGFSSLK_598.8_854.4
19
0.78

HHGPTITAK_321.18_432.3
33
0.78

LHEAFSPVSYQHDLALLR_699.37_251.2
41
0.78

GA.of.Time.to.Event.in.Days

0.77

DALSSVQESQVAQQAR_572.96_502.3
24
0.77

DYWSTVK_449.72_347.2
28
0.77

IAQYYYTFK_598.8_395.2
25
0.77

YWGVASFLQK_599.82_849.5
17
0.77

AHYDLR_387.7_288.2
42
0.76

EDTPNSVWEPAK_686.82_630.3
40
0.76

GDTYPAELYITGSILR_884.96_922.5
43
0.76

SVSLPSLDPASAK_636.35_885.5
15
0.76

TSESGELHGLTTEEEFVEGIYK_819.06_310.2
44
0.76

ALEQDLPVNIK_620.35_570.4
45
0.75

HHGPTITAK_321.18_275.1
33
0.75

IAQYYYTFK_598.8_884.4
25
0.75

ITENDIQIALDDAK_779.9_632.3
18
0.75

LPNNVLQEK_527.8_844.5
46
0.75

YWGVASFLQK_599.82_350.2
17
0.75

FQLPGQK_409.23_276.1
47
0.75

HTLNQIDEVK_598.82_958.5
48
0.75

VVLSSGSGPGLDLPLVLGLPLQLK_791.48_768.5
38
0.75

DADPDTFFAK_563.76_302.1
49
0.74

DADPDTFFAK_563.76_825.4
49
0.74

FQLPGQK_409.23_429.2
47
0.74

HFQNLGK_422.23_527.2
50
0.74

VIAVNEVGR_478.78_284.2
30
0.74

VPLALFALNR_557.34_620.4
29
0.74

ETLLQDFR_511.27_322.2
9
0.73

FNAVLTNPQGDYDTSTGK_964.46_262.1
51
0.73

SVSLPSLDPASAK_636.35_473.3
15
0.73

AHYDLR_387.7_566.3
42
0.72

ALNHLPLEYNSALYSR_620.99_538.3
52
0.72

AWVAWR_394.71_258.1
53
0.72

AWVAWR_394.71_531.3
53
0.72

ETAASLLQAGYK_626.33_879.5
54
0.72

IALGGLLFPASNLR_481.29_657.4
55
0.72

IAPQLSTEELVSLGEK_857.47_533.3
56
0.72

ITENDIQIALDDAK_779.9_873.5
18
0.72

VAPEEHPVLLTEAPLNPK_652.03_869.5
57
0.71

EPGLCTWQSLR_673.83_375.2
37
0.71

IAPQLSTEELVSLGEK_857.47_333.2
56
0.71

SPEQQETVLDGNLIIR_906.48_699.3
36
0.71

VSALLTPAEQTGTWK_801.43_371.2
23
0.71

VSALLTPAEQTGTWK_801.43_585.4
23
0.71

VSEADSSNADWVTK_754.85_347.2
964
0.71

GDTYPAELYITGSILR_884.96_274.1
43
0.70

IPGIFELGISSQSDR_809.93_849.4
58
0.70

IQTHSTTYR_369.52_540.3
59
0.70

LLDSLPSDTR_558.8_890.4
60
0.70

QLGLPGPPDVPDHAAYHPF_676.67_299.2
61
0.70

SYELPDGQVITIGNER_895.95_251.1
62
0.70

VILGAHQEVNLEPHVQEIEVSR_832.78_860.4
27
0.70

WGAAPYR_410.71_577.3
63
0.69

DFHINLFQVLPWLK_885.49_543.3
64
0.69

LLDSLPSDTR_558.8_276.2
60
0.69

VEPLYELVTATDFAYSSTVR_754.38_549.3
32
0.69

VPTADLEDVLPLAEDITNILSK_789.43_841.4
65
0.69

GGEGTGYFVDFSVR_745.85_869.5
35
0.69

HTLNQIDEVK_598.82_951.5
48
0.69

LIENGYFHPVK_439.57_627.4
66
0.69

LPNNVLQEK_527.8_730.4
46
0.69

NKPGVYTDVAYYLAWIR_677.02_545.3
67
0.69

NTVISVNPSTK_580.32_845.5
68
0.69

QLGLPGPPDVPDHAAYHPF_676.67_263.1
61
0.69

YTTEIIK_434.25_704.4
39
0.69

LPDATPK_371.21_628.3
69
0.68

IEGNLIFDPNNYLPK_873.96_845.5
16
0.68

LEQGENVFLQATDK_796.4_822.4
70
0.68

TLYSSSPR_455.74_533.3
71
0.68

TLYSSSPR_455.74_696.3
71
0.68

VSEADSSNADWVTK_754.85_533.3
964
0.68

DGSPDVTTADIGANTPDATK_973.45_844.4
72
0.67

EWVAIESDSVQPVPR_856.44_486.2
73
0.67

IALGGLLFPASNLR_481.29_412.3
55
0.67

IEEIAAK_387.22_531.3
26
0.67

IEGNLIFDPNNYLPK_873.96_414.2
16
0.67

LYYGDDEK_501.72_726.3
74
0.67

TGISPLALIK_506.82_741.5
20
0.67

VPTADLEDVLPLAEDITNILSK_789.43_940.5
65
0.67

ADSQAQLLLSTVVGVFTAPGLHLK_822.46_983.6
75
0.66

AYSDLSR_406.2_577.3
76
0.66

DFHINLFQVLPWLK_885.49_400.2
64
0.66

DLHLSDVFLK_396.22_260.2
77
0.66

EWVAIESDSVQPVPR_856.44_468.3
73
0.66

FNAVLTNPQGDYDTSTGK_964.46_333.2
51
0.66

LSSPAVITDK_515.79_743.4
78
0.66

LYYGDDEK_501.72_563.2
74
0.66

SGFSFGFK_438.72_732.4
79
0.66

IIEVEEEQEDPYLNDR_995.97_777.4
80
0.66

AVYEAVLR_460.76_750.4
81
0.66

WGAAPYR_410.71_634.3
63
0.66

FTFTLHLETPKPSISSSNLNPR_829.44_874.4
82
0.65

DAQYAPGYDK_564.25_315.1
83
0.65

YGLVTYATYPK_638.33_334.2
84
0.65

DGSPDVTTADIGANTPDATK_973.45_531.3
72
0.65

ETAASLLQAGYK_626.33_679.4
54
0.65

ALNHLPLEYNSALYSR_620.99_696.4
52
0.65

DISEVVTPR_508.27_787.4
85
0.65

IS.2_662.3_313.1

0.65

IVLGQEQDSYGGK_697.35_261.2
86
0.65

IVLGQEQDSYGGK_697.35_754.3
86
0.65

TLEAQLTPR_514.79_685.4
87
0.65

VPVAVQGEDTVQSLTQGDGVAK_733.38_775.4
88
0.65

VAPEEHPVLLTEAPLNPK_652.03_568.3
57
0.64

ADSQAQLLLSTVVGVFTAPGLHLK_822.46_664.4
75
0.64

AEAQAQYSAAVAK_654.33_908.5
89
0.64

DISEVVTPR_508.27_472.3
85
0.64

ELLESYIDGR_597.8_710.3
90
0.64

TGISPLALIK_506.82_654.5
20
0.64

TNLESILSYPK_632.84_807.5
91
0.64

DAQYAPGYDK_564.25_813.4
83
0.63

LPTAVVPLR_483.31_755.5
92
0.63

DSPVLIDFFEDTER_841.9_512.3
93
0.63

FAFNLYR_465.75_712.4
94
0.63

FVFGTTPEDILR_697.87_843.5
95
0.63

GDSGGAFAVQDPNDK_739.33_473.2
96
0.63

SLDFTELDVAAEK_719.36_316.2
97
0.63

SLLQPNK_400.24_599.4
98
0.63

TLLIANETLR_572.34_816.5
22
0.63

VILGAHQEVNLEPHVQEIEVSR_832.78_603.3
27
0.63

VQEAHLTEDQIFYFPK_655.66_701.4
99
0.63

FTFTLHLETPKPSISSSNLNPR_829.44_787.4
82
0.63

AYSDLSR_406.2_375.2
76
0.62

DDLYVSDAFHK_655.31_344.1
100
0.62

DDLYVSDAFHK_655.31_704.3
100
0.62

DPDQTDGLGLSYLSSHIANVER_796.39_456.2
101
0.62

ESDTSYVSLK_564.77_347.2
102
0.62

ESDTSYVSLK_564.77_696.4
102
0.62

FVFGTTPEDILR_697.87_742.4
95
0.62

ILDDLSPR_464.76_587.3
103
0.62

LEQGENVFLQATDK_796.4_675.4
70
0.62

LHEAFSPVSYQHDLALLR_699.37_380.2
41
0.62

LIENGYFHPVK_439.57_343.2
66
0.62

SLPVSDSVLSGFEQR_810.92_836.4
104
0.62

TWDPEGVIFYGDTNPK_919.93_403.2
105
0.62

VGEYSLYIGR_578.8_708.4
106
0.62

VIAVNEVGR_478.78_744.4
30
0.62

VPGTSTSATLTGLTR_731.4_761.5
107
0.62

YEVQGEVFTKPQLWP_910.96_293.1
108
0.62

AFTECCVVASQLR_770.87_673.4
109
0.61

APLTKPLK_289.86_357.3
110
0.61

DSPVLIDFFEDTER_841.9_399.2
93
0.61

ELLESYIDGR_597.8_839.4
90
0.61

FLQEQGHR_338.84_369.2
111
0.61

IQTHSTTYR_369.52_627.3
59
0.61

IS.3_432.6_397.3

0.61

IS.4_706.3_780.3

0.61

IS.4_706.3_927.4

0.61

IS.5_726.3_876.3

0.61

ISLLLIESWLEPVR_834.49_500.3
112
0.61

LQGTLPVEAR_542.31_842.5
113
0.61

NKPGVYTDVAYYLAWIR_677.02_821.5
67
0.61

SLDFTELDVAAEK_719.36_874.5
97
0.61

SYTITGLQPGTDYK_772.39_352.2
114
0.61

TASDFITK_441.73_710.4
115
0.61

VLSALQAVQGLLVAQGR_862.02_941.6
116
0.61

VTGWGNLK_437.74_617.3
117
0.61

YEVQGEVFTKPQLWP_910.96_392.2
108
0.61

AFIQLWAFDAVK_704.89_650.4
118
0.60

APLTKPLK_289.86_260.2
110
0.60

GYVIIKPLVWV_643.9_304.2
119
0.60

IITGLLEFEVYLEYLQNR_738.4_822.4
120
0.60

ILDDLSPR_464.76_702.3
103
0.60

LSSPAVITDK_515.79_830.5
78
0.60

TDAPDLPEENQAR_728.34_843.4
121
0.60

TFTLLDPK_467.77_359.2
122
0.60

TFTLLDPK_467.77_686.4
122
0.60

VLEPTLK_400.25_587.3
123
0.60

YEFLNGR_449.72_606.3
124
0.60

YGLVTYATYPK_638.33_843.4
84
0.60

TABLE 6

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
0.81
0.75
0.48
0.92

2
0.95
0.85
0.61
0.86

3
0.95
0.83
0.56
0.93

4
0.94
0.82
0.52
0.92

5
0.95
0.81
0.51
0.94

6
0.95
0.81
0.49
0.93

7
0.95
0.83
0.46
0.93

8
0.96
0.79
0.49
0.91

9
0.95
0.82
0.46
0.88

10
0.94
0.80
0.50
0.85

11
0.93
0.78
0.49
0.84

12
0.94
0.79
0.47
0.82

13
0.92
0.80
0.48
0.84

14
0.95
0.73
0.47
0.83

15
0.93
0.73
0.49
0.83

TABLE 7

Top 15 transitions selected by each multivariate method, ranked by

importance for that method.

SEQ

SEQ

SEQ

SEQ

ID

ID

ID

ID

rf
NO:
boosting
NO:
lasso
NO:
logit
NO:

1
FSVVYA
1
DPNGL
14
SPELQAE
2
AFIQLWAF
118

K_407.23_579.4

PPEAQ

AK_486.75_788.4

DAVK_704.89_650.4

K_583.3_497.2

2
SPELQA
2
ALNFG
34
VILGAHQ
27
AFIQLWAF
118

EAK_486.75_788.4

GIGVV

EVNLEPH

DAVK_704.89_836.4

VGHEL

VQEIEVS

THAFD

R_832.78_860.4

DQGR_837.09_299.2

3
VNHVTL
3
ALEQD
45
VVGGLV
5
AEAQAQYS
89

SQPK_561.82_673.4

LPVNI

ALR_442.29_784.5

AAVAK_654.33_709.4

K_620.35_570.4

4
SSNNPH
4
DALSS
24
TSESGEL
44
AFTECCVV
109

SPIVEEF

VQESQ

HGLTTEE

ASQLR_770.87_574.3

QVPYNK_729.36_261.2

VAQQ

EFVEGIY

AR_572.96_502.3

K_819.06_310.2

5
SSNNPH
4
AHYDL
42
SSNNPHS
4
ADSQAQLL
75

SPIVEEF

R_387.7_288.2

PIVEEFQ

LSTVVGVFT

QVPYNK_729.36_521.3

VPYNK_729.36_261.2

APGLHLK_822.46_664.4

6
VVGGLV
5
FQLPG
47
VVLSSGS
38
AEAQAQYS
89

ALR_442.29_784.547

QK_409.23_276.1

GPGLDLP

AAVAK_654.33_908.5

LVLGLPL

QLK_791.48_598.4

7
FQLPGQ
47
AFTEC
109
ALEQDLP
45
ADSQAQLL
75

K_409.23_276.1

CVVAS

VNIK_620.35_570.4

LSTVVGVFT

QLR_770.87_673.4

APGLHLK_822.46_983.6

8
TLLIANE
22
ALNHL
52
IQTHSTT
59
AFTECCVV
109

TLR_572.34_703.4

PLEYN

YR_369.52_540.3

ASQLR_770.87_673.4

SALYS

R_620.99_538.3

9
DYWSTV
28
ADSQA
75
SSNNPHS
4
Collection.Window.

K_449.72_620.3

QLLLS

PIVEEFQ

GA.in.

TVVGV

VPYNK_729.36_521.3

Days

FTAPG

LHLK_822.46_664.4

10
VVGGLV
5
AEAQA
89
FSVVYAK_407.23_579.4
1
AHYDLR_387.7_288.2
42

ALR_442.29_685.4

QYSAA

VAK_654.33_908.5

11
DPNGLP
14
ADSQA
75
IAQYYYT
25
AHYDLR_387.7_566.3
42

PEAQK_583.3_497.2

QLLLS

FK_598.8_884.4

TVVGV

FTAPG

LHLK_822.46_983.6

12
LLEVPE
31
AITPPH
125
IAQYYYT
25
AITPPHPAS
125

GR_456.76_356.2

PASQA

FK_598.8_395.2

QANIIFDITE

NIIFDI

GNLR_825.77_459.3

TEGNL

R_825.77_459.3

13
GWVTD
19
Collection.

GDTYPAE
43
AITPPHPAS
125

GFSSLK_598.8_953.5

Window.

LYITGSIL

QANIIFDITE

GA.in.

R_884.96_922.5

GNLR_825.77_917.5

Days

14
VILGAH
27
AEAQA
89
SPEQQET
36
ALEQDLPV
45

QEVNLE

QYSAA

VLDGNLI

NIK_620.35_570.4

PHVQEIE

VAK_654.33_709.4

IR_906.48_699.3

VSR_832.78_860.4

15
FQLPGQ
47
AFIQL
118
IAPQLSTE
56
ALEQDLPV
45

K_409.23_429.2

WAFD

ELVSLGE

NIK_620.35_798.5

AVK_704.89_650.4

K_857.47_533.3

In yet another aspect, the invention provides kits for determining probability of preeclampsia, wherein the kits can be used to detect N of the isolated biomarkers listed in Tables 2, 3, 4, 5 and 7 through 22. For example, the kits can be used to detect one or more, two or more, three or more, four or more, or five of the isolated biomarkers selected from the group consisting of SPELQAEAK (SEQ ID NO: 2), SSNNPHSPIVEEFQVPYN (SEQ ID NO: 12), VNHVTLSQPK (SEQ ID NO: 3), VVGGLVALR (SEQ ID NO: 5), and FSVVYAK (SEQ ID NO: 1), LDFHFSSDR (SEQ ID NO: 6), TVQAVLTVPK (SEQ ID NO: 7), GPGEDFR (SEQ ID NO: 8), ETLLQDFR (SEQ ID NO: 9), ATVVYQGER (SEQ ID NO: 10), and GFQALGDAADIR (SEQ ID NO: 11). 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-1-microglobulin (AMBP), ADP/ATP translocase 3 (ANT3), apolipoprotein A-II (APOA2), apolipoprotein B (APOB), apolipoprotein C-III (APOC3), beta-2-microglobulin (B2MG), complement component 1, s subcomponent (C1S), and retinol binding protein 4 (RBP4 or RET4), Inhibin beta C chain (INHBC), Pigment epithelium-derived factor (PEDF), Prostaglandin-H2 D-isomerase (PTGDS), alpha-1-microglobulin (AMBP), Beta-2-glycoprotein 1 (APOH), Metalloproteinase inhibitor 1 (TIMP1), Coagulation factor XIII B chain (F13B), Alpha-2-HS-glycoprotein (FETUA), Sex hormone-binding globulin (SHBG).

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 2, 3, 4, 5 and 7 through 22. 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-1-microglobulin (AMBP), an antibody that specifically binds to ADP/ATP translocase 3 (ANT3), an antibody that specifically binds to apolipoprotein A-II (APOA2), an antibody that specifically binds to apolipoprotein C-III (APOC3), an antibody that specifically binds to apolipoprotein B (APOB), an antibody that specifically binds to beta-2-microglobulin (B2MG), an antibody that specifically binds to retinol binding protein 4 (RBP4 or RET4), an antibody that specifically binds to Inhibin beta C chain (INHBC), an antibody that specifically binds to Pigment epithelium-derived factor (PEDF), an antibody that specifically binds to Prostaglandin-H2 D-isomerase (PTGDS), an antibody that specifically binds to alpha-1-microglobulin (AMBP), an antibody that specifically binds to Beta-2-glycoprotein 1 (APOH), an antibody that specifically binds to Metalloproteinase inhibitor 1 (TIMP1), an antibody that specifically binds to Coagulation factor XIII B chain (F13B), an antibody that specifically binds to Alpha-2-HS-glycoprotein (FETUA), and an antibody that specifically binds to Sex hormone-binding globulin (SHBG).

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 preeclampsia.

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 Preeclampsia

A standard protocol was developed governing conduct of the Proteomic Assessment of Preterm Risk (PAPR) clinical study. This protocol also provided the option that the samples and clinical information could be used to study other pregnancy complications. 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, preeclampsia cases were individually reviewed. Only preterm preeclampsia cases were used for this analysis. For discovery of biomarkers of preeclampsia, 20 samples collected between 17-28 weeks of gestation were analyzed. Samples included 9 cases, 9 term controls matched within one week of sample collection and 2 random term controls. The samples were processed in batches of 24 that included 20 clinical samples and 4 identical human gold standards (HGS). HGS samples are identical aliquots from a pool of human blood and were used for quality control. HGS samples were placed in position 1, 8, 15 and 24 of a batch with patient samples processed in the remaining 20 positions. Matched cases and controls were always processed adjacently.

The samples were subsequently depleted of high abundance proteins using the Human 14 Multiple Affinity Removal System (MARS14), which removes 14 of the most abundant proteins that are essentially uninformative with regard to the identification for disease-relevant changes in the serum proteome. To this end, equal volumes of each clinical or 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 of Transitions to Identify PE Biomarkers

The objective of these analyses was to examine the data collected in Example 1 to identify transitions and proteins that predict preeclampsia. The specific analyses employed were (i) Cox time-to-event analyses and (ii) models with preeclampsia as a binary categorical dependent variable. The dependent variable for all the Cox analyses was Gestational Age of time to event (where event is preeclampsia). For the purpose of the Cox analyses, preeclampsia subjects have the event on the day of birth. Non-preeclampsia 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 obtained in Example 1 were previously adjusted for run order and log transformed. The data was not further adjusted. There were 9 matched non-preeclampsia subjects, and two unmatched non-preeclampsia subjects, where matching was done according to center, gestational age and ethnicity.

Univariate Cox Proportional Hazards Analyses

Univariate Cox Proportional Hazards analyses was performed to predict Gestational Age of time to event (preeclampsia), including Gestational age on the day of specimen collection as a covariate. Table 1 shows the 40 transitions with p-values less than 0.05. Table 2 shows the same transitions sorted by protein ID. There are 8 proteins that have multiple transitions with p-values less than 0.05: AMBP, ANT3, APOA2, APOB, APOC3, B2MG, C1S, and RET4.

Multivariate Cox Proportional Hazards Analyses: Stepwise AIC Selection

Cox Proportional Hazards analyses was performed to predict Gestational Age of time to event (preeclampsia), including Gestational age on the day of specimen collection as a covariate, using stepwise and lasso models for variable selection. The stepwise variable selection analysis used the Akaike Information Criterion (AIC) as the stopping criterion. Table 3 shows the transitions selected by the stepwise AIC analysis. The coefficient of determination (R²) for the stepwise AIC model is 0.87 of a maximum possible 0.9.

Multivariate Cox Proportional Hazards Analyses: Lasso Selection

Lasso variable selection was utilized as the second method of multivariate Cox Proportional Hazards analyses to predict Gestational Age of time to event (preeclampsia), including Gestational age on the day of specimen collection as a covariate. Lasso regression models estimate regression coefficients using penalized optimization methods, where the penalty discourages the model from considering large regression coefficients since we usually believe such large values are not very likely. As a result, some regression coefficients are forced to be zero (i.e., excluded from the model). Here, the resulting model included analytes with non-zero regression coefficients only. The number of these analytes (with non-zero regression coefficients) depends on the severity of the penalty. Cross-validation was used to choose an optimum penalty level. Table 4 shows the results. The coefficient of determination (R²) for the lasso model is 0.53 of a maximum possible 0.9.

Univariate ROC Analysis of Preeclampsia as a Binary Categorical Dependent Variable

Univariate analyses was used to discriminate preeclampsia subjects from non-preeclampsia subjects (preeclampsia as a binary categorical variable) as estimated by area under the receiver operating characteristic (ROC) curve. Table 5 shows the area under the ROC curve for the 196 transitions with the highest ROC area of 0.6 or greater.

Multivariate Analysis of Preeclampsia as a Binary Categorical Dependent Variable

Multivariate analyses was performed to predict preeclampsia 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 nodes at each step: To determine which node to be removed, 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 6 and in FIG. 1, as estimated by 100 rounds of bootstrap resampling. Table 7 shows the top 15 transitions selected by each multivariate method, ranked by importance for that method. These multivariate analyses suggest that models that combine 2 or more transitions give AUC greater than 0.9, as estimated by bootstrap.

In multivariate models, random forest (rf) and lasso models gave the best area under the ROC curve as estimated by bootstrap. The following transitions were selected by these two models for having high univariate ROC's:.

FSVVYAK_407.23_579.4
(SEQ ID NO: 1)

SPELQAEAK_486.75_788.4
(SEQ ID NO: 2)

VNHVTLSQPK_561.82_673.4
(SEQ ID NO: 3)

SSNNPHSPIVEEFQVPYNK_729.36_261.2
(SEQ ID NO: 4)

SSNNPHSPIVEEFQVPYNK_729.36_521.3
(SEQ ID NO: 4)

VVGGLVALR_442.29_784.5
(SEQ ID NO: 5)

In summary, univariate and multivariate Cox analyses were performed using transitions collected in Example 1 to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate. In the univariate Cox analyses, 8 proteins were identified with multiple transitions with p-value less than 0.05. In multivariate Cox analyses, stepwise AIC variable analysis selected 4 transitions, while the lasso model selected 2 transitions. Univariate (ROC) and multivariate (random forest, boosting, lasso, and logistic regression) analyses were performed to predict preeclampsia as a binary categorical variable. Univariate analyses identify 78 analytes with AUROC of 0.7 or greater and 196 analytes with AUROC of 0.6 or greater. Multivariate analyses suggest that models that combine 2 or more transitions give AUC greater than 0.9, as estimated by bootstrap.

Example 3
Study II Shotgun Identification of Preeclampsia 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.

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.

Tryptic digests of MARS depleted patient (preeclampsia cases and normal pregnancycontrols) samples were fractionated by two-dimensional liquid chromatography and analyzed by tandem mass spectrometry. Aliquots of the samples, equivalent to 3-4 μA 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 2 cases and 2 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 identified by both approaches are found in Table 8 and those found uniquely by Sequest or Xtandem are found in Tables 9 and 10, respectively.

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. Candidates were prioritized by AUC and biological function, with preference given for new pathways. Sequences for each protein of interest, were imported into Skyline software which generated a list of tryptic peptides, m/z values for the parent ions and fragment ions, and an instrument-specific collision energy (McLean et al. Bioinformatics (2010) 26 (7): 966-968. McLean et al. Anal. Chem. (2010) 82 (24): 10116-10124).

The list was refined by eliminating peptides containing cysteines and methionines, where possible, 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. For development, peptides from the digested serum were separated with a 15 min acetonitrile.e 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.

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 4 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. In some cases, transition selection and CEs were re-optimized using purified, synthetic peptides.

TABLE 8

Preeclampsia: Peptides significant with AUC >0.6 by X!Tandem and

Sequest

SEQ

Protein

ID

description
Uniprot ID (name)
Peptide
NO:
XT_AUC
S_AUC

afamin
P43652
R.IVQIYKDLL
126
0.67
0.63

(AFAM_HUMAN)
R.N

afamin
P43652
K.VMNHICSK.Q
127
0.73
0.74

(AFAM_HUMAN)

afamin
P43652
R.RHPDLSIPEL
128
0.86
0.83

(AFAM_HUMAN)
LR.I

afamin
P43652
K.HFQNLGK.D
129
0.71
0.75

(AFAM_HUMAN)

alpha-1-
P01011
K.ITLLSALVET
130
0.68
0.70

antichymotrypsin
(AACT_HUMAN)
R.T

alpha-1-
P01011
R.LYGSEAFAT
131
0.70
0.78

antichymotrypsin
(AACT_HUMAN)
DFQDSAAAK.K

alpha-1-
P01011
R.NLAVSQVV
132
0.81
0.79

antichymotrypsin
(AACT_HUMAN)
HK.A

alpha-1B-
P04217
R.CEGPIPDVTF
133
0.78
0.60

glycoprotein
(A1BG_HUMAN)
ELLR.E

alpha-1B-
P04217
R.LHDNQNGW
134
0.72
0.66

glycoprotein
(A1BG_HUMAN)
SGDSAPVELIL

SDETLPAPEFS

PEPESGR.A

alpha-1B-
P04217
R.CEGPIPDVTF
133
0.64
0.60

glycoprotein
(A1BG_HUMAN)
ELLR.E

alpha-1B-
P04217
R.TPGAAANLE
135
0.71
0.67

glycoprotein
(A1BG_HUMAN)
LIFVGPQHAG

NYR.C

alpha-1B-
P04217
K.LLELTGPK.S
136
0.70
0.66

glycoprotein
(A1BG_HUMAN)

alpha-1B-
P04217
R.ATWSGAVL
137
0.84
0.74

glycoprotein
(A1BG_HUMAN)
AGR.D

alpha-2-
P08697
K.HQM*DLVA
138
0.67
0.67

antiplasmin
(A2AP_HUMAN)
TLSQLGLQELF

QAPDLR.G

alpha-2-
P08697
K.LGNQEPGG
139
0.83
0.83

antiplasmin
(A2AP_HUMAN)
QTALK.S

alpha-2-
P08697
K.GFPIKEDFLE
140
0.68
0.65

antiplasmin
(A2AP_HUMAN)
QSEQLFGAKP

VSLTGK.Q

alpha-2-HS-
P02765
R.QPNCDDPET
141
0.61
0.61

glycoprotein
(FETUA_HUMAN)
EEAALVAIDYI

preproprotein

NQNLPWGYK.H

alpha-2-HS-
P02765
K.VWPQQPSG
142
0.79
0.67

glycoprotein
(FETUA_HUMAN)
ELFEIEIDTLET

preproprotein

TCHVLDPTPV

AR.C

alpha-2-HS-
P02765
K.EHAVEGDC
143
0.90
0.77

glycoprotein
(FETUA_HUMAN)
DFQLLK.L

preproprotein

alpha-2-HS-
P02765
R.QPNCDDPET
141
0.63
0.61

glycoprotein
(FETUA_HUMAN)
EEAALVAIDYI

preproprotein

NQNLPWGYK.H

alpha-2-HS-
P02765
K.HTLNQIDEV
144
0.70
0.68

glycoprotein
(FETUA_HUMAN)
K.V

preproprotein

alpha-2-HS-
P02765
R.TVVQPSVGA
145
0.83
0.83

glycoprotein
(FETUA_HUMAN)
AAGPVVPPCP

preproprotein

GR.I

angiotensinogen
P01019
K.TGCSLMGA
146
0.75
0.67

preproprotein
(ANGT_HUMAN)
SVDSTLAFNT

YVHFQGK.M

angiotensinogen
P01019
R.AAM*VGML
147
0.65
0.63

preproprotein
(ANGT_HUMAN)
ANFLGFR.I

angiotensinogen
P01019
R.AAMVGMLA
147
0.65
0.64

preproprotein
(ANGT_HUMAN)
NFLGFR.I

angiotensinogen
P01019
R.AAM*VGM*
147
0.65
0.65

preproprotein
(ANGT_HUMAN)
LANFLGFR.I

angiotensinogen
P01019
R.AAMVGM*L
147
0.65
0.74

preproprotein
(ANGT_HUMAN)
ANFLGFR.I

angiotensinogen
P01019
K.QPFVQGLAL
148
0.60
0.74

preproprotein
(ANGT_HUMAN)
YTPVVLPR.S

angiotensinogen
P01019
R.AAM*VGML
147
0.64
0.63

preproprotein
(ANGT_HUMAN)
ANFLGFR.I

angiotensinogen
P01019
R.AAMVGMLA
147
0.64
0.64

preproprotein
(ANGT_HUMAN)
NFLGFR.I

angiotensinogen
P01019
R.AAM*VGM*
147
0.64
0.65

preproprotein
(ANGT_HUMAN)
LANFLGFR.I

angiotensinogen
P01019
R.AAMVGM*L
147
0.64
0.74

preproprotein
(ANGT_HUMAN)
ANFLGFR.I

angiotensinogen
P01019
K.VLSALQAV
149
0.74
0.77

preproprotein
(ANGT_HUMAN)
QGLLVAQGR.A

angiotensinogen
P01019
K.QPFVQGLAL
148
0.75
0.74

preproprotein
(ANGT_HUMAN)
YTPVVLPR.S

angiotensinogen
P01019
R.ADSQAQLLL
150
0.78
0.77

preproprotein
(ANGT_HUMAN)
STVVGVFTAP

GLHLK.Q

antithrombin-III
P01008
R.ITDVIPSEAI
151
0.78
0.78

(ANT3_HUMAN)
NELTVLVLVN

TIYFK.G

antithrombin-III
P01008
K.NDNDNIFLS
152
0.87
0.83

(ANT3_HUMAN)
PLSISTAFAMT

K.L

antithrombin-III
P01008
R.EVPLNTIIFM
153
0.69
0.62

(ANT3_HUMAN)
GR.V

antithrombin-III
P01008
R.EVPLNTIIFM
153
0.69
0.69

(ANT3_HUMAN)
*GR.V

antithrombin-III
P01008
R.VAEGTQVLE
154
0.83
0.92

(ANT3_HUMAN)
LPFKGDDITM*

VLILPKPEK.S

antithrombin-III
P01008
R.VAEGTQVLE
154
0.83
0.96

(ANT3_HUMAN)
LPFKGDDITM

VLILPKPEK.S

antithrombin-III
P01008
K.EQLQDMGL
155
0.85
0.86

(ANT3_HUMAN)
VDLFSPEK.S

antithrombin-III
P01008
R.VAEGTQVLE
154
0.94
0.92

(ANT3_HUMAN)
LPFKGDDITM*

VLILPKPEK.S

antithrombin-III
P01008
R.VAEGTQVLE
154
0.94
0.96

(ANT3_HUMAN)
LPFKGDDITM

VLILPKPEK.S

antithrombin-III
P01008
R.EVPLNTIIFM
153
0.63
0.62

(ANT3_HUMAN)
GR.V

antithrombin-III
P01008
R.EVPLNTIIFM
153
0.63
0.69

(ANT3_HUMAN)
*GR.V

antithrombin-III
P01008
R.DIPMNPMCI
156
0.71
0.70

(ANT3_HUMAN)
YR.S

apolipoprotein
P02652
K.EPCVESLVS
157
0.83
0.83

A-II
(APOA2_HUMAN)
QYFQTVTDYG

preproprotein

K.D

apolipoprotein
P06727
K.SLAELGGHL
158
0.67
0.67

A-IV
(APOA4_HUMAN)
DQQVEEFR.R

apolipoprotein
P06727
R.LAPLAEDVR
159
0.67
0.90

A-IV
(APOA4_HUMAN)
.G

apolipoprotein
P06727
R.VLRENADSL
160
0.79
0.63

A-IV
(APOA4_HUMAN)
QASLRPHADE

LK.A

apolipoprotein
P06727
R.SLAPYAQDT
161
0.90
0.65

A-IV
(APOA4_HUMAN)
QEKLNHQLEG

LTFQMK.K

apolipoprotein
P06727
R.SLAPYAQDT
161
0.90
0.69

A-IV
(APOA4_HUMAN)
QEKLNHQLEG

LTFQM*K.K

apolipoprotein
P06727
K.LGPHAGDV
162
0.63
0.73

A-IV
(APOA4_HUMAN)
EGHLSFLEK.D

apolipoprotein
P06727
K.SELTQQLNA
163
0.68
0.68

A-IV
(APOA4_HUMAN)
LFQDKLGEVN

TYAGDLQK.K

apolipoprotein
P06727
R.SLAPYAQDT
161
0.71
0.65

A-IV
(APOA4_HUMAN)
QEKLNHQLEG

LTFQMK.K

apolipoprotein
P06727
R.SLAPYAQDT
161
0.71
0.69

A-IV
(APOA4_HUMAN)
QEKLNHQLEG

LTFQM*K.K

apolipoprotein
P06727
R.LLPHANEVS
164
0.62
0.79

A-IV
(APOA4_HUMAN)
QK.I

apolipoprotein
P06727
K.SLAELGGHL
165
0.67
0.69

A-IV
(APOA4_HUMAN)
DQQVEEFRR.R

apolipoprotein
P06727
K.SELTQQLNA
166
0.68
0.62

A-IV
(APOA4_HUMAN)
LFQDK.L

apolipoprotein
P04114
K.GFEPTLEAL
167
0.73
0.76

B-100
(APOB_HUMAN)
FGK.Q

apolipoprotein
P04114
K.ALYWVNGQ
168
0.78
0.67

B-100
(APOB_HUMAN)
VPDGVSK.V

apolipoprotein
P04114
K.FIIPSPK.R
169
0.90
0.90

B-100
(APOB_HUMAN)

apolipoprotein
P04114
R.TPALHFK.S
170
0.68
0.81

B-100
(APOB_HUMAN)

apolipoprotein
P04114
K.TEVIPPLIEN
171
0.62
0.64

B-100
(APOB_HUMAN)
R.Q

apolipoprotein
P04114
R.NLQNNAEW
172
0.65
0.60

B-100
(APOB_HUMAN)
VYQGAIR.Q

apolipoprotein
P04114
K.LPQQANDY
173
0.65
0.62

B-100
(APOB_HUMAN)
LNSFNWER.Q

apolipoprotein
P04114
R.LAAYLMLM
174
0.60
0.73

B-100
(APOB_HUMAN)
R.S

apolipoprotein
P04114
R.VIGNMGQT
175
0.68
0.67

B-100
(APOB_HUMAN)
MEQLTPELK.S

apolipoprotein
P04114
K.LIVAMSSWL
176
0.74
0.86

B-100
(APOB_HUMAN)
QK.A

apolipoprotein
P04114
R.TSSFALNLP
177
0.79
0.70

B-100
(APOB_HUMAN)
TLPEVK.F

apolipoprotein
P04114
K.IADFELPTII
178
0.62
0.61

B-100
(APOB_HUMAN)
VPEQTIEIPSIK.F

apolipoprotein
P04114
K.IEGNLIFDPN
179
0.63
0.62

B-100
(APOB_HUMAN)
NYLPK.E

apolipoprotein
P04114
R.TSSFALNLP
180
0.66
0.72

B-100
(APOB_HUMAN)
TLPEVKFPEV

DVLTK.Y

apolipoprotein
P04114
R.LELELRPTG
181
0.78
0.78

B-100
(APOB_HUMAN)
EIEQYSVSATY

ELQR.E

apolipoprotein
P02655
K.STAAMSTYT
182
0.73
0.73

C-II
(APOC2_HUMAN)
GIFTDQVLSVL

K.G

apolipoprotein
P02656
R.GWVTDGFS
183
1.00
1.00

C-III
(APOC3_HUMAN)
SLKDYWSTVK

DK.F

apolipoprotein E
P02649
R.WELALGR.F
184
0.60
0.63

(APOE_HUMAN)

apolipoprotein E
P02649
R.LAVYQAGA
185
0.61
0.64

(APOE_HUMAN)
R.E

apolipoprotein E
P02649
K.SWFEPLVED
186
0.83
0.73

(APOE_HUMAN)
MQR.Q

apolipoprotein E
P02649
R.AATVGSLA
187
0.67
0.67

(APOE_HUMAN)
GQPLQER.A

apolipoprotein(a)
P08519
R.TPEYYPNAG
188
0.72
0.61

(APOA_HUMAN)
LIMNYCR.N

beta-2-
P02749
K.TFYEPGEEIT
189
0.66
0.76

glycoprotein 1
(APOH_HUMAN)
YSCKPGYVSR.G

beta-2-
P02749
K.FICPLTGLW
190
0.72
0.70

glycoprotein 1
(APOH_HUMAN)
PINTLK.C

bone marrow
P13727
R.SLQTFSQAW
191
0.82
0.72

proteoglycan
(PRG2_HUMAN)
FTCR.R

ceruloplasmin
P00450
K.HYYIGIIETT
192
0.78
0.89

(CERU_HUMAN)
WDYASDHGE

KK.L

ceruloplasmin
P00450
R.EYTDASFTN
193
0.63
0.63

(CERU_HUMAN)
RK.E

ceruloplasmin
P00450
K.M*YYSAVD
194
0.66
0.68

(CERU_HUMAN)
PTKDIFTGLIG

PMK.I

ceruloplasmin
P00450
K.M*YYSAVD
194
0.66
0.76

(CERU_HUMAN)
PTKDIFTGLIG

PM*K.I

ceruloplasmin
P00450
R.SGAGTEDSA
195
0.95
0.95

(CERU_HUMAN)
CIPWAYYSTV

DQVKDLYSGL

IGPLIVCR.R

ceruloplasmin
P00450
R.KAEEEHLGI
196
0.85
0.77

(CERU_HUMAN)
LGPQLHADVG

DKVK.I

ceruloplasmin
P00450
K.EVGPTNADP
197
0.62
0.77

(CERU_HUMAN)
VCLAK.M

ceruloplasmin
P00450
R.MYSVNGYT
198
0.63
0.71

(CERU_HUMAN)
FGSLPGLSMC

AEDR.V

ceruloplasmin
P00450
K.DIASGLIGPL
199
0.63
0.66

(CERU_HUMAN)
IICK.K

ceruloplasmin
P00450
R.QKDVDKEF
200
0.64
0.66

(CERU_HUMAN)
YLFPTVFDEN

ESLLLEDNIR.M

ceruloplasmin
P00450
R.GPEEEHLGI
201
0.65
0.61

(CERU_HUMAN)
LGPVIWAEVG

DTIR.V

ceruloplasmin
P00450
K.M*YYSAVD
194
0.67
0.68

(CERU_HUMAN)
PTKDIFTGLIG

PMK.I

ceruloplasmin
P00450
K.M*YYSAVD
194
0.67
0.76

(CERU_HUMAN)
PTKDIFTGLIG

PM*K.I

ceruloplasmin
P00450
K.M*YYSAVD
194
0.67
0.68

(CERU_HUMAN)
PTKDIFTGLIG

PMK.I

ceruloplasmin
P00450
K.M*YYSAVD
194
0.67
0.76

(CERU_HUMAN)
PTKDIFTGLIG

PM*K.I

ceruloplasmin
P00450
K.GAYPLSIEPI
202
0.67
0.63

(CERU_HUMAN)
GVR.F

ceruloplasmin
P00450
R.GVYSSDVFD
203
0.67
0.67

(CERU_HUMAN)
IFPGTYQTLEM

*FPR.T

ceruloplasmin
P00450
K.DIASGLIGPL
204
0.67
0.73

(CERU_HUMAN)
IICKK.D

ceruloplasmin
P00450
R.SGAGTEDSA
205
0.70
0.70

(CERU_HUMAN)
CIPWAYYSTV

DQVK.D

ceruloplasmin
P00450
R.IYHSHIDAP
206
0.77
0.76

(CERU_HUMAN)
K.D

ceruloplasmin
P00450
R.ADDKVYPG
207
0.77
0.80

(CERU_HUMAN)
EQYTYMLLAT

EEQSPGEGDG

NCVTR.I

ceruloplasmin
P00450
K.DLYSGLIGP
208
0.78
0.82

(CERU_HUMAN)
LIVCR.R

ceruloplasmin
P00450
R.TTIEKPVWL
209
0.88
0.85

(CERU_HUMAN)
GFLGPIIK.A

cholinesterase
P06276
K.IFFPGVSEFG
210
0.87
0.76

(CHLE_HUMAN)
K.E

cholinesterase
P06276
R.AILQSGSFN
211
1.00
0.83

(CHLE_HUMAN)
APWAVTSLYE

AR.N

coagulation
P00748
R.LHEAFSPVS
212
0.72
0.76

factor XII
(FA12_HUMAN)
YQHDLALLR.L

coagulation
P05160
R.GDTYPAELY
213
0.67
0.83

factor XIII B
(F13B_HUMAN)
ITGSILR.M

chain

coagulation
P05160
K.VLHGDLIDF
214
0.69
0.60

factor XIII B
(F13B_HUMAN)
VCK.Q

chain

complement C1r
P00736
K.LVFQQFDLE
215
0.69
0.66

subcomponent
(C1R_HUMAN)
PSEGCFYDYV

K.I

complement C1s
P09871
R.VKNYVDWI
216
0.69
0.60

subcomponent
(C1S_HUMAN)
MK.T

complement C1s
P09871
K.SNALDIIFQT
217
0.75
0.70

subcomponent
(C1S_HUMAN)
DLTGQK.K

complement C2
P06681
R.DFHINLFR.M
218
0.75
0.72

(CO2_HUMAN)

complement C2
P06681
R.GALISDQWV
219
0.60
0.75

(CO2_HUMAN)
LTAAHCFR.D

complement C2
P06681
K.KNQGILEFY
220
0.62
0.67

(CO2_HUMAN)
GDDIALLK.L

complement C3
P01024
R.IHWESASLL
221
0.80
0.77

(CO3_HUMAN)
R.S

complement C4-
P0C0L5
R.VHYTVCIW
222
0.67
0.65

B-like
(CO4B_HUMAN)
R.N

preproprotein

complement C4-
P0C0L5
K.AEMADQAA
223
0.78
0.89

B-like
(CO4B_HUMAN)
AWLTR.Q

preproprotein

complement C4-
P0C0L5
K.M*RPSTDTI
224
0.65
0.65

B-like
(CO4B_HUMAN)
TVMVENSHGL

preproprotein

R.V

complement C4-
P0C0L5
K.MRPSTDTIT
224
0.65
0.72

B-like
(CO4B_HUMAN)
VMVENSHGLR

preproprotein

.V

complement C4-
P0C0L5
R.VQQPDCREP
225
0.67
0.60

B-like
(CO4B_HUMAN)
FLSCCQFAESL

preproprotein

RK.K

complement C4-
P0C0L5
K.LVNGQSHIS
226
0.73
0.73

B-like
(CO4B_HUMAN)
LSK.A

preproprotein

complement C4-
P0C0L5
R.GQIVFMNRE
227
0.80
0.62

B-like
(CO4B_HUMAN)
PK.R

preproprotein

complement C4-
P0C0L5
K.VGLSGM*AI
228
0.80
0.80

B-like
(CO4B_HUMAN)
ADVTLLSGFH

preproprotein

ALR.A

complement C4-
P0C0L5
K.VGLSGMAIA
228
0.80
0.83

B-like
(CO4B_HUMAN)
DVTLLSGFHA

preproprotein

LR.A

complement C4-
P0C0L5
R.GHLFLQTDQ
229
0.70
0.68

B-like
(CO4B_HUMAN)
PIYNPGQR.V

preproprotein

complement C4-
P0C0L5
K.M*RPSTDTI
224
0.75
0.65

B-like
(CO4B_HUMAN)
TVMVENSHGL

preproprotein

R.V

complement C4-
P0C0L5
K.MRPSTDTIT
224
0.75
0.72

B-like
(CO4B_HUMAN)
VMVENSHGLR

preproprotein

.V

complement C4-
P0C0L5
K.SHALQLNN
230
0.76
0.70

B-like
(CO4B_HUMAN)
R.Q

preproprotein

complement C4-
P0C0L5
R.YVSHFETEG
231
0.88
0.89

B-like
(CO4B_HUMAN)
PHVLLYFDSV

preproprotein

PTSR.E

complement C4-
P0C0L5
R.GSSTWLTAF
232
0.61
0.72

B-like
(CO4B_HUMAN)
VLK.V

preproprotein

complement C4-
P0C0L5
R.YIYGKPVQG
233
0.63
0.73

B-like
(CO4B_HUMAN)
VAYVR.F

preproprotein

complement C4-
P0C0L5
K.SCGLHQLLR
234
0.65
0.65

B-like
(CO4B_HUMAN)
.G

preproprotein

complement C4-
P0C0L5
R.GPEVQLVA
235
0.69
0.73

B-like
(CO4B_HUMAN)
HSPWLK.D

preproprotein

complement C4-
P0C0L5
R.KKEVYM*PS
236
0.70
0.67

B-like
(CO4B_HUMAN)
SIFQDDFVIPDI

preproprotein

SEPGTWK.I

complement C4-
P0C0L5
R.KKEVYMPSS
236
0.70
0.69

B-like
(CO4B_HUMAN)
IFQDDFVIPDIS

preproprotein

EPGTWK.I

complement C4-
P0C0L5
R.VQQPDCREP
237
0.76
0.74

B-like
(CO4B_HUMAN)
FLSCCQFAESL

preproprotein

R.K

complement C4-
P0C0L5
K.VGLSGM*AI
228
0.80
0.80

B-like
(CO4B_HUMAN)
ADVTLLSGFH

preproprotein

ALR.A

complement C4-
P0C0L5
K.VGLSGMAIA
228
0.80
0.83

B-like
(CO4B_HUMAN)
DVTLLSGFHA

preproprotein

LR.A

complement C4-
P0C0L5
K.ASAGLLGA
238
0.85
0.83

B-like
(CO4B_HUMAN)
HAAAITAYAL

preproprotein

TLTK.A

complement C5
P01031
K.ITHYNYLILS
239
0.73
0.73

preproprotein
(CO5_HUMAN)
K.G

complement C5
P01031
R.KAFDICPLV
240
0.83
0.87

preproprotein
(CO5_HUMAN)
K.I

complement C5
P01031
R.IPLDLVPK.T
241
0.90
0.63

preproprotein
(CO5_HUMAN)

complement C5
P01031
R.MVETTAYA
242
0.92
0.75

preproprotein
(CO5_HUMAN)
LLTSLNLKDIN

YVNPVIK.W

complement C5
P01031
K.ALLVGEHL
243
1.00
0.87

preproprotein
(CO5_HUMAN)
NIIVTPK.S

complement C5
P01031
K.LKEGMLSIM
244
0.62
0.75

preproprotein
(CO5_HUMAN)
SYR.N

complement C5
P01031
R.YIYPLDSLT
245
0.70
0.69

preproprotein
(CO5_HUMAN)
WIEYWPR.D

complement C5
P01031
K.GGSASTWL
246
0.63
0.83

preproprotein
(CO5_HUMAN)
TAFALR.V

complement C5
P01031
R.YGGGFYSTQ
247
0.73
0.74

preproprotein
(CO5_HUMAN)
DTINAIEGLTE

YSLLVK.Q

complement
P13671
K.AKDLHLSD
248
0.63
0.62

component C6
(CO6_HUMAN)
VFLK.A

complement
P13671
K.ALNHLPLEY
249
0.60
0.62

component C6
(CO6_HUMAN)
NSALYSR.I

complement
P10643
R.LSGNVLSYT
250
0.71
0.63

component C7
(CO7_HUMAN)
FQVK.I

complement
P07357
R.KDDIMLDEG
251
0.78
0.89

component C8
(CO8A_HUMAN)
MLQSLMELPD

alpha chain

QYNYGMYAK.F

complement
P07358
R.DFGTHYITE
252
0.80
0.73

component C8
(CO8B_HUMAN)
AVLGGIYEYT

beta chain

LVMNK.E

preproprotein

complement
P07358
R.DTMVEDLV
253
0.88
0.76

component C8
(CO8B_HUMAN)
VLVR.G

beta chain

preproprotein

complement
P07358
R.YYAGGCSP
254
0.70
0.71

component C8
(CO8B_HUMAN)
HYILNTR.F

beta chain

preproprotein

complement
P07360
R.SLPVSDSVL
255
0.79
0.81

component C8
(CO8G_HUMAN)
SGFEQR.V

gamma chain

complement
P07360
R.VQEAHLTED
256
0.98
0.84

component C8
(CO8G_HUMAN)
QIFYFPK.Y

gamma chain

complement
P02748
R.TAGYGINIL
257
0.62
0.64

component C9
(CO9_HUMAN)
GMDPLSTPFD

NEFYNGLCNR.D

complement
P02748
R.RPWNVASLI
258
0.60
0.74

component C9
(CO9_HUMAN)
YETK.G

complement
P02748
R.AIEDYINEFS
259
0.67
0.67

component C9
(CO9_HUMAN)
VRK.C

complement
P02748
R.AIEDYINEFS
260
0.77
0.79

component C9
(CO9_HUMAN)
VR.K

complement
P00751
R.LEDSVTYHC
261
0.60
0.60

factor B
(CFAB_HUMAN)
SR.G

preproprotein

complement
P00751
R.FIQVGVISW
262
0.67
0.79

factor B
(CFAB_HUMAN)
GVVDVCK.N

preproprotein

complement
P00751
R.DFHINLFQV
263
0.78
0.76

factor B
(CFAB_HUMAN)
LPWLK.E

preproprotein

complement
P00751
K.YGQTIRPICL
264
0.60
0.70

factor B
(CFAB_HUMAN)
PCTEGTTR.A

preproprotein

complement
P00751
R.LLQEGQALE
265
0.74
0.74

factor B
(CFAB_HUMAN)
YVCPSGFYPY

preproprotein

PVQTR.T

complement
P08603
R.RPYFPVAVG
266
0.67
0.70

factor H
(CFAH_HUMAN)
K.Y

complement
P08603
K.CTSTGWIPA
267
0.70
0.66

factor H
(CFAH_HUMAN)
PR.C

complement
P08603
K.CLHPCVISR.E
268
0.94
0.64

factor H
(CFAH_HUMAN)

complement
P08603
R.EIMENYNIA
269
0.67
0.71

factor H
(CFAH_HUMAN)
LR.W

complement
P08603
K.CLHPCVISR.E
268
0.75
0.64

factor H
(CFAH_HUMAN)

complement
P08603
K.AVYTCNEG
270
0.73
0.62

factor H
(CFAH_HUMAN)
YQLLGEINYR.E

complement
P08603
R.SITCIHGVW
271
0.61
0.61

factor H
(CFAH_HUMAN)
TQLPQCVAID

K.L

complement
P08603
R.WQSIPLCVE
272
0.65
0.65

factor H
(CFAH_HUMAN)
K.I

complement
P08603
K.TDCLSLPSF
273
0.74
0.77

factor H
(CFAH_HUMAN)
ENAIPMGEK.K

complement
P08603
K.CFEGFGIDG
274
0.76
0.69

factor H
(CFAH_HUMAN)
PAIAK.C

complement
P08603
K.CFEGFGIDG
274
0.83
0.69

factor H
(CFAH_HUMAN)
PAIAK.C

complement
P08603
K.IDVHLVPDR
275
0.61
0.67

factor H
(CFAH_HUMAN)
.K

complement
P08603
K.SSNLIILEEH
276
0.77
0.69

factor H
(CFAH_HUMAN)
LK.N

complement
P05156
R.AQLGDLPW
277
0.66
0.69

factor I
(CFAI_HUMAN)
QVAIK.D

preproprotein

complement
P05156
R.VFSLQWGE
278
0.69
0.77

factor I
(CFAI_HUMAN)
VK.L

preproprotein

corticosteroid-
P08185
R.WSAGLTSSQ
279
0.63
0.61

binding globulin
(CBG_HUMAN)
VDLYIPK.V

fibrinogen alpha
P02671
K.TFPGFFSPM
280
0.80
0.78

chain
(FIBA_HUMAN)
LGEFVSETESR

.G

gelsolin
P06396
R.IEGSNKVPV
281
0.78
0.78

(GELS_HUMAN)
DPATYGQFYG

GDSYIILYNYR

.H

gelsolin
P06396
R.AQPVQVAE
282
0.62
0.65

(GELS_HUMAN)
GSEPDGFWEA

LGGK.A

gelsolin
P06396
K.TPSAAYLW
283
0.78
0.78

(GELS_HUMAN)
VGTGASEAEK

TGAQELLR.V

gelsolin
P06396
R.VEKFDLVPV
284
0.61
0.63

(GELS_HUMAN)
PTNLYGDFFT

GDAYVILK.T

gelsolin
P06396
R.EVQGFESAT
285
0.87
0.88

(GELS_HUMAN)
FLGYFK.S

gelsolin
P06396
K.NWRDPDQT
286
0.89
0.89

(GELS_HUMAN)
DGLGLSYLSS

HIANVER.V

gelsolin
P06396
K.TPSAAYLW
287
0.87
0.77

(GELS_HUMAN)
VGTGASEAEK.T

glutathione
P22352
K.FLVGPDGIPI
288
0.85
0.77

peroxidase 3
(GPX3_HUMAN)
MR.W

hemopexin
P02790
R.LEKEVGTPH
289
0.93
0.74

(HEMO_HUMAN)
GIILDSVDAAF

ICPGSSR.L

hemopexin
P02790
R.WKNFPSPVD
290
0.64
0.82

(HEMO_HUMAN)
AAFR.Q

hemopexin
P02790
R.GECQAEGV
291
0.60
0.64

(HEMO_HUMAN)
LFFQGDREWF

WDLATGTMK.E

hemopexin
P02790
R.GECQAEGV
291
0.60
0.83

(HEMO_HUMAN)
LFFQGDREWF

WDLATGTM*

K.E

hemopexin
P02790
R.GECQAEGV
291
0.93
0.64

(HEMO_HUMAN)
LFFQGDREWF

WDLATGTMK.E

hemopexin
P02790
R.GECQAEGV
291
0.93
0.83

(HEMO_HUMAN)
LFFQGDREWF

WDLATGTM*

K.E

hemopexin
P02790
K.EVGTPHGIIL
292
0.62
0.69

(HEMO_HUMAN)
DSVDAAFICP

GSSR.L

hemopexin
P02790
R.LWWLDLK.S
293
0.64
0.64

(HEMO_HUMAN)

hemopexin
P02790
K.NFPSPVDAA
294
0.65
0.72

(HEMO_HUMAN)
FR.Q

hemopexin
P02790
R.EWFWDLAT
295
0.68
0.65

(HEMO_HUMAN)
GTMK.E

hemopexin
P02790
K.GGYTLVSG
296
0.69
0.65

(HEMO_HUMAN)
YPK.R

hemopexin
P02790
K.LYLVQGTQ
297
0.69
0.76

(HEMO_HUMAN)
VYVFLTK.G

heparin cofactor 2
P05546
R.EYYFAEAQI
298
0.80
0.78

(HEP2_HUMAN)
ADFSDPAFISK.T

heparin cofactor 2
P05546
K.QFPILLDFK.T
299
0.62
1.00

(HEP2_HUMAN)

heparin cofactor 2
P05546
K.QFPILLDFK.T
299
0.64
1.00

(HEP2_HUMAN)

heparin cofactor 2
P05546
K.FAFNLYR.V
300
0.70
0.60

(HEP2_HUMAN)

histidine-rich
P04196
R.DGYLFQLLR
301
0.65
0.65

glycoprotein
(HRG_HUMAN)
.I

insulin-like
P35858
R.SFEGLGQLE
302
0.75
0.83

growth factor-
(ALS_HUMAN)
VLTLDHNQLQ

binding protein

EVK.A

complex acid

labile subunit

insulin-like
P35858
R.TFTPQPPGL
303
0.75
0.60

growth factor-
(ALS_HUMAN)
ER.L

binding protein

complex acid

labile subunit

insulin-like
P35858
R.AFWLDVSH
304
0.77
0.75

growth factor-
(ALS_HUMAN)
NR.L

binding protein

complex acid

labile subunit

insulin-like
P35858
R.LAELPADAL
305
0.66
0.64

growth factor-
(ALS_HUMAN)
GPLQR.A

binding protein

complex acid

labile subunit

insulin-like
P35858
R.LEALPNSLL
306
0.70
0.67

growth factor-
(ALS_HUMAN)
APLGR.L

binding protein

complex acid

labile subunit

insulin-like
P35858
R.NLIAAVAPG
307
0.70
0.68

growth factor-
(ALS_HUMAN)
AFLGLK.A

binding protein

complex acid

labile subunit

inter-alpha-
P19827
R.QAVDTAVD
308
0.60
0.64

trypsin inhibitor
(ITIH1_HUMAN)
GVFIR.S

heavy chain H1

inter-alpha-
P19827
K.TAFISDFAV
309
0.81
0.86

trypsin inhibitor
(ITIH1_HUMAN)
TADGNAFIGDI

heavy chain H1

K.D

inter-alpha-
P19827
R.GHMLENHV
310
0.63
0.61

trypsin inhibitor
(ITIH1_HUMAN)
ER.L

heavy chain H1

inter-alpha-
P19827
R.GHM*LENH
310
0.63
0.70

trypsin inhibitor
(ITIH1_HUMAN)
VER.L

heavy chain H1

inter-alpha-
P19827
K.TAFISDFAV
311
0.75
0.60

trypsin inhibitor
(ITIH1_HUMAN)
TADGNAFIGDI

heavy chain H1

KDKVTAWK.Q

inter-alpha-
P19827
R.GIEILNQVQ
312
0.80
0.80

trypsin inhibitor
(ITIH1_HUMAN)
ESLPELSNHAS

heavy chain H1

ILIMLTDGDPT

EGVTDR.S

inter-alpha-
P19827
K.ILGDM*QPG
313
0.85
0.79

trypsin inhibitor
(ITIH1_HUMAN)
DYFDLVLFGT

heavy chain H1

R.V

inter-alpha-
P19827
K.LDAQASFLP
314
0.88
0.75

trypsin inhibitor
(ITIH1_HUMAN)
K.E

heavy chain H1

inter-alpha-
P19827
R.GFSLDEATN
315
0.80
0.80

trypsin inhibitor
(ITIH1_HUMAN)
LNGGLLR.G

heavy chain H1

inter-alpha-
P19827
K.TAFISDFAV
316
0.93
0.96

trypsin inhibitor
(ITIH1_HUMAN)
TADGNAFIGDI

heavy chain H1

KDK.V

inter-alpha-
P19827
K.GSLVQASEA
317
0.60
0.65

trypsin inhibitor
(ITIH1_HUMAN)
NLQAAQDFVR

heavy chain H1

.G

inter-alpha-
P19827
R.GHMLENHV
310
0.64
0.61

trypsin inhibitor
(ITIH1_HUMAN)
ER.L

heavy chain H1

inter-alpha-
P19827
R.GHM*LENH
310
0.64
0.70

trypsin inhibitor
(ITIH1_HUMAN)
VER.L

heavy chain H1

inter-alpha-
P19827
R.LWAYLTIQE
318
0.72
0.74

trypsin inhibitor
(ITIH1_HUMAN)
LLAK.R

heavy chain H1

inter-alpha-
P19827
R.EVAFDLEIP
319
0.78
0.62

trypsin inhibitor
(ITIH1_HUMAN)
K.T

heavy chain H1

inter-alpha-
P19823
R.SILQMSLDH
320
0.76
0.76

trypsin inhibitor
(ITIH2_HUMAN)
HIVTPLTSLVI

heavy chain H2

ENEAGDER.M

inter-alpha-
P19823
R.SILQM*SLD
320
0.76
0.80

trypsin inhibitor
(ITIH2_HUMAN)
HHIVTPLTSLV

heavy chain H2

IENEAGDER.M

inter-alpha-
P19823
R.SILQMSLDH
320
0.77
0.76

trypsin inhibitor
(ITIH2_HUMAN)
HIVTPLTSLVI

heavy chain H2

ENEAGDER.M

inter-alpha-
P19823
R.SILQM*SLD
320
0.77
0.80

trypsin inhibitor
(ITIH2_HUMAN)
HHIVTPLTSLV

heavy chain H2

IENEAGDER.M

inter-alpha-
P19823
K.AGELEVFNG
321
0.79
0.76

trypsin inhibitor
(ITIH2_HUMAN)
YFVHFFAPDN

heavy chain H2

LDPIPK.N

inter-alpha-
P19823
R.ETAVDGELV
322
0.94
0.97

trypsin inhibitor
(ITIH2_HUMAN)
VLYDVK.R

heavy chain H2

inter-alpha-
P19823
R.NVQFNYPHT
323
0.74
0.83

trypsin inhibitor
(ITIH2_HUMAN)
SVTDVTQNNF

heavy chain H2

HNYFGGSEIV

VAGK.F

inter-alpha-
P19823
R.FLHVPDTFE
324
0.81
0.81

trypsin inhibitor
(ITIH2_HUMAN)
GHFDGVPVIS

heavy chain H2

K.G

inter-alpha-
Q14624
K.YIFHNFM*E
325
0.70
0.73

trypsin inhibitor
(ITIH4_HUMAN)
R.L

heavy chain H4

inter-alpha-
Q14624
R.SFAAGIQAL
326
0.75
0.75

trypsin inhibitor
(ITIH4_HUMAN)
GGTNINDAML

heavy chain H4

MAVQLLDSSN

QEER.L

inter-alpha-
Q14624
R.NMEQFQVS
327
1.00
1.00

trypsin inhibitor
(ITIH4_HUMAN)
VSVAPNAK.I

heavy chain H4

inter-alpha-
Q14624
R.VQGNDHSA
328
0.85
0.86

trypsin inhibitor
(ITIH4_HUMAN)
TR.E

heavy chain H4

inter-alpha-
Q14624
K.WKETLFSV
329
0.66
0.69

trypsin inhibitor
(ITIH4_HUMAN)
MPGLK.M

heavy chain H4

inter-alpha-
Q14624
K.AGFSWIEVT
330
0.78
0.82

trypsin inhibitor
(ITIH4_HUMAN)
FK.N

heavy chain H4

inter-alpha-
Q14624
R.DQFNLIVFS
331
0.61
0.60

trypsin inhibitor
(ITIH4_HUMAN)
TEATQWRPSL

heavy chain H4

VPASAENVNK

.A

inter-alpha-
Q14624
R.LWAYLTIQQ
332
0.66
0.66

trypsin inhibitor
(ITIH4_HUMAN)
LLEQTVSASD

heavy chain H4

ADQQALR.N

kallistatin
P29622
K.FSISGSYVL
333
0.79
0.72

(KAIN_HUMAN)
DQILPR.L

kininogen-1
P01042
K.AATGECTAT
334
0.76
0.60

(KNG1_HUMAN)
VGKR.S

kininogen-1
P01042
K.ENFLFLTPD
335
0.71
0.68

(KNG1_HUMAN)
CK.S

kininogen-1
P01042
R.DIPTNSPELE
336
0.65
0.64

(KNG1_HUMAN)
ETLTHTITK.L

kininogen-1
P01042
K.IYPTVNCQP
337
0.66
0.60

(KNG1_HUMAN)
LGM*ISLMK.R

kininogen-1
P01042
K.IYPTVNCQP
337
0.66
0.62

(KNG1_HUMAN)
LGMISLMK.R

kininogen-1
P01042
K.IYPTVNCQP
337
0.66
0.63

(KNG1_HUMAN)
LGMISLM*K.R

kininogen-1
P01042
R.IGEIKEETTS
338
0.67
0.70

(KNG1_HUMAN)
HLR.S

kininogen-1
P01042
K.YNSQNQSN
339
0.76
0.65

(KNG1_HUMAN)
NQFVLYR.I

kininogen-1
P01042
K.TVGSDTFYS
340
0.78
0.77

(KNG1_HUMAN)
FK.Y

leucine-rich
P02750
R.DGFDISGNP
341
0.73
0.73

alpha-2-
(A2GL_HUMAN)
WICDQNLSDL

glycoprotein

YR.W

leucine-rich
P02750
R.NALTGLPPG
342
0.79
0.79

alpha-2-
(A2GL_HUMAN)
LFQASATLDT

glycoprotein

LVLK.E

leucine-rich
P02750
K.ALGHLDLSG
343
0.71
0.71

alpha-2-
(A2GL_HUMAN)
NR.L

glycoprotein

leucine-rich
P02750
R.VAAGAFQG
344
0.71
0.77

alpha-2-
(A2GL_HUMAN)
LR.Q

glycoprotein

lipopolysacchari
P18428
R.SPVTLLAAV
345
0.65
0.61

de-binding
(LBP_HUMAN)
MSLPEEHNK.M

protein

lumican
P51884
K.SLEYLDLSF
346
0.93
0.96

(LUM_HUMAN)
NQIAR.L

monocyte
P08571
R.LTVGAAQV
347
0.68
0.63

differentiation
(CD14_HUMAN)
PAQLLVGALR.V

antigen CD14

N-
Q96PD5
R.EGKEYGVV
348
0.64
0.64

acetylmuramoyl-
(PGRP2_HUMAN)
LAPDGSTVAV

L-alanine

EPLLAGLEAG

amidase

LQGR.R

N-
Q96PD5
K.EFTEAFLGC
349
0.63
0.62

acetylmuramoyl-
(PGRP2_HUMAN)
PAIHPR.C

L-alanine

amidase

N-
Q96PD5
R.TDCPGDALF
350
0.88
0.86

acetylmuramoyl-
(PGRP2_HUMAN)
DLLR.T

L-alanine

amidase

phosphatidylinos
P80108
K.VAFLTVTLH
351
0.63
0.65

itol-glycan-
(PHLD_HUMAN)
QGGATR.M

specific

phospholipase D

pigment
P36955
R.ALYYDLISS
352
0.69
0.65

epithelium-
(PEDF_HUMAN)
PDIHGTYKELL

derived factor

DTVTAPQK.N

pigment
P36955
K.TVQAVLTVP
353
0.72
0.62

epithelium-
(PEDF_HUMAN)
K.L

derived factor

pigment
P36955
R.LDLQEINNW
354
0.67
0.68

epithelium-
(PEDF_HUMAN)
VQAQMK.G

derived factor

plasma kallikrein
P03952
R.LVGITSWGE
355
1.00
0.67

preproprotein
(KLKB1_HUMAN)
GCAR.R

plasma protease
P05155
K.TNLESILSYP
356
0.83
0.83

C1 inhibitor
(IC1_HUMAN)
KDFTCVHQAL

K.G

plasma protease
P05155
R.LVLLNAIYL
357
0.64
0.61

C1 inhibitor
(IC1_HUMAN)
SAK.W

plasma protease
P05155
K.FQPTLLTLP
358
0.86
0.77

C1 inhibitor
(IC1_HUMAN)
R.I

plasminogen
P00747
R.HSIFTPETNP
359
0.66
0.64

(PLMN_HUMAN)
R.A

plasminogen
P00747
R.FVTWIEGV
360
0.65
0.74

(PLMN_HUMAN)
MR.N

PREDICTED:
P0C0L4
R.GQIVFMNR.E
361
0.75
0.61

complement C4-A
(CO4A_HUMAN)

PREDICTED:
P0C0L4
R.DSSTWLTAF
362
0.65
0.67

complement C4-A
(CO4A_HUMAN)
VLK.V

PREDICTED:
P0C0L4
R.YLDKTEQW
363
0.70
0.60

complement C4-A
(CO4A_HUMAN)
STLPPETK.D

PREDICTED:
P0C0L4
R.DFALLSLQV
364
0.78
0.62

complement C4-A
(CO4A_HUMAN)
PLK.D

PREDICTED:
P0C0L4
R.TLEIPGNSDP
365
0.74
0.78

complement C4-A
(CO4A_HUMAN)
NMIPDGDFNS

YVR.V

PREDICTED:
P0C0L4
R.EMSGSPASG
366
0.88
0.88

complement C4-A
(CO4A_HUMAN)
IPVK.V

PREDICTED:
P0C0L4
K.LHLETDSLA
367
0.68
0.64

complement C4-A
(CO4A_HUMAN)
LVALGALDTA

LYAAGSK.S

PREDICTED:
P0C0L4
R.GCGEQTMIY
368
0.71
0.67

complement C4-A
(CO4A_HUMAN)
LAPTLAASR.Y

pregnancy zone
P20742
R.NELIPLIYLE
369
1.00
0.67

protein
(PZP_HUMAN)
NPR.R

pregnancy zone
P20742
K.LEAGINQLS
370
1.00
0.73

protein
(PZP_HUMAN)
FPLSSEPIQGS

YR.V

pregnancy zone
P20742
R.NQGNTWLT
371
0.73
0.78

protein
(PZP_HUMAN)
AFVLK.T

pregnancy zone
P20742
R.AFQPFFVEL
372
0.83
0.88

protein
(PZP_HUMAN)
TMPYSVIR.G

pregnancy zone
P20742
R.IQHPFTVEEF
373
0.65
0.79

protein
(PZP_HUMAN)
VLPK.F

pregnancy zone
P20742
K.ALLAYAFSL
374
0.69
0.74

protein
(PZP_HUMAN)
LGK.Q

pregnancy-
P11464
R.TLFLLGVTK.Y
375
0.74
0.83

specific beta-1-
(PSG1_HUMAN)/

glycoprotein 1/
Q9UQ74

8/4
(PSG8_HUMAN)/

Q00888

(PSG4_HUMAN)

protein AMBP
P02760
R.TVAACNLPI
376
0.78
0.77

preproprotein
(AMBP_HUMAN)
VR.G

protein AMBP
P02760
K.WYNLAIGST
377
0.80
0.80

preproprotein
(AMBP_HUMAN)
CPWLK.K

protein Z-
Q9UK55
K.LILVDYILFK.G
378
0.69
0.62

dependent
(ZPI_HUMAN)

protease inhibitor

prothrombin
P00734
R.KSPQELLCG
379
0.63
0.65

preproprotein
(THRB_HUMAN)
ASLISDR.W

prothrombin
P00734
R.TATSEYQTF
380
0.79
0.61

preproprotein
(THRB_HUMAN)
FNPR.T

prothrombin
P00734
R.VTGWGNLK
381
1.00
0.71

preproprotein
(THRB_HUMAN)
ETWTANVGK.G

prothrombin
P00734
R.IVEGSDAEIG
382
0.65
0.61

preproprotein
(THRB_HUMAN)
MSPWQVMLF

R.K

prothrombin
P00734
K.HQDFNSAV
383
0.65
0.64

preproprotein
(THRB_HUMAN)
QLVENFCR.N

prothrombin
P00734
R.IVEGSDAEIG
382
0.65
0.80

preproprotein
(THRB_HUMAN)
M*SPWQVMLF

R.K

prothrombin
P00734
R.IVEGSDAEIG
382
0.65
1.00

preproprotein
(THRB_HUMAN)
MSPWQVM*LF

R.K

prothrombin
P00734
R.RQECSIPVC
384
0.74
0.73

preproprotein
(THRB_HUMAN)
GQDQVTVAM

TPR.S

prothrombin
P00734
R.LAVTTHGLP
385
0.76
0.80

preproprotein
(THRB_HUMAN)
CLAWASAQA

K.A

prothrombin
P00734
K.GQPSVLQV
386
0.76
0.67

preproprotein
(THRB_HUMAN)
VNLPIVERPVC

K.D

retinol-binding
P02753
R.LLNLDGTCA
387
0.70
0.66

protein 4
(RET4_HUMAN)
DSYSFVFSR.D

sex hormone-
P04278
R.LFLGALPGE
388
0.72
0.72

binding globulin
(SHBG_HUMAN)
DSSTSFCLNGL

WAQGQR.L

sex hormone-
P04278
R.TWDPEGVIF
389
0.75
0.76

binding globulin
(SHBG_HUMAN)
YGDTNPKDD

WFMLGLR.D

sex hormone-
P04278
R.IALGGLLFP
390
0.62
0.72

binding globulin
(SHBG_HUMAN)
ASNLR.L

sex hormone-
P04278
K.VVLSSGSGP
391
0.65
0.68

binding globulin
(SHBG_HUMAN)
GLDLPLVLGL

PLQLK.L

thyroxine-
P05543
K.AVLHIGEK.G
392
0.64
0.75

binding globulin
(THBG_HUMAN)

thyroxine-
P05543
K.GWVDLFVP
393
0.60
0.61

binding globulin
(THBG_HUMAN)
K.F

thyroxine-
P05543
K.FSISATYDL
394
0.62
0.64

binding globulin
(THBG_HUMAN)
GATLLK.M

thyroxine-
P05543
R.SILFLGK.V
395
0.66
0.63

binding globulin
(THBG_HUMAN)

transforming
Q15582
R.LTLLAPLNS
396
0.78
0.65

growth factor-
(BGH3_HUMAN)
VFK.D

beta-induced

protein ig-h3

vitamin D-
P02774
K.EYANQFMW
397
0.67
0.64

binding protein
(VTDB_HUMAN)
EYSTNYGQAP

LSLLVSYTK.S

vitamin D-
P02774
K.EYANQFM*
397
0.67
0.67

binding protein
(VTDB_HUMAN)
WEYSTNYGQ

APLSLLVSYT

K.S

vitamin D-
P02774
K.ELPEHTVK.L
398
0.79
0.74

binding protein
(VTDB_HUMAN)

vitamin D-
P02774
R.RTHLPEVFL
399
0.63
0.76

binding protein
(VTDB_HUMAN)
SK.V

vitamin D-
P02774
K.TAMDVFVC
400
0.66
0.63

binding protein
(VTDB_HUMAN)
TYFMPAAQLP

ELPDVELPTN

K.D

vitamin D-
P02774
K.LPDATPTEL
401
0.67
0.73

binding protein
(VTDB_HUMAN)
AK.L

vitamin D-
P02774
K.EYANQFMW
397
0.65
0.64

binding protein
(VTDB_HUMAN)
EYSTNYGQAP

LSLLVSYTK.S

vitamin D-
P02774
K.EYANQFM*
397
0.65
0.67

binding protein
(VTDB_HUMAN)
WEYSTNYGQ

APLSLLVSYT

K.S

vitamin D-
P02774
K.ELSSFIDKG
402
0.71
0.73

binding protein
(VTDB_HUMAN)
QELCADYSEN

TFTEYKK.K

vitamin D-
P02774
K.EDFTSLSLV
403
0.71
0.75

binding protein
(VTDB_HUMAN)
LYSR.K

vitamin D-
P02774
K.HQPQEFPTY
404
0.77
0.75

binding protein
(VTDB_HUMAN)
VEPTNDEICEA

FRK.D

vitamin D-
P02774
K.HQPQEFPTY
405
0.60
0.67

binding protein
(VTDB_HUMAN)
VEPTNDEICEA

FR.K

vitamin D-
P02774
R.KFPSGTFEQ
406
0.62
0.61

binding protein
(VTDB_HUMAN)
VSQLVK.E

vitamin D-
P02774
K.ELSSFIDKG
407
0.64
0.64

binding protein
(VTDB_HUMAN)
QELCADYSEN

TFTEYK.K

vitamin D-
P02774
K.EFSHLGKED
408
0.66
0.64

binding protein
(VTDB_HUMAN)
FTSLSLVLYSR

.K

vitamin D-
P02774
K.SYLSMVGSC
409
0.68
0.77

binding protein
(VTDB_HUMAN)
CTSASPTVCFL

K.E

vitronectin
P04004
R.IYISGMAPRP
410
0.63
0.66

(VTNC_HUMAN)
SLAK.K

vitronectin
P04004
R.IYISGMAPRP
410
0.64
0.66

(VTNC_HUMAN)
SLAK.K

vitronectin
P04004
K.LIRDVWGIE
411
0.81
0.75

(VTNC_HUMAN)
GPIDAAFTR.I

von Willebrand
P04275
R.IGWPNAPILI
412
0.67
0.67

factor
(VWF_HUMAN)
QDFETLPR.E

preproprotein

*= Oxidation of Methionine

TABLE 9

Preeclampsia: Additional peptides significant with AUC >0.6

by Sequest only

SEQ

Protein

ID

description
Uniprot ID (name)
Peptide
NO:
S_AUC

afamin
P43652
R.LCFFYNKK.S
413
0.67

(AFAM_HUMAN)

afamin
P43652
R.RPCFESLK.A
414
0.81

(AFAM_HUMAN)

afamin
P43652
R.IVQIYK.D
415
0.61

(AFAM_HUMAN)

afamin
P43652
R.FLVNLVK.L
416
0.60

(AFAM_HUMAN)

afamin
P43652
K.LPNNVLQEK.I
417
0.67

(AFAM_HUMAN)

alpha-1-
P01011
R.LYGSEAFATDF
418
0.61

antichymotrypsin
(AACT_HUMAN)
QDSAAAKK.L

alpha-1-
P01011
K.EQLSLLDRFTE
419
0.71

antichymotrypsin
(AACT_HUMAN)
DAKR.L

alpha-1-
P01011
R.EIGELYLPK.F
420
0.68

antichymotrypsin
(AACT_HUMAN)

alpha-1-
P01011
R.WRDSLEFR.E
421
0.71

antichymotrypsin
(AACT_HUMAN)

alpha-1-
P01011
K.RLYGSEAFATD
422
0.89

antichymotrypsin
(AACT_HUMAN)
FQDSAAAK.K

alpha-1B-
P04217
R.FALVR.E
423
1.00

glycoprotein
(A1BG_HUMAN)

alpha-1B-
P04217
R. GVTFLLRR.E
424
0.67

glycoprotein
(A1BG_HUMAN)

alpha-1B-
P04217
R.RGEKELLVPR.S
425
0.71

glycoprotein
(A1BG_HUMAN)

alpha-1B-
P04217
K.ELLVPR.S
426
0.61

glycoprotein
(A1BG_HUMAN)

alpha-1B-
P04217
K.NGVAQEPVHLD
427
0.64

glycoprotein
(A1BG_HUMAN)
SPAIK.H

alpha-2-
P08697
R.NKFDPSLTQR.D
428
0.60

antiplasmin
(A2AP_HUMAN)

alpha-2-
P08697
R. QLTSGPNQEQV
429
0.67

antiplasmin
(A2AP_HUMAN)
SPLTLLK.L

alpha-2-
P08697
K.HQM*DLVATLS
138
0.67

antiplasmin
(A2AP_HUMAN)
QLGLQELFQAPDL

R.G

angiotensinogen
P01019
R.FM*QAVTGWK.T
430
0.60

preproprotein
(ANGT_HUMAN)

angiotensinogen
P01019
K.PKDPTFIPAPIQ
431
0.83

preproprotein
(ANGT_HUMAN)
AK.T

angiotensinogen
P01019
R.SLDFTELDVAA
432
0.60

preproprotein
(ANGT_HUMAN)
EK.I

ankyrin repeat
Q8NFD2
R.KNLVPR.D
433
1.00

and protein
(ANKK1_HUMAN)

kinase domain-

containing

protein 1

antithrombin-III
P01008
R.RVWELSK.A
434
0.68

(ANT3_HUMAN)

apolipoprotein
P06727
K.VKIDQTVEELR
435
0.62

A-IV
(APOA4_HUMAN)
R.S

apolipoprotein
P06727
K.DLRDKVNSFFS
436
0.92

A-IV
(APOA4_HUMAN)
TFK.E

apolipoprotein
P06727
K.LVPFATELHER.L
437
0.71

A-IV
(APOA4_HUMAN)

apolipoprotein
P06727
R.RVEPYGENFNK.A
438
0.86

A-IV
(APOA4_HUMAN)

apolipoprotein
P06727
K.VNSFFSTFK.E
439
0.87

A-IV
(APOA4_HUMAN)

apolipoprotein B-
P04114
K.AVSM*PSFSILG
440
0.70

100
(APOB_HUMAN)
SDVR.V

apolipoprotein B-
P04114
K.AVSMPSFSILGS
440
0.66

100
(APOB_HUMAN)
DVR.V

apolipoprotein B-
P04114
K.AVSMPSFSILGS
440
0.66

100
(APOB_HUMAN)
DVR.V

apolipoprotein B-
P04114
K.AVSM*PSFSILG
440
0.70

100
(APOB_HUMAN)
SDVR.V

apolipoprotein B-
P04114
K.VNWEEEAASGL
441
0.60

100
(APOB_HUMAN)
LTSLKDNVPK.A

apolipoprotein B-
P04114
R.DLKVEDIPLAR.I
442
0.70

100
(APOB_HUMAN)

apolipoprotein C-I
P02654
K.MREWFSETFQK
443
0.73

(APOC1_HUMAN)
.V

apolipoprotein C-
P02655
K.STAAMSTYTGI
444
0.68

II
(APOC2_HUMAN)
FTDQVLSVLKGEE

.-

apolipoprotein E
P02649
R.AKLEEQAQQIR.L
445
0.67

(APOE_HUMAN)

apolipoprotein E
P02649
R.FWDYLR.W
446
0.67

(APOE_HUMAN)

apolipoprotein E
P02649
R.LKSWFEPLVED
447
0.65

(APOE_HUMAN)
MQR.Q

beta-2-
P02749
K.VSFFCK.N
448
0.67

glycoprotein 1
(APOH_HUMAN)

beta-2-
P02749
R.VCPFAGILENG
449
0.63

glycoprotein 1
(APOH_HUMAN)
AVR.Y

beta-2-
P61769
K.SNFLNCYVSGF
450
0.60

microglobulin
(B2MG_HUMAN)
HPSDIEVDLLK.N

biotinidase
P43251
R.LSSGLVTAALY
451
1.00

(BTD_HUMAN)
GR.L

carboxypeptidase
Q96IY4
K.IAWHVIR.N
452
0.90

B2 preproprotein
(CBPB2_HUMAN)

carboxypeptidase
P22792
K.LSNNALSGLPQ
453
0.62

N subunit 2
(CPN2_HUMAN)
GVFGK.L

carboxypeptidase
P15169
R.DHLGFQVTWPD
454
0.93

N subunit 2
(CBPN_HUMAN)
ESK.A

ceruloplasmin
P00450
K.VYVHLK.N
455
0.67

(CERU_HUMAN)

ceruloplasmin
P00450
K.LISVDTEHSNIY
456
0.62

(CERU_HUMAN)
LQNGPDR.I

ceruloplasmin
P00450
K.M*YYSAVDPTK
194
0.76

(CERU_HUMAN)
DIFTGLIGPM*K.I

ceruloplasmin
P00450
K.M*YYSAVDPTK
194
0.68

(CERU_HUMAN)
DIFTGLIGPMK.I

ceruloplasmin
P00450
R.QKDVDKEFYLF
200
0.66

(CERU_HUMAN)
PTVFDENESLLLE

DNIR.M

ceruloplasmin
P00450
K.DVDKEFYLFPT
457
0.60

(CERU_HUMAN)
VFDENESLLLEDN

IR.M

ceruloplasmin
P00450
K.DIFTGLIGPMK.I
458
0.62

(CERU_HUMAN)

ceruloplasmin
P00450
R.SVPPSASHVAPT
459
0.66

(CERU_HUMAN)
ETFTYEWTVPK.E

ceruloplasmin
P00450
R.GVYSSDVFDIFP
203
0.67

(CERU_HUMAN)
GTYQTLEM*FPR.T

ceruloplasmin
P00450
K.DIFTGLIGPMK.I
458
0.62

(CERU_HUMAN)

ceruloplasmin
P00450
K.VNKDDEEFIES
460
0.78

(CERU_HUMAN)
NK.M

clusterin
P10909
R.KYNELLK.S
461
0.75

preproprotein
(CLUS_HUMAN)

coagulation
P00748
R.TTLSGAPCQPW
462
0.64

factor XII
(FA12_HUMAN)
ASEATYR.N

complement C1q
P02745
K.GHIYQGSEADS
463
0.64

subcomponent
(C1QA_HUMAN)
VFSGFLIFPSA.-

subunit A

complement C1q
P02747
K.FQSVFTVTR.Q
464
0.65

subcomponent
(C1QC_HUMAN)

subunit C

complement C1r
P00736
R.WILTAAHTLYP
465
0.68

subcomponent
(C1R_HUMAN)
K.E

complement C1r
P00736
K.VLNYVDWIKK.E
466
0.81

subcomponent
(C1R_HUMAN)

complement C1s
P09871
R.LPVAPLRK.C
467
0.63

subcomponent
(C1S_HUMAN)

complement C2
P06681
R.PICLPCTMEANL
468
0.78

(CO2_HUMAN)
ALR.R

complement C2
P06681
R.QHLGDVLNFLP
469
0.70

(CO2_HUMAN)
L.-

complement C4-
P0C0L5
K.LGQYASPTAKR
470
0.89

B-like
(CO4B_HUMAN)
.C

preproprotein

complement C4-
P0C0L5
K.M*RPSTDTITV
224
0.65

B-like
(CO4B_HUMAN)
MVENSHGLR.V

preproprotein

complement C4-
P0C0L5
K.MRPSTDTITVM
224
0.72

B-like
(CO4B_HUMAN)
VENSHGLR.V

preproprotein

complement C5
P01031
K.EFPYRIPLDLVP
471
0.67

preproprotein
(CO5_HUMAN)
K.T

complement C5
P01031
R.VFQFLEK.S
472
0.60

preproprotein
(CO5_HUMAN)

complement C5
P01031
R.MVETTAYALLT
473
0.61

preproprotein
(CO5_HUMAN)
SLNLK.D

complement C5
P01031
R.ENSLYLTAFTVI
474
0.81

preproprotein
(CO5_HUMAN)
GIR.K

complement
P07357
K.YNPVVIDFEMQ
475
0.62

component C8
(CO8A_HUMAN)
PIHEVLR.H

alpha chain

complement
P07358
K.IPGIFELGISSQS
476
0.61

component C8
(CO8B_HUMAN)
DR.G

beta chain

preproprotein

complement
P07360
R.RPASPISTIQPK.A
477
0.71

component C8
(CO8G_HUMAN)

gamma chain

complement
P07360
R.FLQEQGHR.A
478
0.87

component C8
(CO8G_HUMAN)

gamma chain

complement
P00751
K.VSVGGEKR.D
479
0.60

factor B
(CFAB_HUMAN)

preproprotein

complement
P00751
K.CLVNLIEK.V
480
0.69

factor B
(CFAB_HUMAN)

preproprotein

complement
P00751
K.KDNEQHVFK.V
481
0.68

factor B
(CFAB_HUMAN)

preproprotein

complement
P00751
K.ISVIRPSK.G
482
0.63

factor B
(CFAB_HUMAN)

preproprotein

complement
P00751
K.KCLVNLIEK.V
483
0.63

factor B
(CFAB_HUMAN)

preproprotein

complement
P00751
R.LPPTTTCQQQK
484
0.64

factor B
(CFAB_HUMAN)
EELLPAQDIK.A

preproprotein

complement
P00751
K.LQDEDLGFL.-
485
0.66

factor B
(CFAB_HUMAN)

preproprotein

complement
P08603
K.SCDIPVFMNAR.T
486
0.60

factor H
(CFAH_HUMAN)

complement
P08603
K.HGGLYHENMR.R
487
0.75

factor H
(CFAH_HUMAN)

complement
P08603
K.IIYKENER.F
488
0.69

factor H
(CFAH_HUMAN)

complement
P05156
K.RAQLGDLPWQ
489
0.68

factor I
(CFAI_HUMAN)
VAIK.D

preproprotein

conserved
Q9Y2V7
K.ISNLLK.F
490
0.71

oligomeric Golgi
(COG6_HUMAN)

complex subunit

6 isoform

cornulin
Q9UBG3
R.RYARTEGNCTA
491
0.81

(CRNN_HUMAN)
LTR.G

FERM domain-
Q9BZ67
R.VQLGPYQPGRP
492
0.63

containing
(FRMD8_HUMAN)
AACDLR.E

protein 8

gelsolin
P06396
R.VPEARPNSMVV
493
0.61

(GELS_HUMAN)
EHPEFLK.A

gelsolin
P06396
K.AGKEPGLQIWR
494
0.70

(GELS_HUMAN)
.V

glucose-induced
Q9NWU2
K.VWSEVNQAVL
495
0.83

degradation
(GID8_HUMAN)
DYENRESTPK.L

protein 8

homolog

hemK
Q9Y5R4
R.M*LWALLSGPG
496
0.61

methyltransferase
(HEMK1_HUMAN)
RRGSTR.G

family member 1

hemopexin
P02790
R.ELISER.W
497
0.82

(HEMO_HUMAN)

hemopexin
P02790
R.DVRDYFM*PCP
498
0.70

(HEMO_HUMAN)
GR.G

hemopexin
P02790
K.GDKVWVYPPE
499
0.71

(HEMO_HUMAN)
KK.E

hemopexin
P02790
R.DVRDYFMPCPG
498
0.60

(HEMO_HUMAN)
R.G

hemopexin
P02790
R.EWFWDLATGT
295
0.65

(HEMO_HUMAN)
MK.E

hemopexin
P02790
R.YYCFQGNQFLR
500
0.68

(HEMO_HUMAN)
.F

hemopexin
P02790
R.RLWWLDLK.S
501
0.65

(HEMO_HUMAN)

heparin cofactor 2
P05546
R.LNILNAK.F
502
0.75

(HEP2_HUMAN)

heparin cofactor 2
P05546
R.NFGYTLR.S
503
0.66

(HEP2_HUMAN)

histone
Q8TEE9
K.LLPPPPIM*SAR
504
0.63

deacetylase
(SAP25_HUMAN)
VLPR.P

complex subunit

SAP25

hyaluronan-
Q14520
K.RPGVYTQVTK.F
505
0.68

binding protein 2
(HABP2_HUMAN)

hyaluronan-
Q14520
K.FLNWIK.A
506
0.62

binding protein 2
(HABP2_HUMAN)

immediate early
Q5T953
-
507
0.93

response gene 5-
(IER5L_HUMAN)
.MECALDAQSLISI

like protein

SLRKIHSSR.T

inactive caspase-
Q6UXS9
K.AGADTHGRLLQ
508
0.60

12
(CASPC_HUMAN)
GNICNDAVTK.A

insulin-like
P35858
K.ANVFVQLPR.L
509
0.62

growth factor-
(ALS_HUMAN)

binding protein

complex acid

labile subunit

inter-alpha-
P19827
K.ELAAQTIKK.S
510
0.71

trypsin inhibitor
(ITIH1_HUMAN)

heavy chain H1

inter-alpha-
P19827
K.ILGDM*QPGDY
313
0.79

trypsin inhibitor
(ITIH1_HUMAN)
FDLVLFGTR.V

heavy chain H1

inter-alpha-
P19827
K.VTFQLTYEEVL
511
0.70

trypsin inhibitor
(ITIH1_HUMAN)
KR.N

heavy chain H1

inter-alpha-
P19827
R.TMEQFTIHLTV
512
0.61

trypsin inhibitor
(ITIH1_HUMAN)
NPQSK.V

heavy chain H1

inter-alpha-
P19827
R.FAHYVVTSQVV
513
0.63

trypsin inhibitor
(ITIH1_HUMAN)
NTANEAR.E

heavy chain H1

inter-alpha-
P19823
R.SSALDMENFRT
514
0.89

trypsin inhibitor
(ITIH2_HUMAN)
EVNVLPGAK.V

heavy chain H2

inter-alpha-
P19823
K.MKQTVEAMK.T
515
0.93

trypsin inhibitor
(ITIH2_HUMAN)

heavy chain H2

inter-alpha-
P19823
R.IYLQPGR.L
516
0.66

trypsin inhibitor
(ITIH2_HUMAN)

heavy chain H2

inter-alpha-
P19823
K.HLEVDVWVIEP
517
0.61

trypsin inhibitor
(ITIH2_HUMAN)
QGLR.F

heavy chain H2

inter-alpha-
P19823
K.FYNQVSTPLLR.N
518
0.89

trypsin inhibitor
(ITIH2_HUMAN)

heavy chain H2

inter-alpha-
P19823
R.KLGSYEHR.I
519
0.69

trypsin inhibitor
(ITIH2_HUMAN)

heavy chain H2

inter-alpha-
Q14624
K.GSEMVVAGK.L
520
1.00

trypsin inhibitor
(ITIH4_HUMAN)

heavy chain H4

inter-alpha-
Q14624
R.MNFRPGVLSSR.Q
521
0.72

trypsin inhibitor
(ITIH4_HUMAN)

heavy chain H4

inter-alpha-
Q14624
K.YIFHNFM*ER.L
325
0.73

trypsin inhibitor
(ITIH4_HUMAN)

heavy chain H4

inter-alpha-
Q14624
K.ETLFSVMPGLK.M
522
0.60

trypsin inhibitor
(ITIH4_HUMAN)

heavy chain H4

inter-alpha-
Q14624
R.FKPTLSQQQK.S
523
0.64

trypsin inhibitor
(ITIH4_HUMAN)

heavy chain H4

inter-alpha-
Q14624
K.WKETLFSVMPG
329
0.69

trypsin inhibitor
(ITIH4_HUMAN)
LK.M

heavy chain H4

inter-alpha-
Q14624
R.RLGVYELLLK.V
524
0.65

trypsin inhibitor
(ITIH4_HUMAN)

heavy chain H4

inter-alpha-
Q14624
R.DTDRFSSHVGG
525
0.69

trypsin inhibitor
(ITIH4_HUMAN)
TLGQFYQEVLWG

heavy chain H4

SPAASDDGRR.T

inter-alpha-
Q14624
K.VRPQQLVK.H
526
0.62

trypsin inhibitor
(ITIH4_HUMAN)

heavy chain H4

inter-alpha-
Q14624
R.NVHSAGAAGSR
527
0.69

trypsin inhibitor
(ITIH4_HUMAN)
.M

heavy chain H4

kallistatin
P29622
R.LGFTDLFSK.W
528
0.63

(KAIN_HUMAN)

kallistatin
P29622
R.VGSALFLSHNL
529
0.62

(KAIN_HUMAN)
K.F

kininogen-1
P01042
R.VQVVAGKK.Y
530
0.68

(KNG1_HUMAN)

leucine-rich
P02750
R.LHLEGNKLQVL
531
0.75

alpha-2-
(A2GL_HUMAN)
GK.D

glycoprotein

lumican
P51884
R.FNALQYLR.L
532
0.77

(LUM_HUMAN)

m7GpppX
Q96C86
R.IVFENPDPSDGF
533
0.94

diphosphatase
(DCPS_HUMAN)
VLIPDLK.W

MAGUK p55
Q8N3R9
K.ILEIEDLFSSLK.H
534
0.69

subfamily
(MPP5_HUMAN)

member 5

MBT domain-
Q05BQ5
K.WFDYLR.E
535
0.63

containing
(MBTD1_HUMAN)

protein 1

obscurin
Q5VST9
R.CELQIRGLAVE
536
0.73

(OBSCN_HUMAN)
DTGEYLCVCGQE

RTSATLTVR.A

olfactory
Q8NH94
K.DMKQGLAKLM
537
0.89

receptor 1L1
(OR1L1_HUMAN)
*HR.M

phosphatidylinositol-
P80108
K.GIVAAFYSGPSL
538
0.79

glycan-
(PHLD_HUMAN)
SDKEK.L

specific

phospholipase D

phosphatidylinositol-
P80108
R.TLLLVGSPTWK.N
539
0.65

glycan-
(PHLD_HUMAN)

specific

phospholipase D

phosphatidylinositol-
P80108
R.WYVPVKDLLGI
540
0.92

glycan-
(PHLD_HUMAN)
YEK.L

specific

phospholipase D

pigment
P36955
R.SSTSPTTNVLLS
541
0.63

epithelium-
(PEDF_HUMAN)
PLSVATALSALSL

derived factor

GAEQR.T

plasma protease
P05155
K.GVTSVSQIFHSP
542
0.60

C1 inhibitor
(IC1_HUMAN)
DLAIR.D

PREDICTED:
P0C0L4
R.DKGQAGLQR.A
543
0.67

complement C4-A
(CO4A_HUMAN)

PREDICTED:
P0C0L4
K.SHKPLNMGK.V
544
0.87

complement C4-A
(C04A_HUMAN)

PREDICTED:
P0C0L4
R.KKEVYM*PSSIF
236
0.67

complement C4-A
(CO4A_HUMAN)
QDDFVIPDISEPGT

WK.I

PREDICTED:
P0C0L4
R.FGLLDEDGKK.T
545
0.64

complement C4-A
(CO4A_HUMAN)

PREDICTED:
P0C0L4
R.KKEVYMPSSIF
236
0.69

complement C4-A
(CO4A_HUMAN)
QDDFVIPDISEPGT

WK.I

PREDICTED:
P0C0L4
K.GLCVATPVQLR
546
0.78

complement C4-A
(CO4A_HUMAN)
.V

PREDICTED:
P0C0L4
R.YRVFALDQK.M
547
0.63

complement C4-A
(CO4A_HUMAN)

PREDICTED:
P0C0L4
K.AEFQDALEKLN
548
0.60

complement C4-A
(CO4A_HUMAN)
MGITDLQGLR.L

PREDICTED:
P0C0L4
R.ECVGFEAVQEV
549
0.60

complement C4-A
(CO4A_HUMAN)
PVGLVQPASATL

YDYYNPERR.C

PREDICTED:
P0C0L4
K.AEFQDALEKLN
548
0.60

complement C4-A
(CO4A_HUMAN)
MGITDLQGLR.L

PREDICTED:
P0C0L4
R.VTASDPLDTLG
550
0.61

complement C4-A
(CO4A_HUMAN)
SEGALSPGGVASL

LR.L

pregnancy zone
P20742
R.NELIPLIYLENP
551
0.60

protein
(PZP_HUMAN)
RR.N

pregnancy zone
P20742
K.AVGYLITGYQR.Q
552
0.67

protein
(PZP_HUMAN)

protein AMBP
P02760
R.AFIQLWAFDAV
553
0.70

preproprotein
(AMBP_HUMAN)
K.G

protein
O43439
R.LTEREWADEW
554
0.61

CBFA2T2
(MTG8R_HUMAN)
KHLDHALNCIME

MVEK.T

protein NLRC3
Q7RTR2
K.ALM*DLLAGKG
555
0.83

(NLRC3_HUMAN)
SQGSQAPQALDR.T

prothrombin
P00734
R.TFGSGEADCGL
556
0.69

preproprotein
(THRB_HUMAN)
RPLFEK.K

ras-related GTP-
Q7L523
K.ISNIIK.Q
557
0.68

binding protein A
(RRAGA_HUMAN)

retinol-binding
P02753
R.FSGTWYAMAK.K
558
0.64

protein 4
(RET4_HUMAN)

retinol-binding
P02753
R.LLNNWDVCAD
559
0.61

protein 4
(RET4_HUMAN)
MVGTFTDTEDPA

KFK.M

retinol-binding
P02753
K.YWGVASFLQK.G
560
0.63

protein 4
(RET4_HUMAN)

serum amyloid P-
P02743
R.GYVIIKPLVWV.-
561
0.60

component
(SAMP_HUMAN)

sex hormone-
P04278
R.LPLVPALDGCL
562
0.63

binding globulin
(SHBG_HUMAN)
R.R

spectrin beta
Q13813
R.NELIRQEKLEQL
563
0.88

chain, non-
(SPTN1_HUMAN)
AR.R

erythrocytic 1

TATA element
P82094
K.EELATRLNSSET
564
0.71

modulatory
(TMF1_HUMAN)
ADLLK.E

factor

testicular haploid
PODJG4
R.QCLLNRPFSDN
565
0.67

expressed gene
(THEGL_HUMAN)
SAR.D

protein-like

thyroxine-
P05543
K.NALALFVLPK.E
566
0.61

binding globulin
(THBG_HUMAN)

thyroxine-
P05543
R.SFMLLILER.S
567
0.64

binding globulin
(THBG_HUMAN)

titin
Q8WZ42
K.TEPKAPEPISSK.P
568
0.89

(TITIN_HUMAN)

transthyretin
P02766
R.GSPAINVAVHV
569
0.61

(TTHY_HUMAN)
FR.K

tripartite motif-
Q9C035
R.ELISDLEHRLQG
570
0.92

containing
(TRIM5_HUMAN)
SVM*ELLQGVDG

protein 5

VIK.R

vitamin D-
P02774
K.TAMDVFVCTYF
571
0.88

binding protein
(VTDB_HUMAN)
MPAAQLPELPDV

ELPTNKDVCDPG

NTK.V

vitamin D-
P02774
K.VM*DKYTFELS
572
0.70

binding protein
(VTDB_HUMAN)
R.R

vitamin D-
P02774
K.LAQKVPTADLE
573
0.61

binding protein
(VTDB_HUMAN)
DVLPLAEDITNILS

K.C

vitamin D-
P02774
K.SCESNSPFPVHP
574
0.68

binding protein
(VTDB_HUMAN)
GTAECCTK.E

vitamin D-
P02774
R.KLCMAALK.H
575
0.71

binding protein
(VTDB_HUMAN)

vitamin D-
P02774
K.LCDNLSTK.N
576
0.60

binding protein
(VTDB_HUMAN)

vitamin D-
P02774
K.VM*DKYTFELS
572
0.70

binding protein
(VTDB_HUMAN)
R.R

vitronectin
P04004
R.IYISGM*APR.P
577
0.75

(VTNC_HUMAN)

vitronectin
P04004
R.ERVYFFK.G
578
0.67

(VTNC_HUMAN)

vitronectin
P04004
R.IYISGMAPR.P
577
0.81

(VTNC_HUMAN)

vitronectin
P04004
K.AVRPGYPK.L
579
0.63

(VTNC_HUMAN)

zinc finger
P52746
K.TRFLLR.T
580
0.67

protein 142
(ZN142_HUMAN)

*= Oxidation of methionine

TABLE 10

Preeclampsia: Additional peptides significant with AUC >0.6 by

X!Tandem only

SEQ

Protein

ID

description
Uniprot ID (name)
Peptide
NO:
XT_AUC

afamin
P43652
K.TYVPPPFSQDLFTFHA
581
0.76

(AFAM_HUMAN)
DMCQSQNEELQR.K

afamin
P43652
K.KSDVGFLPPFPTLDPEE
582
0.62

(AFAM_HUMAN)
K.C

alpha-1-
P01011
R.GTHVDLGLASANVDF
583
0.69

antichymotrypsin
(AACT_HUMAN)
AFSLYK.Q

alpha-1B-
P04217
K.SLPAPWLSM*APVSWI
584
0.67

glycoprotein
(A1BG_HUMAN)
TPGLK.T

alpha-1B-
P04217
K.SLPAPWLSM*APVSWI
584
0.67

glycoprotein
(A1BG_HUMAN)
TPGLK.T

alpha-1B-
P04217
R.C{circumflex over ( )}LAPLEGAR.F
585
0.62

glycoprotein
(A1BG_HUMAN)

alpha-2-
P08697
R.WFLLEQPEIQVAHFPF
586
0.60

antiplasmin
(A2AP_HUMAN)
K.N

alpha-2-
P08697
R.LCQDLGPGAFR.L
587
0.92

antiplasmin
(A2AP_HUMAN)

alpha-2-
P08697
K.HQMDLVATLSQLGLQ
138
0.67

antiplasmin
(A2AP_HUMAN)
ELFQAPDLR.G

alpha-2-HS-
P02765
R.QLKEHAVEGDCDFQL
588
0.63

glycoprotein
(FETUA_HUMAN)
LK.L

preproprotein

alpha-2-HS-
P02765
R.Q{circumflex over ( )}LKEHAVEGDCDFQ
588
0.65

glycoprotein
(FETUA_HUMAN)
LLK.L

preproprotein

alpha-2-HS-
P02765
K.C{circumflex over ( )}NLLAEK.Q
589
0.61

glycoprotein
(FETUA_HUMAN)

preproprotein

angiotensinogen
P01019
R.SLDFTELDVAAEKIDR.F
590
0.62

preproprotein
(ANGT_HUMAN)

angiotensinogen
P01019
K.DPTFIPAPIQAK.T
591
0.78

preproprotein
(ANGT_HUMAN)

apolipoprotein
P02652
K.EPCVESLVSQYFQTVT
592
0.67

A-II
(APOA2_HUMAN)
DYGKDLMEK.V

preproprotein

apolipoprotein B-
P04114
K.FSVPAGIVIPSFQALTA
593
0.66

100
(APOB_HUMAN)
R.F

apolipoprotein B-
P04114
K.EQHLFLPFSYK.N
594
0.90

100
(APOB_HUMAN)

apolipoprotein B-
P04114
R.GIISALLVPPETEEAK.Q
595
0.70

100
(APOB_HUMAN)

beta-2-
P02749
K.C{circumflex over ( )}FKEHSSLAFWK.T
596
0.70

glycoprotein 1
(APOH_HUMAN)

beta-2-
P02749
K.EHSSLAFWK.T
597
0.62

glycoprotein 1
(APOH_HUMAN)

ceruloplasmin
P00450
R.FNKNNEGTYYSPNYN
598
0.64

(CERU_HUMAN)
PQSR.S

ceruloplasmin
P00450
K.HYYIGIIETTWDYASD
599
0.63

(CERU_HUMAN)
HGEK.K

ceruloplasmin
P00450
K.M*YYSAVDPTKDIFTG
194
0.66

(CERU_HUMAN)
LIGPM*K.I

ceruloplasmin
P00450
K.M*YYSAVDPTKDIFTG
194
0.66

(CERU_HUMAN)
LIGPM*K.I

ceruloplasmin
P00450
K.M*YYSAVDPTKDIFTG
194
0.67

(CERU_HUMAN)
LIGPMK.I

ceruloplasmin
P00450
K.M*YYSAVDPTKDIFTG
194
0.67

(CERU_HUMAN)
LIGPMK.I

ceruloplasmin
P00450
K.MYYSAVDPTKDIFTGL
194
0.67

(CERU_HUMAN)
IGPM*K.I

ceruloplasmin
P00450
K.MYYSAVDPTKDIFTGL
194
0.67

(CERU_HUMAN)
IGPM*K.I

ceruloplasmin
P00450
R.GVYSSDVFDIFPGTYQ
203
0.67

(CERU_HUMAN)
TLEM*FPR.T

coagulation
P00748
R.VVGGLVALR.G
600
0.64

factor XII
(FA12_HUMAN)

complement C1q
P02745
K.KGHIYQGSEADSVFSG
601
0.81

subcomponent
(C1QA HUMAN)
FLIFPSA.-

subunit A

complement C1q
P02747
R.Q{circumflex over ( )}THQPPAPNSLIR.F
602
0.64

subcomponent
(C1QC_HUMAN)

subunit C

complement C1s
P09871
R.Q{circumflex over ( )}FGPYCGHGFPGPLN
603
0.71

subcomponent
(C1S_HUMAN)
IETK.S

complement C2
P06681
R.QPYSYDFPEDVAPALG
604
0.63

(CO2_HUMAN)
TSFSHMLGATNPTQK.T

complement C2
P06681
R.LLGMETMAWQEIR.H
605
0.70

(CO2_HUMAN)

complement C4-
P0C0L5
R.AVGSGATFSHYYYM*I
606
0.67

B-like
(CO4B_HUMAN)
LSR.G

preproprotein

complement C4-
P0C0L5
R.FGLLDEDGKKTFFR.G
607
0.61

B-like
(CO4B_HUMAN)

preproprotein

complement C4-
P0C0L5
K.ITQVLHFTK.D
608
0.67

B-like
(CO4B_HUMAN)

preproprotein

complement C4-
P0C0L5
K.M*RPSTDTITVM*VEN
224
0.65

B-like
(CO4B_HUMAN)
SHGLR.V

preproprotein

complement C4-
P0C0L5
K.M*RPSTDTITVM*VEN
224
0.75

B-like
(CO4B_HUMAN)
SHGLR.V

preproprotein

complement C5
P01031
R.IVACASYKPSR.E
609
0.67

preproprotein
(CO5_HUMAN)

complement C5
P01031
R.SYFPESWLWEVHLVP
610
0.60

preproprotein
(CO5_HUMAN)
R.R

complement C5
P01031
K.Q{circumflex over ( )}LPGGQNPVSYVYLE
611
0.74

preproprotein
(CO5_HUMAN)
VVSK.H

complement C5
P01031
K.TLLPVSKPEIR.S
612
0.78

preproprotein
(CO5_HUMAN)

complement
P07358
R. GGASEHITTLAYQELP
613
0.60

component C8
(CO8B_HUMAN)
TADLMQEWGDAVQYNP

beta chain

AIIK.V

preproprotein

complement
P00751
K.GTDYHKQPWQAK.I
614
0.89

factor B
(CFAB_HUMAN)

preproprotein

complement
P00751
K.VKDISEVVTPR.F
615
0.64

factor B
(CFAB_HUMAN)

preproprotein

complement
P00751
K.Q{circumflex over ( )}VPAHAR.D
616
0.63

factor B
(CFAB_HUMAN)

preproprotein

complement
P00751
R.GDSGGPLIVHKR.S
617
0.79

factor B
(CFAB_HUMAN)

preproprotein

complement
P00751
R.FLCTGGVSPYADPNTC
618
0.71

factor B
(CFAB_HUMAN)
R.G

preproprotein

complement
P00751
K.KEAGIPEFYDYDVALI
619
0.74

factor B
(CFAB_HUMAN)
K.L

preproprotein

complement
P00751
R.YGLVTYATYPK.I
620
0.88

factor B
(CFAB_HUMAN)

preproprotein

complement
P08603
K.EFDHNSNIR.Y
621
1.00

factor H
(CFAH_HUMAN)

complement
P08603
K.WSSPPQCEGLPCK.S
622
0.71

factor H
(CFAH_HUMAN)

complement
P08603
R.KGEWVALNPLR.K
623
0.67

factor H
(CFAH_HUMAN)

complement
P05156
K.SLECLHPGTK.F
624
0.60

factor I
(CFAI_HUMAN)

preproprotein

corticosteroid-
P08185
R.GLASANVDFAFSLYK.H
625
0.62

binding globulin
(CBG_HUMAN)

fetuin-B
Q9UGM5
K.LVVLPFPK.E
626
0.74

(FETUB_HUMAN)

fetuin-B
Q9UGM5
R.ASSQWVVGPSYFVEY
627
0.61

(FETUB_HUMAN)
LIK.E

ficolin-3
O75636
R.LLGEVDHYQLALGK.F
628
0.61

(FCN3_HUMAN)

gelsolin
P06396
K.QTQVSVLPEGGETPLF
629
0.69

(GELS_HUMAN)
K.Q

hemopexin
P02790
K.VDGALCMEK.S
630
0.60

(HEMO_HUMAN)

hemopexin
P02790
K.SGAQATWTELPWPHE
631
0.66

(HEMO_HUMAN)
KVDGALCM*EK.S

hemopexin
P02790
K.SGAQATWTELPWPHE
631
0.66

(HEMO_HUMAN)
KVDGALCM*EK.S

hemopexin
P02790
R.EWFWDLATGTMK.E
295
0.68

(HEMO_HUMAN)

hemopexin
P02790
R.Q{circumflex over ( )}GHNSVFLIK.G
632
0.67

(HEMO_HUMAN)

heparin cofactor 2
P05546
K.TLEAQLTPR.V
633
0.67

(HEP2_HUMAN)

histidine-rich
P04196
K.DSPVLIDFFEDTER.Y
634
0.60

glycoprotein
(HRG_HUMAN)

insulin-like
P35858
K.ALRDFALQNPSAVPR.F
635
0.89

growth factor-
(ALS_HUMAN)

binding protein

complex acid

labile subunit

insulin-like
P35858
R.LWLEGNPWDCGCPLK
636
0.60

growth factor-
(ALS_HUMAN)
.A

binding protein

complex acid

labile subunit

inter-alpha-
P19827
K.ILGDM*QPGDYFDLVL
313
0.85

trypsin inhibitor
(ITIH1_HUMAN)
FGTR.V

heavy chain H1

inter-alpha-
P19823
R.SSALDMENFR.T
637
0.63

trypsin inhibitor
(ITIH2_HUMAN)

heavy chain H2

inter-alpha-
P19823
R.SLAPTAAAK.R
638
0.83

trypsin inhibitor
(ITIH2_HUMAN)

heavy chain H2

inter-alpha-
P19823
R.LSNENHGIAQR.I
639
0.76

trypsin inhibitor
(ITIH2_HUMAN)

heavy chain H2

inter-alpha-
P19823
R.IYGNQDTSSQLKK.F
640
0.63

trypsin inhibitor
(ITIH2_HUMAN)

heavy chain H2

inter-alpha-
Q14624
K.TGLLLLSDPDKVTIGL
641
0.60

trypsin inhibitor
(ITIH4_HUMAN)
LFWDGR.G

heavy chain H4

inter-alpha-
Q14624
K.YIFHNFM*ER.L
325
0.70

trypsin inhibitor
(ITIH4_HUMAN)

heavy chain H4

inter-alpha-
Q14624
K.IPKPEASFSPR.R
642
0.65

trypsin inhibitor
(ITIH4_HUMAN)

heavy chain H4

inter-alpha-
Q14624
R.QGPVNLLSDPEQGVEV
643
0.64

trypsin inhibitor
(ITIH4_HUMAN)
TGQYER.E

heavy chain H4

inter-alpha-
Q14624
R.ANTVQEATFQMELPK.K
644
0.61

trypsin inhibitor
(ITIH4_HUMAN)

heavy chain H4

inter-alpha-
Q14624
K.WKETLFSVMPGLK.M
329
0.66

trypsin inhibitor
(ITIH4_HUMAN)

heavy chain H4

inter-alpha-
Q14624
R.RLDYQEGPPGVEISCW
645
0.69

trypsin inhibitor
(ITIH4_HUMAN)
SVEL.-

heavy chain H4

inter-alpha-
Q14624
K.SPEQQETVLDGNLIIR.Y
646
0.66

trypsin inhibitor
(ITIH4_HUMAN)

heavy chain H4

kallistatin
P29622
K.ALWEKPFISSR.T
647
0.65

(KAIN_HUMAN)

kininogen-1
P01042
R.Q{circumflex over ( )}VVAGLNFR.I
648
0.67

(KNG1_HUMAN)

kininogen-1
P01042
R.QVVAGLNFR.I
648
0.71

(KNG1_HUMAN)

kininogen-1
P01042
K.LGQSLDCNAEVYVVP
649
0.62

(KNG1_HUMAN)
WEK.K

kininogen-1
P01042
R.IASFSQNCDIYPGKDFV
650
0.64

(KNG1_HUMAN)
QPPTK.I

leucine-rich
P02750
R.C{circumflex over ( )}AGPEAVKGQTLLA
651
0.70

alpha-2-
(A2GL_HUMAN)
VAK.S

glycoprotein

leucine-rich
P02750
K.GQTLLAVAK.S
652
0.67

alpha-2-
(A2GL_HUMAN)

glycoprotein

leucine-rich
P02750
K.DLLLPQPDLR.Y
653
0.71

alpha-2-
(A2GL_HUMAN)

glycoprotein

lumican
P51884
K.ILGPLSYSK.I
654
0.83

(LUM_HUMAN)

PREDICTED:
P0C0L4
R.QGSFQGGFR.S
655
0.83

complement C4-A
(CO4A_HUMAN)

PREDICTED:
P0C0L4
K.YVLPNFEVK.I
656
0.69

complement C4-A
(CO4A_HUMAN)

PREDICTED:
P0C0L4
R.LLATLCSAEVCQCAEG
657
0.60

complement C4-A
(CO4A_HUMAN)
K.C

PREDICTED:
P0C0L4
R.VGDTLNLNLR.A
658
0.66

complement C4-A
(CO4A_HUMAN)

PREDICTED:
P0C0L4
R.EPFLSCCQFAESLR.K
659
0.62

complement C4-A
(CO4A_HUMAN)

PREDICTED:
P0C0L4
R.EELVYELNPLDHR.G
660
0.60

complement C4-A
(CO4A_HUMAN)

PREDICTED:
P0C0L4
R.GSFEFPVGDAVSK.V
661
0.62

complement C4-A
(CO4A_HUMAN)

PREDICTED:
P0C0L4
R.GCGEQTMIYLAPTLAA
368
0.71

complement C4-A
(CO4A_HUMAN)
SR.Y

pregnancy zone
P20742
K.GSFALSFPVESDVAPIA
662
0.63

protein
(PZP_HUMAN)
R.M

protein AMBP
P02760
R.VVAQGVGIPEDSIFTM
663
0.62

preproprotein
(AMBP_HUMAN)
ADRGECVPGEQEPEPILI

PR.V

prothrombin
P00734
R.SGIECQLWR.S
664
0.65

preproprotein
(THRB_HUMAN)

thyroxine-
P05543
K.MSSINADFAFNLYR.R
665
0.63

binding globulin
(THBG_HUMAN)

vitronectin
P04004
R.MDWLVPATCEPIQSVF
666
1.00

(VTNC_HUMAN)
FFSGDKYYR.V

vitronectin
P04004
R.IYISGM*APRPSLAK.K
410
0.64

(VTNC_HUMAN)

vitronectin
P04004
R.IYISGMAPRPSLAK.K
410
0.63

(VTNC_HUMAN)

vitronectin
P04004
R.DVWGIEGPIDAAFTR.I
667
0.61

(VTNC_HUMAN)

zinc finger
Q8N567
R.SCPDNPK.G
668
0.68

CCHC domain-
(ZCHC9_HUMAN)

containing

protein 9

*= Oxidation of Methionine, {circumflex over ( )}= cyclic pyrolidone derivative by the loss of NH3 (−17 Da)

TABLE 11

Candidate peptides and transitions for transferring to the MRM assay

SEQ

ID
m/z,
fragment ion, m/z,

Protein
Peptide
NO:
charge
charge, rank
area

inter-alpha-trypsin
K.AAISGENAGLVR.A
670
579.3173++
S [y9]-902.4690+[1]
518001

inhibitor heavy chain H1

G [y8]-815.4370+[2]
326256

ITIH1_HUMAN

N [y6]-629.3729+[3]
296670

S [b4]-343.1976+[4]
258172

inter-alpha-trypsin
K.GSLVQASEANLQAA
317
668.6763+++
A [y7]-806.4155+[1]
304374

inhibitor heavy chain H1
QDFVR.G

V [b4]-357.2132+[3]
294094

ITIH1_HUMAN

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

A [y6]-735.3784+[2]
193844

F [y3]-421.2558+[4]
167816

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

A [b6]-556.3089+[5]
149216

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

inter-alpha-trypsin
K.TAFISDFAVTADGNA
309
1087.0442++
G [y4]-432.2453+[1]
22362

inhibitor heavy chain H1
FIGDIK.D

V [b9]-952.4775+[2]
9508

ITIH1_HUMAN

I [y5]-545.3293+[3]
8319

A [b8]-853.4090+[4]
7006

G [y9]-934.4993+[5]
6755

F [y6]-692.3978+[6]
6193

inter-alpha-trypsin
K.VTYDVSR.D
671
420.2165++
T [b2]-201.1234+[1]
792556

inhibitor heavy chain H1

Y [y5]-639.3097+[2]
609348

ITIH1_HUMAN

V [y3]-361.2194+[3]
256946

D [y4]-476.2463+[4]
169546

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

S [y2]-262.1510+[6]
50268

D [b4]-479.2136+[7]
13662

Y [b3]-182.5970++[8]
10947

inter-alpha-trypsin
R.EVAFDLEIPK.T
319
580.8135++
P [y2]-244.1656+[1]
2032509

inhibitor heavy chain H1

D [y6]-714.4032+[2]
672749

ITIH1_HUMAN

A [y8]-932.5088+[3]
390837

F [y7]-861.4716+[4]
305087

L [y5]-599.3763+[5]
255527

inter-alpha-trypsin
R.LWAYLTIQELLAK.R
318
781.4531++
W [b2]-300.1707+[1]
602601

inhibitor heavy chain H1

A [b3]-371.2078+[2]
356967

ITIH1_HUMAN

T [y8]-915.5510+[3]
150419

Y [b4]-534.2711+[4]
103449

L [b5]-647.3552+[5]
99820

I [y7]-814.5033+[6]
72044

Q [y6]-701.4192+[7]
66989

E [y5]-573.3606+[8]
44843

inter-alpha-trypsin
K.FYNQVSTPLLR.N
518
669.3642++
S [y6]-686.4196+[1]
367330

inhibitor heavy chain H2

V [y7]-785.4880+[2]
182396

ITIH2_HUMAN

P [y4]-498.3398+[3]
103638

Q [b4]-553.2405+[4]
54270

Y [b2]-311.1390+[5]
52172

N [b3]-425.1819+[6]
34567

inter-alpha-trypsin
K.HLEVDVWVIEPQGL
517
597.3247+++
P [y5]-570.3358+[1]
303693

inhibitor heavy chain H2
R.F

I [y7]-812.4625+[2]
206996

ITIH2_HUMAN

E [y6]-699.3784+[3]
126752

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

inter-alpha-trypsin
K.TAGLVR.S
672
308.6925++
G [y4]-444.2929+[1]
789068

inhibitor heavy chain H2

A [b2]-173.0921+[2]
460019

ITIH2_HUMAN

V [y2]-274.1874+[3]
34333

L [y3]-387.2714+[4]
29020

G [b3]-230.1135+[5]
15169

inter-alpha-trypsin
R.IYLQPGR.L
516
423.7452++
L [y5]-570.3358+[1]
638209

inhibitor heavy chain H2

Y [b2]-277.1547+[2]
266889

ITIH2_HUMAN

P [y3]-329.1932+[3]
235194

Q [y4]-457.2518+[4]
171389

inter-alpha-trypsin
R.LSNENHGIAQR.I
639
413.5461+++
N [y9]-519.7574++[1]
325409

inhibitor heavy chain H2

G [y5]-544.3202+[2]
139598

ITIH2_HUMAN

S [b2]-201.1234+[3]
54786

N [y7]-398.2146++[4]
39521

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

inter-alpha-trypsin
R.SLAPTAAAKR.R
673
415.2425++
A [y7]-629.3617+[1]
582421

inhibitor heavy chain H2

P [y6]-558.3246+[2]
463815

ITIH2_HUMAN

L [b2]-201.1234+[3]
430584

A [b3]-272.1605+[4]
204183

T [y5]-461.2718+[5]
47301

pregnancy-specific beta-
K.FQLPGQK.L
674
409.2320++
L [y5]-542.3297+[3]
192218

1-glycoprotein 1

P [y4]-429.2456+[2]
252933

PSG1_HUMAN

Q [y2]-275.1714+[6]
15366

Q [b2]-276.1343+[1]
305361

L [b3]-389.2183+[4]
27279

G [b5]-543.2926+[5]
18416

pregnancy-specific beta-
R.DLYHYITSYVVDGEIII
675
955.4762+++
G [y7]-707.3471+[1]
66891

1-glycoprotein 1
YGPAYSGR.E

Y [y8]-870.4104+[2]
45076

PSG1_HUMAN

P [y6]-650.3257+[3]
28437

I [y9]-983.4945+[4]
20423

V [b10]-628.3033++[5]
17864

E [b14]-828.3830++[6]
13690

V [b11]-677.8375++[7]
12354

I [b6]-805.3879+[8]
11186

V [y15]-805.4147++[9]
10573

G [b13]-
10407

763.8617++[10]

pregnancy-specific beta-
TLFIFGVTK
676
513.3051++
F [y7]-811.4713+[1]
102139

1-glycoprotein 4

L [b2]-215.1390+[2]
86272

PSG4_HUMAN

F [y5]-551.3188+[3]
49520

I [y6]-664.4028+[4]
26863

T [y2]-248.1605+[5]
18671

F [b3]-362.2074+[6]
17343

G [y4]-404.2504+[7]
17122

pregnancy-specific beta-
NYTYIWWLNGQSLPV
677
1097.5576++
W [b6]-841.3879+[1]
25756

1-glycoprotein 4
SPR

G [y9]-940.5211+[2]
25018

PSG4_HUMAN

Y [b4]-542.2245+[3]
19778

PSG8_HUMAN
LQLSETNR
678
480.7591++
T [y3]-390.2096+[1]
185568

pregnancy-specific

Q [b2]-242.1499+[2]
120644

beta-1-glycoprotein 8

N [y2]-289.1619+[3]
95164

S [y5]-606.2842+[4]
84314

L [b3]-355.2340+[5]
38587

E [y4]-519.2522+[6]
34807

L [y6]-719.3682+[7]
17482

E [b5]-571.3086+[8]
8855

S [b4]-442.2660+[9]
7070

Pan-PSG
ILILPSVTR
679
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.ELELQIGNALFIGK.H
680
515.6276+++
E [b3]-186.5919++[1]
48549

globulin

E [b3]-372.1765+[2]
28849

THBG_HUMAN

G [y2]-204.1343+[3]
27487

F [b11]-614.8322++[4]
14892

L [b4]-485.2606+[5]
14552

L [b2]-243.1339+[6]
10169

L [b4]-243.1339++[7]
10169

thyroxine-binding
K.AQWANPFDPSK.T
681
630.8040++
A [b4]-457.2194+[1]
48405

globulin

S [y2]-234.1448+[2]
43781

THBG_HUMAN

D [y4]-446.2245+[3]
26549

D [y4]-446.2245+[4]
25148

thyroxine-binding
K.TEDSSSFLIDK.T
682
621.2984++
E [b2]-231.0975+[1]
37113

globulin

D [y2]-262.1397+[2]
14495

THBG_HUMAN

thyroxine-binding
K.AVLHIGEK.G
392
433.7584++
V [b2]-171.1128+[1]
151828

globulin

L [y6]-696.4039+[2]
102903

THBG_HUMAN

H [y5]-583.3198+[3]
73288

I [y4]-446.2609+[4]
54128

G [y3]-333.1769+[5]
32717

H [b4]-421.2558+[6]
22662

thyroxine-binding
K.AVLHIGEK.G
392
289.5080+++
L [y6]-348.7056++[1]
2496283

globulin

V [b2]-171.1128+[2]
551283

THBG_HUMAN

I [y4]-446.2609+[3]
229168

H [y5]-292.1636++[4]
212709

H [y5]-583.3198+[5]
160132

G [y3]-333.1769+[6]
117961

H [b4]-421.2558+[7]
56579

I [y4]-223.6341++[8]
36569

H [b4]-211.1315++[9]
19460

L [b3]-284.1969+[10]
15758

thyroxine-binding
K.FLNDVK.T
683
368.2054++
N [y4]-475.2511+[1]
298227

globulin

V [y2]-246.1812+[2]
252002

THBG_HUMAN

L [b2]-261.1598+[3]
98700

D [y3]-361.2082+[4]
29215

D [b4]-490.2296+[5]
27258

N [b3]-375.2027+[6]
10971

thyroxine-binding
K.FSISATYDLGATLLK.M
394
800.4351++
S [b2]-235.1077+[1]
50075

globulin

G [y6]-602.3872+[2]
46373

THBG_HUMAN

D [y8]-830.4982+[3]
43372

Y [y9]-993.5615+[4]
40970

T [y4]-474.3286+[5]
22161

L [y7]-715.4713+[6]
19710

S [b4]-435.2238+[7]
19310

L [y3]-373.2809+[8]
14157

I [b3]-348.1918+[9]
13207

thyroxine-binding
K.LSNAAHK.A
684
370.7061++
H [y2]-284.1717+[4]
19319

globulin

S [b2]-201.1234+[1]
60611

THBG_HUMAN

N [b3]-315.1663+[2]
42142

A [b4]-386.2034+[3]
31081

thyroxine-binding
K.GWVDLFVPK.F
393
530.7949++
V [y7]-817.4818+[2]
297536

globulin

D [y6]-718.4134+[4]
226951

THBG_HUMAN

L [y5]-603.3865+[8]
60712

F [y4]-490.3024+[9]
45586

V [y3]-343.2340+[6]
134588

P [y2]-244.1656+[1]
1619888

V [b3]-343.1765+[7]
126675

D [b4]-458.2034+[10]
14705

F [b6]-718.3559+[5]
208674

V [b7]-817.4243+[3]
270156

thyroxine-binding
K.NALALFVLPK.E
566
543.3395++
L [b3]-299.1714+[1]
365040

globulin

P [y2]-244.1656+[2]
274988

THBG_HUMAN

A [y7]-787.5076+[3]
237035

L [y6]-716.4705+[4]
107838

L [y3]-357.2496+[5]
103847

L [y8]-900.5917+[6]
97265

F [y5]-603.3865+[7]
88231

A [b4]-370.2085+[8]
82559

V [y4]-456.3180+[9]
32352

L [b5]-483.2926+[10]
11974

thyroxine-binding
R.SILFLGK.V
395
389.2471++
L [y5]-577.3708+[1]
564222

globulin

I [b2]-201.1234+[2]
384240

THBG_HUMAN

G [y2]-204.1343+[3]
302557

L [y3]-317.2183+[4]
282436

F [y4]-464.2867+[5]
194047

L [b3]-314.2074+[6]
27878

leucine-rich alpha-2-
R.VLDLTR.N
685
358.7187++
D [y4]-504.2776+[1]
629222

glycoprotein

L [y5]-617.3617+[2]
236165

A2GL_HUMAN

L [b2]-213.1598+[3]
171391

L [y3]-389.2507+[4]
167609

R [y1]-175.1190+[5]
41213

T [y2]-276.1666+[6]
37194

D [b3]-328.1867+[7]
27029

leucine-rich alpha-2-
K.ALGHLDLSGNR.L
686
576.8096++
G [y9]-484.7490++[1]
46334

glycoprotein

L [y7]-774.4104+[2]
44285

A2GL_HUMAN

D [y6]-661.3264+[3]
40188

H [y8]-456.2383++[4]
29392

H [b4]-379.2088+[5]
26871

L [y5]-546.2994+[6]
17178

L [b5]-492.2929+[7]
14578

leucine-rich alpha-2-
K.LPPGLLANFTLLR.T
687
712.9348++
R [y1]-175.1190+[1]
34435

glycoprotein

A [b7]-662.4236+[2]
25768

A2GL_HUMAN

G [y10]-1117.6728+[3]
11662

leucine-rich alpha-2-
R.TLDLGENQLETLPPD
688
1019.0468++
P [y6]-710.4196+[1]
232459

glycoprotein
LLR.G

L [y7]-823.5036+[2]
16075

A2GL_HUMAN

E [y9]-1053.5939+[3]
15839

D [b3]-330.1660+[4]
15524

leucine-rich alpha-2-
R.GPLQLER.L
689
406.7349++
P [b2]-155.0815+[1]
144054

glycoprotein

Q [y4]-545.3042+[2]
103146

A2GL_HUMAN

L [y5]-658.3883+[3]
77125

L [y3]-417.2456+[4]
65928

R [y1]-175.1190+[5]
27585

E [y2]-304.1615+[6]
22956

leucine-rich alpha-2-
R.LHLEGNK.L
690
405.7271++
H [b2]-251.1503+[1]
79532

glycoprotein

L [y5]-560.3039+[2]
54272

A2GL_HUMAN

G [b5]-550.2984+[3]
49019

G [y3]-318.1772+[4]
18570

L [b3]-364.2343+[5]
14068

E [y4]-447.2198+[6]
13318

leucine-rich alpha-2-
K.LQVLGK.D
691
329.2183++
V [y4]-416.2867+[1]
141056

glycoprotein

G [y2]-204.1343+[2]
102478

A2GL_HUMAN

Q [b2]-242.1499+[3]
98414

L [y3]-317.2183+[4]
60587

Q [y5]-544.3453+[5]
50833

leucine-rich alpha-2-
K.DLLLPQPDLR.Y
692
590.3402++
P [y6]-725.3941+[1]
592715

glycoprotein

L [b3]-342.2023+[2]
570948

A2GL_HUMAN

L [b2]-229.1183+[3]
403755

P [y6]-363.2007++[4]
120157

L [y2]-288.2030+[5]
89508

L [y7]-838.4781+[6]
76185

L [b4]-455.2864+[7]
60422

L [y7]-419.7427++[8]
45849

P [y4]-500.2827+[9]
45223

L [y8]-951.5622+[10]
22393

Q [y5]-628.3413+[11]
15450

leucine-rich alpha-2-
R.VAAGAFQGLR.Q
693
495.2800++
A [y8]-819.4472+[1]
183637

glycoprotein

G [y7]-748.4100+[2]
110920

A2GL_HUMAN

F [y5]-620.3515+[3]
85535

A [y9]-890.4843+[4]
45894

G [y3]-345.2245+[5]
45644

Q [y4]-473.2831+[6]
40579

A [y8]-410.2272++[7]
39266

A [b3]-242.1499+[8]
35890

A [y6]-691.3886+[9]
29637

G [b4]-299.1714+[10]
19195

A [b5]-370.2085+[11]
14944

A [y9]-445.7458++[12]
11567

leucine-rich alpha-2-
R.WLQAQK.D
694
387.2189++
L [y5]-587.3511+[1]
80533

glycoprotein

Q [y4]-474.2671+[2]
57336

A2GL_HUMAN

A [y3]-346.2085+[3]
35952

L [b2]-300.1707+[4]
22509

leucine-rich alpha-2-
K.GQTLLAVAK.S
695
450.7793++
Q [b2]-186.0873+[1]
110213

glycoprotein

T [y7]-715.4713+[2]
81127

A2GL_HUMAN

L [y5]-501.3395+[3]
52292

L [y6]-614.4236+[4]
46349

A [y4]-388.2554+[5]
41283

A [y2]-218.1499+[6]
38843

V [y3]-317.2183+[7]
28961

T [b3]-287.1350+[8]
23831

leucine-rich alpha-2-
R.YLFLNGNK.L
696
484.7636++
F [y6]-692.3726+[1]
61861

glycoprotein

L [b2]-277.1547+[2]
39468

A2GL_HUMAN

F [b3]-424.2231+[3]
21454

L [y5]-545.3042+[4]
20016

N [y4]-432.2201+[5]
18077

leucine-rich alpha-2-
R.NALTGLPPGLFQASA
342
780.7773+++
T [y8]-902.5557+[1]
44285

glycoprotein
TLDTLVLK.E

P [y17]-886.0036++[2]
39557

A2GL_HUMAN

D [y6]-688.4240+[3]
19464

alpha-1B-glycoprotein
K.NGVAQEPVHLDSPAI
427
837.9441++
P [y10]-1076.6099+[1]
130137

A1BG_HUMAN
K.H

V [b3]-271.1401+[2]
110650

A [y13]-702.8777++[3]
75803

S [y5]-515.3188+[4]
63197

G [b2]-172.0717+[5]
57307

E [b6]-599.2784+[6]
49765

A [b4]-342.1772+[7]
36058

E [y11]-1205.6525+[8]
34131

P [y4]-428.2867+[9]
31158

H [y8]-880.4887+[10]
28296

D [y6]-630.3457+[11]
20534

L [y7]-743.4298+[12]
17946

alpha-1B-glycoprotein
K.HQFLLTGDTQGR.Y
697
686.8520++
Q [b2]-266.1248+[1]
1144372

A1BG_HUMAN

F [y10]-1107.5793+[2]
725830

T [y7]-734.3428+[3]
341528

L [y8]-847.4268+[4]
297048

F [b3]-413.1932+[5]
230163

G [y6]-633.2951+[6]
226694

T [y4]-461.2467+[7]
217446

L [y9]-960.5109+[8]
215574

L [b4]-526.2772+[9]
184306

L [b5]-639.3613+[10]
157607

Q [y11]-
117366

1235.6379+[11]

Q [y11]-
109274

618.3226++[12]

D [b8]-912.4574+[13]
53233

T [b6]-740.4090+[14]
49104

D [y5]-576.2736+[15]
35232

alpha-1B-glycoprotein
R.SGLSTGWTQLSK.L
698
632.8302++
G [y7]-819.4359+[1]
1138845

A1BG_HUMAN

L [b3]-258.1448+[2]
1128060

S [y9]-1007.5156+[3]
877313

S [y2]-234.1448+[4]
653032

T [y8]-920.4836+[5]
651216

T [y5]-576.3352+[6]
538856

W [y6]-762.4145+[7]
406137

L [y3]-347.2289+[8]
313255

Q [y4]-475.2875+[9]
209919

L [y10]-
103666

560.8035++[10]

W [b7]-689.3253+[11]
48587

Q [b9]-918.4316+[12]
27677

T [b8]-790.3730+[13]
26742

L [b10]-
23936

1031.5156+[14]

alpha-1B-glycoprotein
K.LLELTGPK.S
699
435.7684++
E [y6]-644.3614+[1]
6043967

A1BG_HUMAN

L [b2]-227.1754+[2]
2185138

L [y7]-757.4454+[3]
1878211

L [y5]-515.3188+[4]
923148

T [y4]-402.2347+[5]
699198

G [y3]-301.1870+[6]
666018

P [y2]-244.1656+[7]
430183

E [b3]-356.2180+[8]
244199

alpha-1B-glycoprotein
R.GVTFLLR.R
700
403.2502++
T [y5]-649.4032+[1]
4135468

A1BG_HUMAN

L [y3]-401.2871+[2]
2868709

V [b2]-157.0972+[3]
2109754

F [y4]-548.3555+[4]
1895653

R [y1]-175.1190+[5]
918856

L [y2]-288.2030+[6]
780084

T [b3]-258.1448+[7]
478494

T [y5]-325.2052++[8]
415711

F [y4]-274.6814++[9]
140533

L [b6]-631.3814+[10]
129473

alpha-1B-glycoprotein
K.ELLVPR.S
701
363.7291++
P [y2]-272.1717+[1]
9969478

A1BG_HUMAN

L [y4]-484.3242+[2]
3676023

V [y3]-371.2401+[3]
2971809

L [b2]-243.1339+[4]
809753

L [y5]-597.4083+[5]
159684

alpha-1B-glycoprotein
R.SSTSPDR.I
702
375.1748++
S [b2]-175.0713+[1]
89016

A1BG_HUMAN

R [y1]-175.1190+[2]
82740

P [y3]-387.1987+[3]
76299

T [y5]-575.2784+[4]
75253

D [b6]-575.2307+[5]
71180

S [y4]-474.2307+[6]
53784

alpha-1B-glycoprotein
R.LELHVDGPPPRPQLR
703
862.4837++
D [b6]-707.3723+[1]
49322

A1BG_HUMAN
.A

G [y9]-1017.5952+[2]
32049

G [y9]-509.3012++[3]
27715

alpha-1B-glycoprotein
R.LELHVDGPPPRPQLR
703
575.3249+++
V [y11]-616.3489++[1]
841163

A1BG_HUMAN
.A

D [y10]-566.8147++[2]
621546

E [b2]-243.1339+[3]
581025

H [y12]-684.8784++[4]
485731

R [y5]-669.4155+[5]
477653

L [y13]-741.4204++[6]
369224

H [b4]-493.2769+[7]
219485

D [b6]-707.3723+[8]
195842

V [b5]-592.3453+[9]
170689

R [y1]-175.1190+[10]
160049

L [b3]-356.2180+[11]
63902

G [b7]-764.3937+[12]
62128

P [y4]-513.3144+[13]
33888

alpha-1B-glycoprotein
R.ATWSGAVLAGR.D
704
544.7960++
S [y8]-730.4206+[1]
1933290

A1BG_HUMAN

G [y7]-643.3886+[2]
1828931

L [y4]-416.2616+[3]
869412

V [y5]-515.3300+[4]
615117

A [y3]-303.1775+[5]
584118

A [y6]-586.3671+[6]
471353

W [y9]-458.7536++[7]
466690

W [y9]-916.4999+[8]
454934

G [y2]-232.1404+[9]
338886

S [b4]-446.2034+[10]
165831

W [b3]-359.1714+[11]
139166

R [y1]-175.1190+[12]
83145

A [b6]-574.2620+[13]
65281

G [b5]-503.2249+[14]
30473

V [b7]-673.3304+[15]
30408

alpha-1B-glycoprotein
R.TPGAAANLELIFVGP
705
1148.5953++
G [y9]-999.4755+[1]
39339

A1BG_HUMAN
QHAGNYR.C

F [y11]-1245.6123+[2]
22329

V [y10]-1098.5439+[3]
14054

I [b11]-1051.5782+[4]
12281

P [y8]-942.4540+[5]
10574

alpha-1B-glycoprotein
R.TPGAAANLELIFVGP
705
766.0659+++
G [y9]-999.4755+[1]
426098

A1BG_HUMAN
QHAGNYR.C

P [y8]-942.4540+[2]
191245

V [y10]-1098.5439+[3]
183889

F [y11]-1245.6123+[4]
172790

G [b3]-256.1292+[5]
172068

A [y5]-580.2838+[6]
170557

A [b4]-327.1663+[7]
146455

H [y6]-717.3427+[8]
127934

E [b9]-825.4101+[9]
119922

G [y4]-509.2467+[10]
107378

L [b10]-938.4942+[11]
102387

A [b5]-398.2034+[12]
86428

L [b10]-
68959

469.7507++[13]

E [y14]-
67711

800.9152++[14]

I [y12]-
65740

679.8518++[15]

N [b7]-583.2835+[16]
58648

A [y17]-
55561

949.9972++[17]

G [y20]-
51555

1049.5451++[18]

I [b11]-
51489

1051.5782+[19]

L [y13]-
49190

736.3939++[20]

L [y15]-
48534

857.4572++[21]

A [y18]-
48337

985.5158++[22]

L [b8]-696.3675+[23]
47352

N [y16]-
43280

914.4787++[24]

A [b6]-469.2405+[25]
38091

Q [y7]-845.4013+[26]
32443

insulin-like growth factor-
R.SLALGTFAHTPALAS
706
737.7342+++
G [y6]-660.3424+[1]
37287

binding protein complex
LGLSNNR.L

A [b3]-272.1605+[2]
21210

acid labile subunit

S [y8]-860.4585+[3]
15266

ALS_HUMAN

S [y4]-490.2368+[4]
12497

L [y5]-603.3209+[5]
9592

insulin-like growth factor-
R.ELVLAGNR.L
707
436.2534++
A [y4]-417.2205+[1]
74710

binding protein complex

L [y5]-530.3045+[2]
71602

acid labile subunit

G [y3]-346.1833+[3]
39449

ALS_HUMAN

V [y6]-629.3729+[4]
30127

insulin-like growth factor-
R.LAYLQPALFSGLAELR
708
881.4985++
P [y11]-1173.6626+[1]
47285

binding protein complex
.E

Y [b3]-348.1918+[2]
27425

acid labile subunit

Q [b5]-589.3344+[3]
18779

ALS_HUMAN

L [b4]-461.2758+[4]
13442

insulin-like growth

588.0014+++
S [y7]-745.4203+[1]
29519

factor-binding protein

A [y4]-488.2827+[2]
23305

complex acid labile

G [y6]-658.3883+[3]
22089

subunit

F [y8]-892.4887+[4]
16888

ALS_HUMAN

Q [b5]-589.3344+[5]
15807

L [y2]-288.2030+[6]
15266

Y [b3]-348.1918+[7]
12835

L [y5]-601.3668+[8]
12024

insulin-like growth factor-
R.ELDLSR.N
709
366.6980++
S [y2]-262.1510+[1]
91447

binding protein complex

D [b3]-358.1609+[2]
85115

acid labile subunit

D [y4]-490.2620+[3]
75618

ALS_HUMAN

L [y3]-375.2350+[4]
37835

insulin-like growth factor-
K.ANVFVQLPR.L
710
522.3035++
N [b2]-186.0873+[1]
90097

binding protein complex

F [y6]-759.4512+[2]
61085

acid labile subunit

P [y2]-272.1717+[3]
46657

ALS_HUMAN

V [y5]-612.3828+[4]
43595

V [b3]-285.1557+[5]
31451

Q [y4]-513.3144+[6]
28908

V [y7]-858.5196+[7]
15725

L [y3]-385.2558+[8]
14324

Q [y4]-257.1608++[9]
13753

insulin-like growth factor-
R.NLIAAVAPGAFLGLK.A
711
727.9401++
L [b2]-228.1343+[1]
26729

binding protein complex

I [b3]-341.2183+[2]
25535

acid labile subunit

P [y8]-802.4822+[3]
25120

ALS_HUMAN

A [y9]-873.5193+[4]
17542

A [y12]-1114.6619+[5]
14895

insulin-like growth factor-
R.VAGLLEDTFPGLLGL
712
835.9774++
P [y7]-725.4668+[1]
22005

binding protein complex
R.V

L [b4]-341.2183+[2]
13753

acid labile subunit

E [y11]-1217.6525+[3]
12611

ALS_HUMAN

D [y10]-1088.6099+[4]
11003

insulin-like growth factor-
R.SFEGLGQLEVLTLDH
713
833.1026+++
Q [y4]-503.2824+[1]
328959

binding protein complex
NQLQEVK.A

T [y11]-662.8464++[2]
54479

acid labile subunit

G [b4]-421.1718+[3]
24263

ALS_HUMAN

insulin-like growth factor-
R.NLPEQVFR.G
714
501.7720++
P [y6]-775.4097+[1]
88417

binding protein complex

E [y5]-678.3570+[2]
13620

acid labile subunit

ALS_HUMAN

insulin-like growth factor-
R.IRPHTFTGLSGLR.R
715
485.6124+++
S [y4]-432.2565+[1]
82619

binding protein complex

L [y5]-545.3406+[2]
70929

acid labile subunit

T [b5]-303.1795++[3]
56677

ALS_HUMAN

insulin-like growth factor-
K.LEYLLLSR.N
716
503.8002++
Y [y6]-764.4665+[1]
67619

binding protein complex

E [b2]-243.1339+[2]
56261

acid labile subunit

L [y4]-488.3191+[3]
32890

ALS_HUMAN

L [y5]-601.4032+[4]
24224

L [y3]-375.2350+[5]
21139

insulin-like growth factor-
R.LAELPADALGPLQR.A
717
732.4145++
E [b3]-314.1710+[1]
57859

binding protein complex

P [y10]-1037.5738+[2]
45907

acid labile subunit

P [y10]-519.2905++[3]
22723

ALS_HUMAN

L [b4]-427.2551+[4]
14054

insulin-like growth factor-
R.LEALPNSLLAPLGR.L
718
732.4327++
A [b3]-314.1710+[1]
52485

binding protein complex

P [y10]-1037.6102+[2]
37028

acid labile subunit

E [b2]-243.1339+[3]
24846

ALS_HUMAN

P [y10]-519.3087++[4]
15601

P [y4]-442.2772+[5]
12327

insulin-like growth factor-
R.TFTPQPPGLER.L
719
621.8275++
P [y6]-668.3726+[1]
57877

binding protein complex

P [y8]-447.2456++[2]
50606

acid labile subunit

P [b4]-447.2238+[3]
50606

ALS_HUMAN

F [b2]-249.1234+[4]
42083

P [y8]-893.4839+[5]
34716

T [y9]-497.7694++[6]
24220

T [b3]-350.1710+[7]
22053

insulin-like growth factor-
R.DFALQNPSAVPR.F
720
657.8437++
A [b3]-334.1397+[1]
28905

binding protein complex

P [y6]-626.3620+[2]
23750

acid labile subunit

P [y2]-272.1717+[3]
20860

ALS_HUMAN

F [b2]-263.1026+[4]
17536

N [y7]-740.4050+[5]
15320

Q [y8]-868.4635+[6]
12525

beta-2-glycoprotein 1
K.FICPLTGLWPINTLK.C
721
886.9920++
C [b3]-421.1904+[1]
546451

APOH_HUMAN

C [y13]-756.9158++[2]
438858

P [y6]-685.4243+[3]
229375

I [b2]-261.1598+[4]
188092

W [y7]-871.5036+[5]
143885

G [y9]-1041.6091+[6]
143458

T [b13]-757.3972++[7]
127058

T [y10]-1142.6568+[8]
89126

T [b6]-732.3749+[9]
51907

L [b5]-631.3272+[10]
43351

L [b8]-902.4804+[11]
38788

N [y4]-475.2875+[12]
38574

W [b9]-
37148

1088.5597+[13]

T [y3]-361.2445+[14]
34153

G [b7]-789.3964+[15]
22460

P [b4]-518.2432+[16]
19893

L [y8]-984.5877+[17]
19180

beta-2-glycoprotein 1
K.FICPLTGLWPINTLK.C
721
591.6638+++
P [y6]-685.4243+[1]
541745

APOH_HUMAN

P [y6]-343.2158++[2]
234580

G [b7]-789.3964+[3]
99108

W [y7]-871.5036+[4]
89126

L [b8]-902.4804+[5]
68306

C [b3]-421.1904+[6]
58396

N [y4]-475.2875+[7]
54474

I [y5]-588.3715+[8]
54403

W [y7]-436.2554++[9]
44706

I [b2]-261.1598+[10]
40214

T [y3]-361.2445+[11]
20535

beta-2-glycoprotein 1
R.VCPFAGILENGAVR.Y
722
751.8928++
P [y12]-622.3433++[1]
431648

APOH_HUMAN

C [b2]-260.1063+[2]
223667

P [y12]-1243.6793+[3]
134827

G [y9]-928.5211+[4]
89980

L [y7]-758.4155+[5]
85773

A [y10]-999.5582+[6]
69303

A [b5]-575.2646+[7]
47913

E [y6]-645.3315+[8]
44705

N [y5]-516.2889+[9]
23244

I [y8]-871.4996+[10]
20320

G [y4]-402.2459+[11]
19180

I [b7]-745.3702+[12]
18966

F [b4]-504.2275+[13]
16399

beta-2-glycoprotein 1
R.VCPFAGILENGAVR.Y
722
501.5977+++
E [y6]-645.3315+[1]
131191

APOH_HUMAN

N [y5]-516.2889+[2]
130264

I [b7]-745.3702+[3]
112154

G [b6]-632.2861+[4]
102743

G [y4]-402.2459+[5]
82779

C [b2]-260.1063+[6]
65453

L [y7]-758.4155+[7]
54330

I [b7]-373.1887++[8]
39143

L [y7]-379.7114++[9]
29661

V [y2]-274.1874+[10]
28377

P [y12]-
28163

622.3433++[11]

beta-2-glycoprotein 1
K.CTEEGK.W
723
362.1525++
E [y3]-333.1769+[1]
59464

APOH_HUMAN

E [b3]-391.1282+[2]
21675

beta-2-glycoprotein 1
K.WSPELPVCAPIICPPP
724
940.4923+++
P [y12]-648.8692++[1]
294510

APOH_HUMAN
SIPTFATLR.V

P [y11]-600.3428++[2]
206026

P [y7]-805.4567+[3]
122891

P [y10]-1102.6255+[4]
75113

L [b5]-613.2980+[5]
74578

P [y11]-1199.6783+[6]
72855

A [b9]-1040.4870+[7]
28643

T [y3]-195.1290++[8]
28524

S [b2]-274.1186+[9]
23770

P [y10]-
22284

551.8164++[10]

C [y13]-
20918

728.8845++[11]

E [b4]-500.2140+[12]
17114

beta-2-glycoprotein 1
K.ATFGCHDGYSLDGP
725
796.0036+++
P [y8]-503.2315++[1]
67031

APOH_HUMAN
EEIECTK.L

E [y4]-537.2337+[2]
59841

C [b5]-537.2126+[3]
56454

I [y5]-650.3178+[4]
55384

C [y3]-408.1911+[5]
46946

E [y6]-779.3604+[6]
45282

T [b2]-173.0921+[7]
37675

G [y9]-1062.4772+[8]
36843

C [y17]-
35774

1005.4144++[9]

P [y8]-1005.4557+[10]
33991

D [y10]-
30366

1177.5041+[11]

E [y7]-908.4030+[12]
26503

T [y2]-248.1605+[13]
24840

Y [b9]-1009.3832+[14]
19491

G [y9]-531.7422++[15]
17946

S [b10]-
17352

1096.4153+[16]

beta-2-glycoprotein 1
K.ATVVYQGER.V
726
511.7669++
Y [y5]-652.3049+[1]
762897

APOH_HUMAN

V [y6]-751.3733+[2]
548908

T [b2]-173.0921+[3]
252556

V [y7]-850.4417+[4]
231995

V [b3]-272.1605+[5]
223140

Q [y4]-489.2416+[6]
165023

G [y3]-361.1830+[7]
135013

V [b4]-371.2289+[8]
86760

V [y7]-425.7245++[9]
54314

beta-2-glycoprotein 1
K.VSFFCK.N
727
394.1940++
S [y5]-688.3123+[1]
384559

APOH_HUMAN

F [y4]-601.2803+[2]
321951

C [y2]-307.1435+[3]
265521

S [b2]-187.1077+[4]
237662

F [y3]-454.2119+[5]
168104

beta-2-glycoprotein 1
K.CSYTEDAQCIDGTIE
728
1043.4588++
P [y2]-244.1656+[1]
34574

APOH_HUMAN
VPK.C

V [y3]-343.2340+[2]
9173

E [y4]-472.2766+[3]
7291

Y [b3]-411.1333+[4]
6233

beta-2-glycoprotein 1
K.CSYTEDAQCIDGTIE
728
695.9750+++
D [b11]-672.2476++[1]
37044

APOH_HUMAN
VPK.C

D [y8]-858.4567+[2]
18816

D [b6]-756.2505+[3]
12289

V [y3]-343.2340+[4]
11348

A [b7]-414.1474++[5]
9761

G [y7]-743.4298+[6]
8644

beta-2-glycoprotein 1
K.EHSSLAFWK.T
729
552.7773++
H [b2]-267.1088+[1]
237907

APOH_HUMAN

S [y7]-838.4458+[2]
200568

W [y2]-333.1921+[3]
101078

S [y6]-751.4137+[4]
54920

A [y4]-551.2976+[5]
52920

F [y3]-480.2605+[6]
40102

L [y5]-664.3817+[7]
30341

F [b7]-772.3624+[8]
27871

S [b3]-354.1408+[9]
27754

A [b6]-625.2940+[10]
25931

beta-2-glycoprotein 1
K.TDASDVKPC.-
730
496.7213++
D [b2]-217.0819+[1]
323810

APOH_HUMAN

P [y2]-276.1013+[2]
119128

A [y7]-776.3607+[3]
86083

S [y6]-705.3236+[4]
79262

A [b3]-288.1190+[5]
77498

D [y5]-618.2916+[6]
70501

K [y3]-404.1962+[7]
55801

V [y4]-503.2646+[8]
46217

transforming growth
K.SPYQLVLQHSR.L
731
443.2421+++
Y [y9]-572.3171++[1]
560916

factor-beta-induced

P [b2]-185.0921+[2]
413241

protein ig-h3

H [y3]-399.2099+[3]
320572

BGH3_HUMAN

L [y5]-640.3525+[4]
313309

Q [y4]-527.2685+[5]
244398

L [y7]-426.7561++[6]
215854

V [y6]-739.4209+[7]
172897

L [y7]-852.5050+[8]
164959

Q [y8]-490.7854++[9]
149814

L [y5]-320.6799++[10]
127463

L [b5]-589.2980+[11]
118061

S [y2]-262.1510+[12]
110123

V [y6]-370.2141++[13]
97399

P [y10]-
94640

620.8435++[14]

V [b6]-688.3665+[15]
87772

Q [b4]-476.2140+[16]
74203

Y [b3]-348.1554+[17]
65984

H [y3]-200.1086++[18]
55624

Q [y4]-264.1379++[19]
41606

L [b7]-801.4505+[20]
18241

V [b6]-344.6869++[21]
17678

L [b7]-401.2289++[22]
14976

transforming growth
R.VLTDELK.H
732
409.2369++
T [y5]-
937957

factor-beta-induced

605.3141+[1]

protein ig-h3

L [b2]-
298671

BGH3_HUMAN

213.1598+[2]

L [y6]-
244116

718.3981+[3]

L [y2]-
135739

260.1969+[4]

D [y4]-
52472

504.2664+[5]

E [y3]-
50839

389.2395+[6]

transforming growth
K.VISTITNNIQQIIEIED
733
897.4798+++
E [y8]-1010.4789+[1]
282865

factor-beta-induced
TFETLR.A

D [y7]-881.4363+[2]
237234

protein ig-h3

I [y9]-1123.5630+[3]
195581

BGH3_HUMAN

T [y6]-766.4094+[4]
186875

I [b2]-213.1598+[5]
174492

T [y3]-389.2507+[6]
145598

F [y5]-665.3617+[7]
143872

E [y4]-518.2933+[8]
108148

Q [b11]-
106647

606.8328++[9]

I [b5]-514.3235+[10]
82030

N [b8]-843.4571+[11]
75125

T [b4]-401.2395+[12]
71448

I [b12]-
58314

663.3748++[13]

N [b7]-365.2107++[14]
54862

I [b9]-956.5411+[15]
51034

L [y2]-288.2030+[16]
50734

S [b3]-300.1918+[17]
48708

Q [b10]-
43754

542.8035++[18]

Q [b11]-
37375

1212.6583+[19]

T [b6]-615.3712+[20]
33322

I [b9]-478.7742++[21]
29570

Q [b10]-
25817

1084.5997+[22]

T [y6]-383.7083++[23]
17187

N [b8]-422.2322++[24]
17111

I [b13]-
16661

719.9168++[25]

transforming growth
K.IPSETLNR.I
734
465.2562++
S [y6]-719.3682+[1]
326570

factor-beta-induced

P [y7]-816.4210+[2]
168951

protein ig-h3

E [y5]-632.3362+[3]
102452

BGH3_HUMAN

P [b2]-211.1441+[4]
85885

T [y4]-503.2936+[5]
67650

L [y3]-402.2459+[6]
20939

N [y2]-289.1619+[7]
13979

transforming growth
R.ILGDPEALR.D
735
492.2796++
P [y5]-585.3355+[1]
1431619

factor-beta-induced

G [y7]-757.3839+[2]
1066060

protein ig-h3

L [b2]-227.1754+[3]
742225

BGH3_HUMAN

L [y8]-870.4680+[4]
254257

D [b4]-399.2238+[5]
159932

G [b3]-284.1969+[6]
66816

D [y6]-700.3624+[7]
65780

A [y3]-359.2401+[8]
62730

E [y4]-488.2827+[9]
23711

L [y2]-288.2030+[10]
16344

transforming growth
R.DLLNNHILK.S
736
360.5451+++
L [y7]-426.2585++[1]
1488651

factor-beta-induced

L [b2]-229.1183+[2]
591961

protein ig-h3

N [y6]-369.7165++[3]
366710

BGH3_HUMAN

N [y5]-624.3828+[4]
103993

L [y2]-260.1969+[5]
75103

N [b4]-228.6263++[6]
66125

N [y6]-738.4257+[7]
49493

H [y4]-510.3398+[8]
43681

N [y5]-312.6950++[9]
41551

I [y3]-373.2809+[10]
40285

L [b3]-342.2023+[11]
33494

L [y8]-482.8006++[12]
33034

transforming growth
K.AIISNK.D
737
323.2001++
I [y4]-461.2718+[1]
99850

factor-beta-induced

I [b2]-185.1285+[2]
43105

protein ig-h3

S [y3]-348.1878+[3]
39192

BGH3_HUMAN

N [y2]-261.1557+[4]
24516

transforming growth
K.DILATNGVIHYIDELLI
738
804.1003+++
P [y5]-517.2617+[1]
400251

factor-beta-induced
PDSAK.T

I [b2]-229.1183+[2]
306709

protein ig-h3

L [b3]-342.2023+[3]
147923

BGH3_HUMAN

I [y6]-630.3457+[4]
91265

S [y3]-305.1819+[5]
61472

L [y7]-743.4298+[6]
57894

A [b4]-413.2395+[7]
52430

H [y13]-757.3985++[8]
30183

G [y16]-891.9855++[9]
27711

D [y10]-
24979

1100.5834+[10]

A [y19]-
23223

1035.0493++[11]

L [y8]-856.5138+[12]
22507

L [y20]-
16783

1091.5913++[13]

transforming growth
K.TLFELAAESDVSTAID
739
1049.5388++
D [y4]-550.2984+[1]
64464

factor-beta-induced
LFR.Q

S [y8]-922.4993+[2]
47291

protein ig-h3

S [y11]-1223.6266+[3]
44234

BGH3_HUMAN

A [b6]-675.3712+[4]
35972

L [b5]-604.3341+[5]
34997

A [b7]-746.4083+[6]
33045

E [b4]-491.2500+[7]
31744

D [y10]-1136.5946+[8]
30183

E [b8]-875.4509+[9]
26475

F [y2]-322.1874+[10]
25044

T [y7]-835.4672+[11]
21596

I [y5]-663.3824+[12]
21011

L [y3]-435.2714+[13]
20295

L [b2]-215.1390+[14]
20295

V [y9]-1021.5677+[15]
18929

A [y6]-734.4196+[16]
17694

F [b3]-362.2074+[17]
14441

transforming growth
R.QAGLGNHLSGSER.L
740
442.5567+++
G [y9]-478.7309++[1]
180677

factor-beta-induced

L [y10]-535.2729++[2]
147807

protein ig-h3

S [y5]-535.2471+[3]
129825

BGH3_HUMAN

G [y11]-563.7836++[4]
84584

L [y6]-648.3311+[5]
51642

A [b2]-200.1030+[6]
26469

G [y4]-448.2150+[7]
26397

H [y7]-393.1987++[8]
25390

A [y12]-599.3022++[9]
21434

N [y8]-450.2201++[10]
19276

transforming growth
R.LTLLAPLNSVFK.D
741
658.4028++
P [y7]-804.4614+[1]
1635673

factor-beta-induced

A [y8]-875.4985+[2]
869779

protein ig-h3

L [b3]-328.2231+[3]
516429

BGH3_HUMAN

T [b2]-215.1390+[4]
415472

L [y9]-988.5826+[5]
334225

L [b4]-441.3071+[6]
209200

L [y10]-1101.6667+[7]
174268

A [b5]-512.3443+[8]
160217

A [y8]-438.2529++[9]
83264

N [y5]-594.3246+[10]
54512

F [y2]-294.1812+[11]
51649

L [y9]-494.7949++[12]
34541

L [y6]-707.4087+[13]
34086

S [y4]-480.2817+[14]
30053

T [y11]-
16653

1202.7143+[15]

transforming growth
K.DGTPPIDAHTR.N
742
393.8633+++
P [y8]-453.7432++[1]
355240

factor-beta-induced

P [y7]-405.2169++[2]
88181

protein ig-h3

T [b3]-274.1034+[3]
81204

BGH3_HUMAN

G [b2]-173.0557+[4]
40062

D [y5]-599.2896+[5]
37689

A [y4]-242.6350++[6]
29633

P [y7]-809.4264+[7]
22153

I [y6]-712.3737+[8]
16327

transforming growth
K.YLYHGQTLETLGGK.K
743
527.2753+++
E [y6]-604.3301+[1]
483222

factor-beta-induced

Y [y12]-652.3357++[2]
264640

protein ig-h3

T [y5]-475.2875+[3]
239600

BGH3_HUMAN

G [y3]-261.1557+[4]
206272

L [b2]-277.1547+[5]
134992

L [y13]-708.8777++[6]
119379

T [b7]-863.4046+[7]
104307

L [y4]-374.2398+[8]
100344

H [y11]-570.8040++[9]
93318

L [y7]-717.4141+[10]
91276

G [b13]-
80707

717.3566++[11]

T [y8]-818.4618+[12]
57888

Q [b6]-762.3570+[13]
54766

G [y10]-
51523

1003.5419+[14]

T [b7]-432.2060++[15]
49121

G [y2]-204.1343+[16]
45518

T [y8]-409.7345++[17]
44437

L [y7]-359.2107++[18]
33028

T [b10]-
26902

603.7931++[19]

G [b5]-634.2984+[20]
21858

Q [b6]-
17595

381.6821++[21]

H [b4]-577.2769+[22]
16093

L [b8]-488.7480++[23]
15133

T [y5]-238.1474++[24]
15013

E [b9]-553.2693++[25]
12370

transforming growth
R.EGVYTVFAPTNEAFR
744
850.9176++
P [y7]-834.4104+[1]
364143

factor-beta-induced
.A

F [y9]-1052.5160+[2]
269144

protein ig-h3

A [y8]-905.4476+[3]
176007

BGH3_HUMAN

V [b3]-286.1397+[4]
107490

V [y10]-1151.5844+[5]
74822

T [b5]-550.2508+[6]
47560

V [b6]-649.3192+[7]
45398

G [b2]-187.0713+[8]
43056

Y [b4]-449.2031+[9]
33148

F [b7]-796.3876+[10]
24440

A [b8]-867.4247+[11]
24020

E [y4]-522.2671+[12]
17174

A [y3]-393.2245+[13]
14712

F [y2]-322.1874+[14]
12611

transforming growth
R.LLGDAK.E
745
308.6869++
A [y2]-218.1499+[1]
206606

factor-beta-induced

G [y4]-390.1983+[2]
204445

protein ig-h3

L [y5]-503.2824+[3]
117829

BGH3_HUMAN

L [b2]-227.1754+[4]
43998

transforming growth
K.ELANILK.Y
746
400.7475++
A [y5]-558.3610+[1]
963502

factor-beta-induced

L [y2]-260.1969+[2]
583986

protein ig-h3

N [y4]-487.3239+[3]
326252

BGH3_HUMAN

I [y3]-373.2809+[4]
302352

I [b5]-541.2980+[5]
179670

L [b2]-243.1339+[6]
74642

L [y6]-671.4450+[7]
38792

N [b4]-428.2140+[8]
14952

transforming growth
K.YHIGDEILVSGGIGAL
747
935.0151++
H [b2]-301.1295+[1]
24601

factor-beta-induced
VR.L

S [y9]-829.4890+[2]
15456

protein ig-h3

BGH3_HUMAN

transforming growth
K.YHIGDEILVSGGIGAL
747
623.6791+++
S [y9]-829.4890+[1]
917445

factor-beta-induced
VR.L

G [y5]-515.3300+[2]
654048

protein ig-h3

I [b7]-828.3886+[3]
553713

BGH3_HUMAN

G [y8]-742.4570+[4]
467481

L [b8]-941.4727+[5]
322194

G [y7]-685.4355+[6]
228428

E [b6]-715.3046+[7]
199383

V [y10]-928.5574+[8]
141616

G [b4]-471.2350+[9]
126224

L [b8]-471.2400++[10]
117080

H [b2]-301.1295+[11]
107162

I [y6]-628.4141+[12]
105488

A [y4]-458.3085+[13]
103491

L [y3]-387.2714+[14]
73094

I [b3]-414.2136+[15]
72515

S [y9]-415.2482++[16]
65044

V [b9]-1040.5411+[17]
61760

V [y2]-274.1874+[19]
56093

I [b7]-414.6980++[18]
56093

V [b9]-520.7742++[20]
39413

L [y11]-
38962

1041.6415+[21]

D [b5]-586.2620+[22]
36257

S [b10]-
32329

564.2902++[23]

I [y6]-314.7107++[24]
30526

A [b15]-
27692

741.8830++[25]

V [y10]-
26340

464.7824++[26]

L [y11]-
20415

521.3244++[27]

G [b12]-
18612

621.3117++[28]

G [b12]-
13073

1241.6161+[29]

transforming growth
K.LEVSLK.N
748
344.7156++
V [y4]-446.2973+[1]
120860

factor-beta-induced

E [y5]-575.3399+[2]
82786

protein ig-h3

E [b2]-243.1339+[3]
76794

BGH3_HUMAN

S [y3]-347.2289+[4]
36335

L [y2]-260.1969+[5]
24932

transforming growth
K.NNVVSVNK.E
749
437.2431++
V [y5]-546.3246+[1]
17073

factor-beta-induced

N [b2]-229.0931+[2]
14045

protein ig-h3

BGH3_HUMAN

transforming growth
R.GDELADSALEIFK.Q
750
704.3537++
E [b3]-302.0983+[1]
687754

factor-beta-induced

A [y9]-993.5251+[2]
431716

protein ig-h3

D [y8]-922.4880+[3]
368670

BGH3_HUMAN

D [b2]-173.0557+[4]
358545

F [y2]-294.1812+[5]
200930

L [b4]-415.1823+[6]
197364

S [y7]-807.4611+[7]
187412

I [y3]-407.2653+[8]
129601

A [b5]-486.2195+[9]
121605

E [y4]-536.3079+[10]
108432

A [y6]-720.4291+[11]
107627

L [y5]-649.3919+[12]
95662

L [y10]-
79325

1106.6092+[13]

D [b6]-601.2464+[14]
42625

A [b8]-759.3155+[15]
28647

S [b7]-688.2784+[16]
20709

transforming growth
K.QASAFSR.A
751
383.6958++
F [y3]-409.2194+[1]
64604

factor-beta-induced

S [y5]-567.2885+[2]
60496

protein ig-h3

S [y2]-262.1510+[3]
42825

BGH3_HUMAN

A [y4]-480.2565+[4]
25211

transforming growth
R.LAPVYQK.L
752
409.7422++
P [y5]-634.3559+[1]
416225

factor-beta-induced

Y [y3]-438.2347+[2]
171715

protein ig-h3

V [y4]-537.3031+[3]
98187

BGH3_HUMAN

Q [y2]-275.1714+[4]
42056

A [y6]-705.3930+[5]
32429

ceruloplasmin
K.LISVDTEHSNIYLQNG
753
724.3624+++
I [b2]-227.1754+[1]
168111

CERU_HUMAN
PDR.I

N [y5]-558.2630+[2]
87133

G [y4]-444.2201+[3]
86682

L [y7]-799.4057+[4]
84956

Q [y6]-686.3216+[5]
79928

Y [y8]-962.4690+[6]
64167

S [b3]-314.2074+[7]
39476

N [y10]-1189.5960+[8]
24691

P [y3]-387.1987+[9]
22065

I [y18]-
20714

1029.4980++[10]

N [b10]-
18087

1096.5269+[11]

I [y9]-1075.5531+[12]
15460

ceruloplasmin
K.ALYLQYTDETFR.T
754
760.3750++
Y [b3]-348.1918+[1]
681082

CERU_HUMAN

Y [y7]-931.4156+[2]
405797

Q [y8]-1059.4742+[3]
343430

T [y6]-768.3523+[4]
279638

L [b2]-185.1285+[5]
229654

L [y9]-1172.5582+[6]
164660

L [b4]-461.2758+[7]
142145

D [y5]-667.3046+[8]
107547

Y [y10]-668.3144++[9]
91862

E [y4]-552.2776+[10]
76852

Q [b5]-589.3344+[11]
75200

T [y3]-423.2350+[12]
64168

F [y2]-322.1874+[13]
47807

Y [b6]-752.3978+[14]
40377

L [y9]-586.7828++[15]
40227

ceruloplasmin
R.TTIEKPVWLGFLGPII
755
956.5690++
E [b4]-445.2293+[1]
92012

CERU_HUMAN
K.A

K [b5]-573.3243+[2]
45856

L [y9]-957.6132+[3]
32272

G [y8]-844.5291+[4]
29044

K [y13]-734.4579++[5]
26118

G [y5]-527.3552+[6]
24917

L [y6]-640.4392+[7]
19738

I [b3]-316.1867+[8]
18838

P [y4]-470.3337+[9]
18012

W [y10]-
17412

1143.6925+[10]

I [y15]-
14785

855.5213++[11]

V [b7]-769.4454+[12]
14710

ceruloplasmin
R.TTIEKPVWLGFLGPII
755
638.0484+++
G [y8]-844.5291+[1]
1645779

CERU_HUMAN
K.A

G [y5]-527.3552+[2]
1180842

L [y6]-640.4392+[3]
920117

T [b2]-203.1026+[4]
775570

F [y7]-787.5076+[5]
416229

P [y4]-470.3337+[6]
285341

W [b8]-955.5247+[7]
275960

I [y2]-260.1969+[8]
256597

V [b7]-769.4454+[9]
230104

E [b4]-445.2293+[10]
117754

W [b8]-
105521

478.2660++[11]

P [y12]-
104020

670.4105++[13]

P [b6]-670.3770+[12]
104020

G [b10]-
93363

1125.6303+[14]

F [y7]-394.2575++[15]
76176

K [b5]-573.3243+[16]
63718

I [b3]-316.1867+[17]
52986

L [b9]-1068.6088+[18]
33548

I [y3]-373.2809+[19]
20864

ceruloplasmin
K.VYVHLK.N
756
379.7316++
V [y4]-496.3242+[1]
228979

CERU_HUMAN

Y [y5]-659.3875+[2]
196857

H [y3]-397.2558+[3]
89610

Y [b2]-263.1390+[4]
88034

L [y2]-260.1969+[5]
85482

Y [y5]-330.1974++[6]
31821

ceruloplasmin
R.IYHSHIDAPK.D
757
590.8091++
H [y8]-452.7354++[1]
167209

CERU_HUMAN

P [y2]-244.1656+[2]
84831

A [y3]-315.2027+[3]
78036

S [y7]-767.4046+[4]
75864

H [b3]-414.2136+[5]
67808

Y [y9]-534.2671++[6]
50296

H [y8]-904.4635+[7]
42801

D [b7]-866.4155+[8]
28721

H [y6]-680.3726+[9]
23817

A [b8]-937.4526+[10]
19964

D [y4]-430.2296+[11]
17653

Y [b2]-277.1547+[12]
16742

ceruloplasmin
R.IYHSHIDAPK.D
757
394.2085+++
H [y8]-452.7354++[1]
402227

CERU_HUMAN

Y [y9]-534.2671++[2]
305348

P [y2]-244.1656+[5]
101993

A [y3]-315.2027+[3]
97580

Y [b2]-277.1547+[4]
93377

D [y4]-430.2296+[6]
89734

S [y7]-767.4046+[7]
88263

S [y7]-384.2060++[8]
60663

I [y5]-543.3137+[9]
44692

H [y6]-680.3726+[11]
38528

A [b8]-469.2300++[10]
37547

H [b5]-638.3045+[12]
36146

H [b3]-414.2136+[13]
23467

ceruloplasmin
R.HYYIAAEEIIWNYAPS
758
905.4549+++
P [y9]-977.5302+[1]
253794

CERU_HUMAN
GIDIFTK.E

E [b8]-977.4363+[2]
233479

Y [b2]-301.1295+[3]
128823

I [b9]-1090.5204+[4]
103955

A [y10]-1048.5673+[5]
78247

P [y9]-489.2687++[6]
76005

E [b8]-489.2218++[7]
76005

I [b10]-1203.6045+[8]
56671

F [y3]-395.2289+[9]
49456

Y [b3]-464.1928+[10]
46864

E [b7]-848.3937+[11]
44622

A [b5]-648.3140+[12]
42451

A [b6]-719.3511+[13]
40629

I [b4]-577.2769+[14]
39999

D [y5]-623.3399+[15]
29631

I [y4]-508.3130+[16]
28581

T [y2]-248.1605+[17]
27040

I [b10]-
24448

602.3059++[18]

Y [y11]-
24238

1211.6307+[19]

G [y7]-793.4454+[20]
21926

W [b11]-
18704

695.3455++[21]

S [y8]-880.4775+[22]
18633

ceruloplasmin
R.IGGSYK.K
759
312.6712++
G [y5]-511.2511+[1]
592392

CERU_HUMAN

G [y4]-454.2296+[2]
89266

G [b2]-171.1128+[3]
71261

Y [y2]-310.1761+[4]
52498

S [y3]-397.2082+[5]
22364

ceruloplasmin
R.EYTDASFTNR.K
760
602.2675++
S [y5]-624.3100+[1]
163623

CERU_HUMAN

F [y4]-537.2780+[2]
83580

T [y8]-911.4217+[3]
83391

A [y6]-695.3471+[4]
82886

D [y7]-810.3741+[5]
76315

T [y3]-390.2096+[6]
66018

Y [b2]-293.1132+[7]
50224

N [y2]-289.1619+[8]
29376

ceruloplasmin
R.GPEEEHLGILGPVIW
761
829.7675+++
A [y8]-860.4472+[1]
259776

CERU_HUMAN
AEVGDTIR.V

W [y9]-1046.5265+[2]
210032

E [y7]-789.4101+[3]
201448

G [y5]-561.2991+[4]
189809

V [y6]-660.3675+[5]
121142

T [y3]-389.2507+[6]
80306

P [b2]-155.0815+[7]
65806

V [b13]-664.8459++[8]
65676

G [b11]-1132.5633+[9]
64765

I [y10]-
58783

1159.6106+[10]

L [b10]-
56702

1075.5419+[11]

I [b9]-962.4578+[12]
54101

L [b7]-792.3523+[13]
48509

P [b12]-
37715

615.3117++[14]

D [y4]-504.2776+[15]
34528

G [b8]-849.3737+[16]
34008

I [b14]-
23669

721.3879++[17]

H [b6]-679.2682+[18]
22174

W [b15]-
21979

814.4276++[19]

E [b3]-284.1241+[20]
18272

G [b11]-
17882

566.7853++[21]

A [b16]-
15476

849.9461++[22]

ceruloplasmin
R.VTFHNK.G
762
373.2032++
T [y5]-646.3307+[1]
178952

CERU_HUMAN

F [y4]-545.2831+[2]
175829

T [b2]-201.1234+[3]
127758

N [y2]-261.1557+[4]
107852

H [y3]-398.2146+[5]
103754

ceruloplasmin
K.GAYPLSIEPIGVR.F
763
686.3852++
S [y8]-870.5043+[1]
970541

CERU_HUMAN

P [y5]-541.3457+[2]
966508

P [y10]-1080.6412+[3]
590391

E [y6]-670.3883+[4]
493076

I [y7]-783.4723+[5]
391013

Y [b3]-292.1292+[6]
265598

L [y9]-983.5884+[7]
217591

P [b4]-389.1819+[8]
188839

S [b6]-589.2980+[9]
95623

G [y3]-331.2088+[10]
85605

L [b5]-502.2660+[11]
76628

V [y2]-274.1874+[12]
52365

I [b7]-702.3821+[13]
39225

E [b8]-831.4247+[14]
26866

ceruloplasmin
K.NNEGTYYSPNYNPQ
764
952.4139++
P [y4]-487.2623+[1]
37339

CERU_HUMAN
SR.S

S [y9]-1062.4963+[2]
33696

P [y8]-975.4643+[3]
29467

N [y5]-601.3052+[4]
24068

N [b2]-229.0931+[5]
19060

Y [y10]-1225.5596+[6]
16718

E [b3]-358.1357+[7]
16523

ceruloplasmin
R.SVPPSASHVAPTETF
765
844.4199+++
P [y2]-244.1656+[1]
579331

CERU_HUMAN
TYEWTVPK.E

T [y8]-1023.5146+[2]
126817

W [y5]-630.3610+[3]
101524

V [y3]-343.2340+[4]
99970

Y [y7]-922.4669+[5]
95448

E [y6]-759.4036+[6]
88030

T [y4]-444.2817+[7]
55884

F [y9]-1170.5830+[8]
55743

V [b2]-187.1077+[9]
46982

P [y20]-
37303

1124.5497++[10]

P [b3]-284.1605+[11]
21690

E [b18]-
18652

951.4494++[12]

P [b4]-381.2132+[13]
16956

T [b14]-
15543

681.3384++[14]

ceruloplasmin
K.GSLHANGR.Q
766
271.1438+++
L [y6]-334.1854++[1]
154779

CERU_HUMAN

A [y4]-417.2205+[2]
41628

S [y7]-377.7014++[3]
35762

H [y5]-277.6433++[4]
29542

ceruloplasmin
R.QSEDSTFYLGER.T
767
716.3230++
G [y3]-361.1830+[1]
157040

CERU_HUMAN

Y [y5]-637.3304+[2]
126155

F [y6]-784.3988+[3]
97814

L [y4]-474.2671+[4]
80146

T [y7]-443.2269++[5]
70746

T [y7]-885.4465+[6]
54844

S [y8]-972.4785+[7]
44101

S [b2]-216.0979+[8]
42193

D [y9]-1087.5055+[9]
36186

E [y10]-
35055

1216.5481+[10]

E [b3]-345.1405+[11]
20778

E [y2]-304.1615+[12]
19153

ceruloplasmin
R.TYYIAAVEVEWDYSP
768
1045.4969++
P [y3]-400.2303+[1]
64887

CERU_HUMAN
QR.E

Y [b3]-428.1816+[2]
49716

S [y4]-487.2623+[3]
37369

Y [b2]-265.1183+[4]
35596

E [y8]-1080.4745+[5]
28569

W [y7]-951.4319+[6]
26204

V [b7]-782.4083+[7]
23577

A [b6]-683.3399+[8]
23512

V [y9]-1179.5429+[10]
22526

D [y6]-765.3526+[9]
22526

Y [y5]-650.3257+[11]
19965

A [b5]-612.3028+[12]
18520

ceruloplasmin
K.ELHHLQEQNVSNAF
769
674.6728+++
N [y6]-707.3723+[1]
22715

CERU_HUMAN
LDK.G

L [y3]-188.1155++[2]
21336

S [y7]-794.4043+[3]
10176

ceruloplasmin
K.GEFYIGSK.Y
770
450.7267++
E [b2]-187.0713+[1]
53262

CERU_HUMAN

F [y6]-714.3821+[2]
50438

I [y4]-404.2504+[3]
39602

Y [y5]-567.3137+[4]
34020

G [y3]-291.1663+[5]
33100

ceruloplasmin
R.QYTDSTFR.V
771
509.2354++
T [y6]-726.3417+[1]
164056

CERU_HUMAN

S [y4]-510.2671+[2]
155584

D [y5]-625.2940+[3]
136472

T [y3]-423.2350+[4]
54313

F [y2]-322.1874+[5]
47220

Y [b2]-292.1292+[6]
27846

Y [y7]-889.4050+[7]
16550

ceruloplasmin
K.AEEEHLGILGPQLHA
772
710.0272+++
E [b2]-201.0870+[1]
60743

CERU_HUMAN
DVGDK.V

V [y4]-418.2296+[2]
23296

E [y17]-899.9759++[3]
14619

ceruloplasmin
K.LEFALLFLVFDENES
773
945.1372+++
L [y6]-359.1925++[1]
19544

CERU_HUMAN
WYLDDNIK.T

L [b5]-574.3235+[2]
17902

ceruloplasmin
K.TYSDHPEK.V
774
488.7222++
S [y6]-712.3260+[1]
93810

CERU_HUMAN

P [y3]-373.2082+[2]
43778

Y [b2]-265.1183+[3]
35960

H [y4]-510.2671+[4]
16651

ceruloplasmin
K.TYSDHPEK.V
774
326.1505+++
S [y6]-356.6667++[1]
539251

CERU_HUMAN

Y [y7]-438.1983++[2]
180506

Y [b2]-265.1183+[3]
109445

P [y3]-373.2082+[4]
84742

H [y4]-255.6372++[5]
27596

P [y3]-187.1077++[6]
25016

D [y5]-625.2940+[7]
24000

H [y4]-510.2671+[8]
20795

hepatocyte growth factor
R.YEYLEGGDR.W
775
551.2460++
E [b2]-293.1132+[1]
229354

activator

Y [y7]-809.3788+[2]
204587

HGFA_HUMAN

L [y6]-646.3155+[3]
96740

Y [b3]-456.1765+[4]
54186

E [y8]-938.4214+[5]
22065

hepatocyte growth factor
R.VQLSPDLLATLPEPA
776
981.0387++
P [y8]-810.4104+[1]
51109

activator
SPGR.Q

Q [b2]-228.1343+[2]
19063

HGFA_HUMAN

hepatocyte growth factor
R.TTDVTQTFGIEK.Y
777
670.3406++
D [b3]-318.1296+[1]
104844

activator

T [y8]-923.4833+[2]
93287

HGFA_HUMAN

T [b2]-203.1026+[3]
72498

D [y10]-1137.5786+[4]
53886

I [y3]-389.2395+[5]
53811

Q [y7]-822.4356+[6]
42253

V [b4]-417.1980+[7]
38726

T [y6]-694.3770+[8]
36474

F [y5]-593.3293+[9]
26793

E [y2]-276.1554+[10]
24616

G [y4]-446.2609+[11]
22215

V [y9]-1022.5517+[12]
20564

hepatocyte growth factor
R.EALVPLVADHK.C
778
596.3402++
P [y7]-779.4410+[1]
57992

activator

L [b3]-314.1710+[2]
42740

HGFA_HUMAN

hepatocyte growth factor
R.EALVPLVADHK.C
778
397.8959+++
P [y7]-390.2241++[1]
502380

activator

V [y5]-569.3042+[2]
108586

HGFA_HUMAN

V [y8]-439.7584++[3]
100001

H [y2]-284.1717+[4]
71234

L [y9]-496.3004++[5]
65572

A [y4]-470.2358+[6]
62284

hepatocyte growth factor
R.LHKPGVYTR.V
779
357.5417+++
P [y6]-692.3726+[1]
104812

activator

H [y8]-479.2669++[2]
49302

HGFA_HUMAN

K [y7]-410.7374++[3]
30859

Y [y3]-439.2300+[4]
23829

hepatocyte growth factor
R.VANYVDWINDR.I
780
682.8333++
D [y6]-818.3791+[1]
132314

activator

V [y7]-917.4476+[2]
81805

HGFA_HUMAN

N [b3]-285.1557+[3]
70622

W [y5]-703.3522+[4]
53586

N [y3]-404.1888+[5]
37675

A [b2]-171.1128+[6]
36474

alpha-1-antichymotrypsin
R.GTHVDLGLASANVD
583
1113.0655++
L [b6]-
244118

AACT_HUMAN
FAFSLYK.Q

623.3148+[1]

L [b8]-
211429

793.4203+[2]

H [b3]-
204581

296.1353+[3]

D [b5]-
200032

510.2307+[4]

S [y4]-
195904

510.2922+[5]

V [b4]-
187415

395.2037+[6]

A [b9]-
167905

864.4574+[7]

G [b7]-
87564

680.3362+[8]

Y [y2]-
74385

310.1761+[9]

F [y7]-
50794

875.4662+[10]

F [y5]-
44462

657.3606+[11]

S [b10]-
43899

951.4894+[12]

D [y8]-
39866

990.4931+[13]

A [y6]-
33300

728.3978+[14]

A [b11]-
32502

1022.5265+[15]

L [y3]-
29829

423.2602+[16]

V [y9]-
22043

1089.5615+[17]

N [b12]-
17353

1136.5695+[18]

alpha-1-antichymotrypsin
R.GTHVDLGLASANVD
583
742.3794+++
D [y8]-
830612

AACT_HUMAN
FAFSLYK.Q

990.4931+[1]

L [b8]-
635646

793.4203+[2]

G [b7]-
582273

680.3362+[3]

S [y4]-
548645

510.2922+[4]

D [b5]-
471071

510.2307+[5]

F [y7]-
420278

875.4662+[6]

A [b9]-
411366

864.4574+[7]

A [y6]-
391668

728.3978+[8]

Y [y2]-
390214

310.1761+[9]

F [y5]-
358134

657.3606+[10]

T [b2]-
288721

159.0764+[11]

H [b3]-
251998

296.1353+[12]

L [b6]-
240742

623.3148+[13]

V [y9]-
197218

1089.5615+[14]

V [b4]-
186055

395.2037+[15]

L [y3]-
173673

423.2602+[16]

S [b10]-
103651

951.4894+[17]

N [b12]-
97976

1136.5695+[18]

A [b11]-
76448

1022.5265+[19]

alpha-1-antichymotrypsin
K.FNLTETSEAEIHQSFQ
781
800.7363+++
A [b9]-
75792

AACT_HUMAN
HLLR.T

993.4524+[1]

L [b3]-
59001

375.2027+[2]

H [y9]-
57829

1165.6225+[3]

L [y2]-
55343

288.2030+[4]

T [b4]-
19323

476.2504+[5]

alpha-1-antichymotrypsin
K.EQLSLLDR.F
782
487.2693++
S [y5]-
4247034

AACT_HUMAN

603.3461+[1]

L [y3]-
2094711

403.2300+[2]

L [y6]-
1465135

716.4301+[3]

L [y4]-
1365427

516.3140+[4]

Q [b2]-
1222196

258.1084+[5]

D [y2]-
957403

290.1459+[6]

L [b3]-
114810

371.1925+[7]

alpha-1-antichymotrypsin
K.EQLSLLDR.F
782
325.1819+++
L [y3]-
57123

AACT_HUMAN

403.2300+[1]

D [y2]-
52105

290.1459+[2]

alpha-1-antichymotrypsin
K.YTGNASALFILPDQD
783
876.9438++
L [y9]-
39933

AACT_HUMAN
K.M

1088.5986+[1]

A [b5]-
20117

507.2198+[2]

D [y4]-
19937

505.2253+[3]

alpha-1-antichymotrypsin
R.EIGELYLPK.F
784
531.2975++
P [y2]-
8170395

AACT_HUMAN

244.1656+[1]

G [y7]-
3338199

819.4611+[2]

L [y5]-
2616703

633.3970+[3]

L [y3]-
1922561

357.2496+[4]

Y [y4]-
1527792

520.3130+[5]

G [b3]-
1417240

300.1554+[6]

I [b2]-
1097654

243.1339+[7]

E [y6]-
302412

762.4396+[8]

E [b4]-429.1980+[9]
81633

Y [b6]-705.3454+[10]
36795

L [b5]-542.2821+[11]
31993

alpha-1-antichymotrypsin
R.EIGELYLPK.F
784
354.5341+++
P [y2]-
189758

AACT_HUMAN

244.1656+[1]

L [y3]-
86952

357.2496+[2]

G [b3]-
49661

300.1554+[3]

Y [y4]-
45518

520.3130+[4]

E [b4]-
19576

429.1980+[5]

I [b2]-
18375

243.1339+[6]

L [b5]-
13091

542.2821+[7]

alpha-1-antichymotrypsin
R.DYNLNDILLQLGIEEA
785
1148.5890++
G [y9]-
378153

AACT_HUMAN
FTSK.A

981.4888+[1]

F [b17]-
378153

981.4964++[2]

N [b3]-
338897

393.1405+[3]

L [y10]-
283255

1094.5728+[4]

E [y7]-
180253

811.3832+[5]

I [b7]-
172510

848.3785+[6]

T [y3]-
162966

335.1925+[7]

D [b6]-
135235

735.2944+[8]

L [b4]-
131573

506.2245+[9]

A [y5]-
129232

553.2980+[10]

F [y4]-
124490

482.2609+[11]

Y [b2]-
115367

279.0975+[12]

L [b9]-
106363

1074.5466+[13]

L [b8]-
101621

961.4625+[14]

E [y6]-
98740

682.3406+[15]

S [y2]-
75991

234.1448+[16]

N [b5]-
66387

620.2675+[17]

I [y8]-
61465

924.4673+[18]

alpha-1-antichymotrypsin
R.DYNLNDILLQLGIEEA
785
766.0618+++
G [y9]-
309485

AACT_HUMAN
FTSK.A

981.4888+[1]

F [b17]-
309485

981.4964++[2]

E [y7]-
262306

811.3832+[3]

N [b3]-
212306

393.1405+[4]

T [y3]-
199100

335.1925+[5]

F [y4]-
164346

482.2609+[6]

A [y5]-
161405

553.2980+[7]

Y [b2]-
149220

279.0975+[8]

E [y6]-
138836

682.3406+[9]

L [y10]-
137336

1094.5728+[10]

S [y2]-
134094

234.1448+[11]

I [b7]-
80072

848.3785+[12]

I [y8]-
77791

924.4673+[13]

L [b4]-
70889

506.2245+[14]

D [b6]-
64706

735.2944+[15]

L [b8]-
51201

961.4625+[16]

N [b5]-
42677

620.2675+[17]

L [b9]-
21609

1074.5466+[18]

alpha-1-antichymotrypsin
K.ADLSGITGAR.N
786
480.7591++
S [y7]-
4360743

AACT_HUMAN

661.3628+[1]

G [y6]-
3966462

574.3307+[2]

T [y4]-
1937824

404.2252+[3]

D [b2]-
799907

187.0713+[4]

G [y3]-
647883

303.1775+[5]

I [Y5]-
612145

517.3093+[6]

L [b3]-
606995

300.1554+[7]

S [b4]-
544408

387.1874+[8]

L [y8]-
348247

774.4468+[9]

G [b5]-
232083

444.2089+[10]

I [b6]-
132531

557.2930+[11]

A [y2]-
113896

246.1561+[12]

alpha-1-antichymotrypsin
K.ADLSGITGAR.N
786
320.8418+++
T [y4]-
218597

AACT_HUMAN

404.2252+[1]

G [y3]-
159381

303.1775+[2]

G [b5]-
46527

444.2089+[3]

A [y2]-
26911

246.1561+[4]

D [b2]-
22497

187.0713+[5]

S [b4]-
14589

387.1874+[6]

alpha-1-antichymotrypsin
R.NLAVSQVVHK.A
132
547.8195++
L [b2]-
1872233

AACT_HUMAN

228.1343+[1]

A [y8]-
1133381

867.5047+[2]

A [b3]-
1126331

299.1714+[3]

V [y7]-
672341

796.4676+[4]

S [y6]-
650028

697.3991+[5]

H [y2]-
582720

284.1717+[6]

V [y3]-
211547

383.2401+[7]

V [b4]-
163917

398.2398+[8]

Q [y5]-
100778

610.3671+[9]

V [y4]-
88456

482.3085+[10]

S [b5]-
64488

485.2718+[11]

V [b7]-
36045

712.3988+[12]

alpha-1-antichymotrypsin
R.NLAVSQVVHK.A
132
365.5487+++
L [b2]-
1175923

AACT_HUMAN

228.1343+[1]

V [y3]-
593693

383.2401+[2]

S [y6]-
587502

697.3991+[3]

H [y2]-
440259

284.1717+[4]

V [y4]-
375955

482.3085+[5]

Q [y5]-
349044

610.3671+[6]

A [b3]-
339236

299.1714+[7]

V [b4]-
172805

398.2398+[8]

S [b5]-
84594

485.2718+[9]

alpha-1-antichymotrypsin
K.AVLDVFEEGTEASAA
787
954.4835++
D [b4]-
1225699

AACT_HUMAN
TAVK.I

399.2238+[1]

G [y11]-
812780

1005.5211+[2]

V [b5]-
741243

498.2922+[3]

E [y12]-
651070

1134.5637+[4]

V [b2]-
634335

171.1128+[5]

A [y8]-
416106

718.4094+[6]

S [y7]-
360507

647.3723+[7]

F [b6]-
293935

645.3606+[8]

T [y4]-
281736

418.2660+[9]

E [y9]-
247592

847.4520+[10]

A [y3]-
246550

317.2183+[11]

E [b7]-
234044

774.4032+[12]

T [y10]-
221478

948.4997+[13]

A [y6]-
212344

560.3402+[14]

A [y5]-
195364

489.3031+[15]

E [b8]-
183901

903.4458+[16]

L [b3]-
176116

284.1969+[17]

V [y2]-
157419

246.1812+[18]

T [b10]-
52841

1061.5150+[19]

E [b11]-
34757

1190.5576+[20]

G [b9]-
25807

960.4673+[21]

alpha-1-antichymotrypsin
K.AVLDVFEEGTEASAA
787
636.6581+++
V [b2]-
659591

AACT_HUMAN
TAVK.I

171.1128+[1]

S [y7]-
630596

647.3723+[2]

A [y8]-
509467

718.4094+[3]

D [b4]-
353335

399.2238+[4]

A [y6]-
306747

560.3402+[5]

A [y5]-
280878

489.3031+[6]

E [y9]-
247347

847.4520+[7]

T [y4]-
197203

418.2660+[8]

A [y3]-
128853

317.2183+[9]

V [b5]-
120271

498.2922+[10]

V [y2]-
115428

246.1812+[11]

L [b3]-
102984

284.1969+[12]

G [y11]-
91215

1005.5211+[13]

F [b6]-
79016

645.3606+[14]

E [y12]-
72947

1134.5637+[15]

E [b7]-
58358

774.4032+[16]

T [y10]-
41071

948.4997+[17]

E [b8]-
32918

903.4458+[18]

G [b9]-
24275

960.4673+[19]

alpha-1-antichymotrypsin
K.ITLLSALVETR.T
130
608.3690++
S [y7]-
7387615

AACT_HUMAN

775.4308+[1]

T [b2]-
3498457

215.1390+[2]

L [y8]-
2684639

888.5149+[3]

L [b3]-
2164246

328.2231+[4]

A [y6]-
2045853

688.3988+[5]

L [y5]-
2027311

617.3617+[6]

L [y9]-
1949318

1001.5990+[7]

V [y4]-
1598519

504.2776+[8]

T [y2]-
1416847

276.1666+[9]

E [y3]-
967259

405.2092+[10]

A [b6]-
579420

599.3763+[11]

L [b4]-
431556

441.3071+[12]

S [b5]-
107634

528.3392+[13]

L [b7]-712.4604+[14]
71104

V [b8]-811.5288+[15]
24197

alpha-1-antichymotrypsin
K.ITLLSALVETR.T
130
405.9151+++
E [y3]-
738128

AACT_HUMAN

405.2092+[1]

T [y2]-
368830

276.1666+[2]

V [y4]-
328133

504.2776+[3]

A [b6]-
132469

599.3763+[4]

T [b2]-
126898

215.1390+[5]

L [y5]-
124559

617.3617+[6]

S [y7]-
54263

775.4308+[7]

L [b3]-
37891

328.2231+[8]

A [y6]-
29853

688.3988+[9]

L [b4]-
25558

441.3071+[10]

L [b7]-
13353

712.4604+[11]

S [b5]-
12290

528.3392+[12]

Pigment epithelium-
K.LAAAVSNFGYDLYR.V
788
780.3963++
D [b11]-
136227

derived factor

1109.5262+[1]

PEDF_HUMAN*

F [b8]-
61248

774.4145+[2]

N [b7]-
55532

314.1767++[3]

A [y12]-
53268

1375.6641+[4]

V [b5]-
35818

213.6392++[5]

L [b12]-
34918

1222.6103+[6]

G [b9]-
33934

831.4359+[7]

Y [b10]-
32923

994.4993+[8]

G [b9]-
32650

416.2216++[9]

V [b5]-
15646

426.2711+[10]

A [b2]-
14964

185.1285+[11]

D [b11]-
13922

555.2667++[12]

L [y3]-
13027

226.1368++[13]

A [b4]-
12782

327.2027+[14]

A [y12]-
12446

688.3357++[15]

V [y10]-
12400

1233.5899+[16]

A [y11]-
10793

652.8171++[17]

Pigment epithelium-
K.LAAAVSNFGYDLYR.V
788
520.5999+++
G [y6]-
42885

derived factor

786.3781+[1]

PEDF_HUMAN*

D [y4]-
32080

566.2933+[2]

V [y5]-
17494

729.3566+[3]

L [y3]-
12304

451.2663+[5]

Y [y2]-
7780

338.1823+[6]

Pigment epithelium-
R.ALYYDLISSPDIHGTY
789
652.6632+++
Y [y15]-
12278

derived factor
K.E

886.4305++[1]

PEDF_HUMAN*

L [b2]-
7601

185.1285+[2]

S [y10]-
7345

1104.5320+[3]

Y [y14]-
5976

804.8988++[4]

Pigment epithelium-
K.ELLDTVTAPQK.N
790
607.8350++
T [y5]-
59670

derived factor

272.6581++[1]

PEDF_HUMAN*

Q [y2]-
11954

275.1714+[2]

Pigment epithelium-
K.ELLDTVTAPQK.N
790
405.5591+++
L [b2]-
16428

derived factor

243.1339+[1]

PEDF_HUMAN*

T [b7]-
7918

386.7080++[2]

Q [y2]-
7043

275.1714+[3]

T [y5]-
5237

272.6581++[4]

Pigment epithelium-
K.SSFVAPLEK.S
791
489.2687++
A [y5]-
20068

derived factor

557.3293+[1]

PEDF_HUMAN*

A [y5]-
5059

279.1683++[2]

S [b2]-
4883

175.0713+[3]

Pigment epithelium-
K.SSFVAPLEK.S
791
326.5149+++
A [y5]-
70240

derived factor

279.1683++[1]

PEDF_HUMAN*

A [y5]-
63329

557.3293+[2]

S [b2]-
39662

175.0713+[3]

L [b7]-
5393

351.6947++[4]

Pigment epithelium-
K.EIPDEISILLLGVAHFK
792
632.0277+++
P [y15]-
37871

derived factor
.G

826.4745++[1]

PEDF_HUMAN*

G [y6]-
20077

658.3671+[2]

L [y7]-
8952

771.4512+[3]

Pigment epithelium-
K.TSLEDFYLDEER.T
793
758.8437++
R [y1]-
8206

derived factor

175.1190+[1]

PEDF_HUMAN*

D [b9]-
4591

1084.4833+[2]

F [b6]-
4498

693.3090+[3]

Pigment epithelium-
K.TSLEDFYLDEER.T
793
506.2316+++
F [b6]-693.3090+[1]
3526

derived factor

D [y4]-548.2311+[2]
3208

PEDF_HUMAN*

Pigment epithelium-
K.VTQNLTLIEESLTSEFI
794
858.4413+++
T [b13]-
11072

derived factor
HDIDR.E

721.8905++[1]

PEDF_HUMAN*

T [y17]-
8442

1009.5075++[2]

D [y4]-
6522

518.2569+[3]

Pigment epithelium-
K.TVQAVLTVPK.L
795
528.3266++
Q [y8]-
83536

derived factor

855.5298+[1]

PEDF_HUMAN*

V [b2]-
64729

201.1234+[2]

A [b4]-
58198

200.6132++[3]

P [y2]-
43347

244.1656+[4]

Q [y8]-
38398

428.2686++[5]

A [y7]-
33770

727.4713+[6]

Q [b3]-
17809

329.1819+[7]

L [y5]-
17518

557.3657+[8]

V [y6]-
17029

656.4341+[9]

V [y6]-
15839

328.7207++[10]

T [y4]-
13859

444.2817+[11]

V [y3]-343.2340+[12]
10717

A [b4]-400.2191+[13]
9695

Pigment epithelium-
K.TVQAVLTVPK.L
795
352.5535+++
P [y2]-
8295

derived factor

244.1656+[1]

PEDF_HUMAN*

T [y4]-
2986

444.2817+[2]

A [b4]-
2848

400.2191+[3]

Pigment epithelium-
K.LSYEGEVTK.S
796
513.2611++
V [b7]-
60831

derived factor

389.6845++[1]

PEDF_HUMAN*

E [b6]-
34857

679.2933+[2]

Y [y7]-
10075

413.2031++[3]

V [b7]-
8920

778.3618+[4]

Y [b3]-
8008

364.1867+[5]

Pigment epithelium-
K.LQSLFDSPDFSK.I
797
692.3432++
S [y2]-
49594

derived factor

234.1448+[1]

PEDF_HUMAN*

L [y9]-
48160

1055.5044+[2]

P [b8]-
23566

888.4462+[3]

S [b7]-
13766

791.3934+[4]

P [y5]-
12305

297.1501++[5]

P [y5]-
10702

593.2930+[6]

F [b5]-
8929

589.3344+[7]

D [b9]-
8742

1003.4731+[8]

Pigment epithelium-
K.LQSLFDSPDFSK.I
797
461.8979+++
P [y5]-
9154

derived factor

593.2930+[1]

PEDF_HUMAN*

P [y5]-
5479

297.1501++[2]

Pigment epithelium-
R.DTDTGALLFIGK.I
798
625.8350++
G [y2]-
32092

derived factor

204.1343+[1]

PEDF_HUMAN*

G [y8]-
29707

818.5135+[2]

T [b2]-
28172

217.0819+[4]

T [b4]-
28172

217.0819++[3]

F [y4]-
22160

464.2867+[5]

D [y10]-
20267

1034.5881+[6]

T [y9]-
17083

919.5611+[7]

L [y6]-
14854

690.4549+[8]

L [y5]-
12349

577.3708+[9]

T [b4]-
11773

433.1565+[10]

I [y3]-
11575

317.2183+[11]

D [b3]-332.1088+[12]
8968

A [y7]-761.4920+[13]
8598

*Transition scan on Agilent 6490

Example 4
Study III to Identify and Confirm Preeclampsia Biomarkers

A further hypothesis-dependent study was performed using essentially the same methods described in the preceding Examples unless noted below. The scheduled MRM assay used in Examples 1 and 2 but now augmented with newly discovered analytes from the Example 3 and related studies was used. 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.

Thirty subjects with preeclampsia who delivered preterm (<37 weeks 0 days) were selected for analyses. Twenty-three subjects were available with isolated preeclampsia; thus, eight subjects were selected with additional findings as follows: 5 subjects with gestational diabetes, one subject with pre-existing type 2 diabetes, and one subject with chronic hypertension. Subjects were classified as having severe preeclampsia if it was indicated in the Case Report Form as severe or if the pregnancy was complicated by HELLP syndrome. All other cases were classified as mild preeclampsia. Cases were matched to term controls (>/=37 weeks 0 days) without preeclampsia at a 2:1 control-to-case ratio.

The samples were processed in 4 batches with each containing 3 HGS controls. All serum samples were depleted of the 14 most abundant serum proteins using MARS14 (Agilent), digested with trypsin, desalted, and resolubilized with reconstitution solution containing 5 internal standard peptides as described in previous examples.

The LC-MS/MS analysis was performed with an Agilent Poroshell 120 EC-C18 column (2.1×50 mm, 2.7 μm) at a flow rate of 400 μl/min and eluted with an acetonitrile gradient into an AB Sciex QTRAP5500 mass spectrometer. The sMRM assay measured 750 transitions that correspond to 349 peptides and 164 proteins. Chromatographic peaks were integrated using MultiQuant™ software (AB Sciex).

Transitions were excluded from analysis if they were missing in more than 20% of the samples. 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 or various combinations (Tables 12-15). Multivariate classifiers were built by Lasso and Random Forest methods. 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. For summed Random Forest Gini Importance values an Empirical Cumulative Distribution Function was fitted and probabilities (P) were calculated. The nonzero Lasso summed coefficients calculated from the different window combinations are shown in Tables 16-19. Summed Random Forest Gini values, with P>0.9 are found in Tables 20-22.

TABLE 12

Univariate AUC values all windows

SEQ ID

Transition
NO:
Protein
AUC

LDFHFSSDR_375.2_611.3
6
INHBC_HUMAN
0.785

TVQAVLTVPK_528.3_428.3
7
PEDF_HUMAN
0.763

TVQAVLTVPK_528.3_855.5
7
PEDF_HUMAN
0.762

ETLLQDFR_511.3_565.3
9
AMBP_HUMAN
0.756

DTDTGALLFIGK_625.8_818.5
799
PEDF_HUMAN
0.756

DTDTGALLFIGK_625.8_217.1
799
PEDF_HUMAN
0.756

IQTHSTTYR_369.5_627.3
59
F13B_HUMAN
0.755

IQTHSTTYR_369.5_540.3
59
F13B_HUMAN
0.753

ETLLQDFR_511.3_322.2
9
AMBP_HUMAN
0.751

LDFHFSSDR_375.2_464.2
6
INHBC_HUMAN
0.745

HHGPTITAK_321.2_275.1
33
AMBP_HUMAN
0.743

VNHVTLSQPK_374.9_244.2
3
B2MG_HUMAN
0.733

VEHSDLSFSK_383.5_468.2
800
B2MG_HUMAN
0.732

ALALPPLGLAPLLNLWAKPQGR_770.5_256.2
801
SHBG_HUMAN
0.728

HHGPTITAK_321.2_432.3
33
AMBP_HUMAN
0.728

FLYHK_354.2_447.2
802
AMBP_HUMAN
0.722

FLYHK_354.2_284.2
802
AMBP_HUMAN
0.721

IALGGLLFPASNLR_481.3_657.4
55
SHBG_HUMAN
0.719

GDTYPAELYITGSILR_885.0_274.1
43
F13B_HUMAN
0.716

VEHSDLSFSK_383.5_234.1
800
B2MG_HUMAN
0.714

GPGEDFR_389.2_623.3
8
PTGDS_HUMAN
0.714

IALGGLLFPASNLR_481.3_412.3
55
SHBG_HUMAN
0.712

EVFSKPISWEELLQ_852.9_260.2
803
FA40A_HUMAN
0.708

FICPLTGLWPINTLK_887.0_685.4
804
APOH_HUMAN
0.707

GFQALGDAADIR_617.3_717.4
11
TIMP1_HUMAN
0.707

DVLLLVHNLPQNLTGHIWYK_791.8_310.2
805
PSG7_HUMAN
0.704

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4
38
SHBG_HUMAN
0.704

ATVVYQGER_511.8_652.3
10
APOH_HUMAN
0.702

ALALPPLGLAPLLNLWAKPQGR_770.5_457.3
801
SHBG_HUMAN
0.702

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_768.5
38
SHBG_HUMAN
0.702

DVLLLVHNLPQNLTGHIWYK_791.8_883.0
805
PSG7_HUMAN
0.702

AHYDLR_387.7_566.3
42
FETUA_HUMAN
0.701

GPGEDFR_389.2_322.2
8
PTGDS_HUMAN
0.701

FSVVYAK_407.2_579.4
1
FETUA_HUMAN
0.701

TLAFVR_353.7_274.2
806
FA7_HUMAN
0.699

IAPQLSTEELVSLGEK_857.5_533.3
56
AFAM_HUMAN
0.698

HFQNLGK_422.2_527.2
50
AFAM_HUMAN
0.696

GDTYPAELYITGSILR_885.0_922.5
43
F13B_HUMAN
0.694

FICPLTGLWPINTLK_887.0_756.9
804
APOH_HUMAN
0.694

EVFSKPISWEELLQ_852.9_376.2
803
FA40A_HUMAN
0.692

ATVVYQGER_511.8_751.4
10
APOH_HUMAN
0.690

ELIEELVNITQNQK_557.6_618.3
807
IL13_HUMAN
0.690

VNHVTLSQPK_374.9_459.3
3
B2MG_HUMAN
0.687

IAQYYYTFK_598.8_395.2
25
F13B_HUMAN
0.685

IAPQLSTEELVSLGEK_857.5_333.2
56
AFAM_HUMAN
0.685

LIENGYFHPVK_439.6_627.4
66
F13B_HUMAN
0.684

FSVVYAK_407.2_381.2
1
FETUA_HUMAN
0.684

HFQNLGK_422.2_285.1
50
AFAM_HUMAN
0.684

AHYDLR_387.7_288.2
42
FETUA_HUMAN
0.684

ELPQSIVYK_538.8_417.7
808
FBLN3_HUMAN
0.683

DADPDTFFAK_563.8_825.4
49
AFAM_HUMAN
0.679

DADPDTFFAK_563.8_302.1
49
AFAM_HUMAN
0.676

IAQYYYTFK_598.8_884.4
25
F13B_HUMAN
0.673

VVESLAK_373.2_646.4
809
IBP1_HUMAN
0.673

YGIEEHGK_311.5_599.3
810
CXA1_HUMAN
0.673

GFQALGDAADIR_617.3_288.2
11
TIMP1_HUMAN
0.673

YTTEIIK_434.2_704.4
39
C1R_HUMAN
0.671

LPDTPQGLLGEAR_683.87_427.2
811
EGLN_HUMAN
0.666

TLAFVR_353.7_492.3
806
FA7_HUMAN
0.666

LIENGYFHPVK_439.6_343.2
66
F13B_HUMAN
0.665

ELIEELVNITQNQK_557.6_517.3
807
IL13_HUMAN
0.665

DPNGLPPEAQK_583.3_669.4
14
RET4_HUMAN
0.664

TNTNEFLIDVDK_704.85_849.5
812
TF_HUMAN
0.663

NTVISVNPSTK_580.3_845.5
68
VCAM1_HUMAN
0.662

YEFLNGR_449.7_293.1
124
PLMN_HUMAN
0.662

AIGLPEELIQK_605.86_856.5
813
FABPL_HUMAN
0.662

YTTEIIK_434.2_603.4
39
C1R_HUMAN
0.661

AEHPTWGDEQLFQTTR_639.3_765.4
814
PGH1_HUMAN
0.658

HTLNQIDEVK_598.8_951.5
48
FETUA_HUMAN
0.658

HTLNQIDEVK_598.8_958.5
48
FETUA_HUMAN
0.656

LPNNVLQEK_527.8_730.4
46
AFAM_HUMAN
0.655

DPNGLPPEAQK_583.3_497.2
14
RET4_HUMAN
0.655

TFLTVYWTPER_706.9_401.2
815
ICAM1_HUMAN
0.653

TFLTVYWTPER_706.9_502.3
815
ICAM1_HUMAN
0.653

SEPRPGVLLR_375.2_454.3
816
FA7_HUMAN
0.652

FTFTLHLETPKPSISSSNLNPR_829.4_787.4
82
PSG1_HUMAN
0.652

DAQYAPGYDK_564.3_813.4
83
CFAB_HUMAN
0.651

ALDLSLK_380.2_185.1
817
ITIH3_HUMAN
0.651

NCSFSIIYPVVIK_770.4_555.4
818
CRHBP_HUMAN
0.650

NTVISVNPSTK_580.3_732.4
68
VCAM1_HUMAN
0.649

IPSNPSHR_303.2_610.3
819
FBLN3_HUMAN
0.649

DAQYAPGYDK_564.3_315.1
83
CFAB_HUMAN
0.647

TLPFSR_360.7_506.3
820
LYAM1_HUMAN
0.647

LPNNVLQEK_527.8_844.5
46
AFAM_HUMAN
0.644

AALAAFNAQNNGSNFQLEEISR_789.1_746.4
821
FETUA_HUMAN
0.644

AEHPTWGDEQLFQTTR_639.3_569.3
814
PGH1_HUMAN
0.644

NNQLVAGYLQGPNVNLEEK_700.7_999.5
822
IL1RA_HUMAN
0.642

EHSSLAFWK_552.8_267.1
823
APOH_HUMAN
0.642

ALNHLPLEYNSALYSR_621.0_696.4
52
CO6_HUMAN
0.641

VSEADSSNADWVTK_754.9_347.2
964
CFAB_HUMAN
0.641

NFPSPVDAAFR_610.8_959.5
824
HEMO_HUMAN
0.641

WNFAYWAAHQPWSR_607.3_545.3
825
PRG2_HUMAN
0.638

WNFAYWAAHQPWSR_607.3_673.3
825
PRG2_HUMAN
0.638

TAVTANLDIR_537.3_802.4
826
CHL1_HUMAN
0.638

IPSNPSHR_303.2_496.3
819
FBLN3_HUMAN
0.637

YWGVASFLQK_599.8_849.5
17
RET4_HUMAN
0.637

ALDLSLK_380.2_575.3
817
ITIH3_HUMAN
0.636

YNSQLLSFVR_613.8_508.3
827
TFR1_HUMAN
0.636

EHSSLAFWK_552.8_838.4
823
APOH_HUMAN
0.635

YWGVASFLQK_599.8_350.2
17
RET4_HUMAN
0.635

ALNHLPLEYNSALYSR_621.0_538.3
52
CO6_HUMAN
0.633

DLYHYITSYVVDGEIIIYGPAYSGR_955.5_707.3
828
PSG1_HUMAN
0.633

FTFTLHLETPKPSISSSNLNPR_829.4_874.4
82
PSG1_HUMAN
0.633

YQISVNK_426.2_560.3
829
FIBB_HUMAN
0.632

YEFLNGR_449.7_606.3
124
PLMN_HUMAN
0.632

LNIGYIEDLK_589.3_950.5
830
PAI2_HUMAN
0.631

LLEVPEGR_456.8_356.2
31
C1S_HUMAN
0.630

ENPAVIDFELAPIVDLVR_670.7_811.5
831
CO6_HUMAN
0.630

YYLQGAK_421.7_516.3
832
ITIH4_HUMAN
0.630

ITGFLKPGK_320.9_301.2
833
LBP_HUMAN
0.629

DLHLSDVFLK_396.2_260.2
77
CO6_HUMAN
0.629

HELTDEELQSLFTNFANVVDK_817.1_854.4
834
AFAM_HUMAN
0.629

YYLQGAK_421.7_327.1
832
ITIH4_HUMAN
0.628

NCSFSIIYPVVIK_770.4_831.5
818
CRHBP_HUMAN
0.627

FLNWIK_410.7_560.3
835
HABP2_HUMAN
0.627

ITGFLKPGK_320.9_429.3
833
LBP_HUMAN
0.627

VVESLAK_373.2_547.3
809
IBP1_HUMAN
0.627

NFPSPVDAAFR_610.8_775.4
824
HEMO_HUMAN
0.627

AEIEYLEK_497.8_552.3
836
LYAM1_HUMAN
0.627

ENPAVIDFELAPIVDLVR_670.7_601.4
831
CO6_HUMAN
0.627

VQEVLLK_414.8_373.3
837
HYOU1_HUMAN
0.626

TQIDSPLSGK_523.3_703.4
838
VCAM1_HUMAN
0.626

VSEADSSNADWVTK_754.9_533.3
964
CFAB_HUMAN
0.625

DFNQFSSGEK_386.8_189.1
839
FETA_HUMAN
0.624

LPDTPQGLLGEAR_683.87_940.5
811
EGLN_HUMAN
0.623

DLYHYITSYVVDGEIIIYGPAYSGR_955.5_650.3
828
PSG1_HUMAN
0.623

FAFNLYR_465.8_712.4
94
HEP2_HUMAN
0.623

LLELTGPK_435.8_644.4
840
A1BG_HUMAN
0.623

NEIVFPAGILQAPFYTR_968.5_357.2
841
ECE1_HUMAN
0.623

EFDDDTYDNDIALLQLK_1014.48_501.3
842
TPA_HUMAN
0.621

FSLVSGWGQLLDR_493.3_403.2
843
FA7_HUMAN
0.621

LLELTGPK_435.8_227.2
840
A1BG_HUMAN
0.621

LIQDAVTGLTVNGQITGDK_972.0_640.4
844
ITIH3_HUMAN
0.621

QGHNSVFLIK_381.6_520.4
845
HEMO_HUMAN
0.620

ILPSVPK_377.2_244.2
846
PGH1_HUMAN
0.620

STLFVPR_410.2_272.2
847
PEPD_HUMAN
0.620

TLEAQLTPR_514.8_685.4
87
HEP2_HUMAN
0.619

QGHNSVFLIK_381.6_260.2
845
HEMO_HUMAN
0.619

LSSPAVITDK_515.8_743.4
78
PLMN_HUMAN
0.618

LLEVPEGR_456.8_686.4
31
C1S_HUMAN
0.617

GVTGYFTFNLYLK_508.3_260.2
848
PSG5_HUMAN
0.617

EALVPLVADHK_397.9_390.2
849
HGFA_HUMAN
0.616

SFRPFVPR_335.9_272.2
850
LBP_HUMAN
0.616

DFNQFSSGEK_386.8_333.2
839
FETA_HUMAN
0.616

GSLVQASEANLQAAQDFVR_668.7_735.4
851
ITIH1_HUMAN
0.616

ITLPDFTGDLR_624.3_920.5
852
LBP_HUMAN
0.615

LIQDAVTGLTVNGQITGDK_972.0_798.4
844
ITIH3_HUMAN
0.615

ILPSVPK_377.2_227.2
846
PGH1_HUMAN
0.614

DIIKPDPPK_511.8_342.2
853
IL12B_HUMAN
0.613

QGFGNVATNTDGK_654.81_319.2
854
FIBB_HUMAN
0.613

AVLHIGEK_289.5_348.7
855
THBG_HUMAN
0.613

YENYTSSFFIR_713.8_756.4
856
IL12B_HUMAN
0.613

LSSPAVITDK_515.8_830.5
78
PLMN_HUMAN
0.613

SFRPFVPR_335.9_635.3
850
LBP_HUMAN
0.613

GLQYAAQEGLLALQSELLR_1037.1_858.5
857
LBP_HUMAN
0.612

VELAPLPSWQPVGK_760.9_400.3
858
ICAM1_HUMAN
0.612

CRPINATLAVEK_457.9_559.3
859
CGB1_HUMAN
0.610

GIVEECCFR_585.3_771.3
860
IGF2_HUMAN
0.610

AVLHIGEK_289.5_292.2
855
THBG_HUMAN
0.610

TLEAQLTPR_514.8_814.4
87
HEP2_HUMAN
0.610

SILFLGK_389.2_577.4
861
THBG_HUMAN
0.609

HVVQLR_376.2_614.4
862
IL6RA_HUMAN
0.609

TQILEWAAER_608.8_761.4
863
EGLN_HUMAN
0.609

NSDQEIDFK_548.3_409.2
864
S10A5_HUMAN
0.609

SGAQATWTELPWPHEK_613.3_510.3
865
HEMO_HUMAN
0.607

EDTPNSVWEPAK_686.8_630.3
40
C1S_HUMAN
0.607

ITLPDFTGDLR_624.3_288.2
852
LBP_HUMAN
0.607

TLPFSR_360.7_409.2
820
LYAM1_HUMAN
0.607

GIVEECCFR_585.3_900.3
860
IGF2_HUMAN
0.606

SGAQATWTELPWPHEK_613.3_793.4
865
HEMO_HUMAN
0.606

VRPQQLVK_484.3_609.4
866
ITIH4_HUMAN
0.605

SEYGAALAWEK_612.8_788.4
867
CO6_HUMAN
0.605

LEEHYELR_363.5_288.2
868
PAI2_HUMAN
0.605

FQLPGQK_409.2_275.1
47
PSG1_HUMAN
0.605

IHWESASLLR_606.3_437.2
869
CO3_HUMAN
0.604

NAVVQGLEQPHGLVVHPLR_688.4_890.6
870
LRP1_HUMAN
0.604

VTGLDFIPGLHPILTLSK_641.04_771.5
871
LEP_HUMAN
0.603

YNSQLLSFVR_613.8_734.5
827
TFR1_HUMAN
0.603

ALVLELAK_428.8_672.4
872
INHBE_HUMAN
0.603

FAFNLYR_465.8_565.3
94
HEP2_HUMAN
0.603

VRPQQLVK_484.3_722.4
866
ITIH4_HUMAN
0.602

SLQAFVAVAAR_566.8_487.3
873
IL23A_HUMAN
0.602

AGFAGDDAPR_488.7_701.3
874
ACTB_HUMAN
0.601

EDTPNSVWEPAK_686.8_315.2
40
C1S_HUMAN
0.601

VQEVLLK_414.8_601.4
837
HYOU1_HUMAN
0.601

SEYGAALAWEK_612.8_845.5
867
CO6_HUMAN
0.601

TLFIFGVTK_513.3_215.1
676
PSG4_HUMAN
0.601

YNQLLR_403.7_288.2
875
ENOA_HUMAN
0.600

TQIDSPLSGK_523.3_816.5
838
VCAM1_HUMAN
0.600

TABLE 13

Univariate AUC values early window

SEQ ID

Transition
NO:
Protein
AUC

LDFHFSSDR_375.2_611.3
6
INHBC_HUMAN
0.858

LDFHFSSDR_375.2_464.2
6
INHBC_HUMAN
0.838

ELPQSIVYK_538.8_417.7
808
FBLN3_HUMAN
0.815

VNHVTLSQPK_374.9_244.2
3
B2MG_HUMAN
0.789

GFQALGDAADIR_617.3_717.4
11
TIMP1_HUMAN
0.778

VEHSDLSFSK_383.5_234.1
800
B2MG_HUMAN
0.778

TVQAVLTVPK_528.3_428.3
7
PEDF_HUMAN
0.775

TVQAVLTVPK_528.3_855.5
7
PEDF_HUMAN
0.775

DTDTGALLFIGK_625.8_217.1
799
PEDF_HUMAN
0.772

ETLLQDFR_511.3_565.3
9
AMBP_HUMAN
0.772

DTDTGALLFIGK_625.8_818.5
799
PEDF_HUMAN
0.769

VVESLAK_373.2_646.4
809
IBP1_HUMAN
0.766

FSVVYAK_407.2_381.2
1
FETUA_HUMAN
0.764

HHGPTITAK_321.2_275.1
33
AMBP_HUMAN
0.764

ETLLQDFR_511.3_322.2
9
AMBP_HUMAN
0.761

FLYHK_354.2_447.2
802
AMBP_HUMAN
0.758

GPGEDFR_389.2_623.3
8
PTGDS_HUMAN
0.755

HHGPTITAK_321.2_432.3
33
AMBP_HUMAN
0.755

VEHSDLSFSK_383.5_468.2
800
B2MG_HUMAN
0.752

FLYHK_354.2_284.2
802
AMBP_HUMAN
0.749

FSVVYAK_407.2_579.4
1
FETUA_HUMAN
0.749

VNHVTLSQPK_374.9_459.3
3
B2MG_HUMAN
0.749

IPSNPSHR_303.2_610.3
819
FBLN3_HUMAN
0.746

VVESLAK_373.2_547.3
809
IBP1_HUMAN
0.746

IPSNPSHR_303.2_496.3
819
FBLN3_HUMAN
0.746

NCSFSIIYPVVIK_770.4_555.4
818
CRHBP_HUMAN
0.746

GFQALGDAADIR_617.3_288.2
11
TIMP1_HUMAN
0.744

IQTHSTTYR_369.5_627.3
59
F13B_HUMAN
0.744

AALAAFNAQNNGSNFQLEEISR_789.1_746.4
821
FETUA_HUMAN
0.738

AHYDLR_387.7_566.3
42
FETUA_HUMAN
0.738

IQTHSTTYR_369.5_540.3
59
F13B_HUMAN
0.738

AIGLPEELIQK_605.86_856.5
813
FABPL_HUMAN
0.735

ATVVYQGER_511.8_751.4
10
APOH_HUMAN
0.735

FICPLTGLWPINTLK_887.0_685.4
804
APOH_HUMAN
0.735

FICPLTGLWPINTLK_887.0_756.9
804
APOH_HUMAN
0.735

HTLNQIDEVK_598.8_958.5
48
FETUA_HUMAN
0.735

AQETSGEEISK_589.8_979.5
876
IBP1_HUMAN
0.732

DSPSVWAAVPGK_607.31_301.2
877
PROF1_HUMAN
0.732

GPGEDFR_389.2_322.2
8
PTGDS_HUMAN
0.732

ATVVYQGER_511.8_652.3
10
APOH_HUMAN
0.729

NFPSPVDAAFR_610.8_959.5
824
HEMO_HUMAN
0.729

LIENGYFHPVK_439.6_627.4
66
F13B_HUMAN
0.726

AHYDLR_387.7_288.2
42
FETUA_HUMAN
0.726

ELIEELVNITQNQK_557.6_618.3
807
IL13_HUMAN
0.724

ETPEGAEAKPWYEPIYLGGVFQLEK_951.14_877.5
878
TNFA_HUMAN
0.724

ALDLSLK_380.2_185.1
817
ITIH3_HUMAN
0.721

IHWESASLLR_606.3_437.2
869
CO3_HUMAN
0.721

DAQYAPGYDK_564.3_813.4
83
CFAB_HUMAN
0.718

NFPSPVDAAFR_610.8_775.4
824
HEMO_HUMAN
0.718

AVGYLITGYQR_620.8_523.3
879
PZP_HUMAN
0.715

AVGYLITGYQR_620.8_737.4
879
PZP_HUMAN
0.712

DIPHWLNPTR_416.9_600.3
880
PAPP1_HUMAN
0.712

ALDLSLK_380.2_575.3
817
ITIH3_HUMAN
0.709

IEGNLIFDPNNYLPK_874.0_845.5
16
APOB_HUMAN
0.709

LIENGYFHPVK_439.6_343.2
66
F13B_HUMAN
0.709

QTLSWTVTPK_580.8_818.4
881
PZP_HUMAN
0.709

DAQYAPGYDK_564.3_315.1
83
CFAB_HUMAN
0.707

GLQYAAQEGLLALQSELLR_1037.1_858.5
857
LBP_HUMAN
0.707

IEGNLIFDPNNYLPK_874.0_414.2
16
APOB_HUMAN
0.707

IQHPFTVEEFVLPK_562.0_861.5
882
PZP_HUMAN
0.707

QTLSWTVTPK_580.8_545.3
881
PZP_HUMAN
0.707

VSEADSSNADWVTK_754.9_347.2
964
CFAB_HUMAN
0.707

ILPSVPK_377.2_244.2
846
PGH1_HUMAN
0.704

IQHPFTVEEFVLPK_562.0_603.4
882
PZP_HUMAN
0.704

NCSFSIIYPVVIK_770.4_831.5
818
CRHBP_HUMAN
0.704

YNSQLLSFVR_613.8_508.3
827
TFR1_HUMAN
0.704

HTLNQIDEVK_598.8_951.5
48
FETUA_HUMAN
0.701

NEIWYR_440.7_637.4
883
FA12_HUMAN
0.701

QGHNSVFLIK_381.6_260.2
845
HEMO_HUMAN
0.701

YTTEIIK_434.2_603.4
39
C1R_HUMAN
0.701

STLFVPR_410.2_272.2
847
PEPD_HUMAN
0.699

EVFSKPISWEELLQ_852.9_260.2
803
FA40A_HUMAN
0.698

TGISPLALIK_506.8_741.5
20
APOB_HUMAN
0.698

TSESGELHGLTTEEEFVEGIYK_819.06_310.2
44
TTHY_HUMAN
0.698

AEHPTWGDEQLFQTTR_639.3_569.3
814
PGH1_HUMAN
0.695

AEHPTWGDEQLFQTTR_639.3_765.4
814
PGH1_HUMAN
0.695

HFQNLGK_422.2_527.2
50
AFAM_HUMAN
0.695

SVSLPSLDPASAK_636.4_473.3
15
APOB_HUMAN
0.695

ILPSVPK_377.2_227.2
846
PGH1_HUMAN
0.692

LIQDAVTGLTVNGQITGDK_972.0_640.4
844
ITIH3_HUMAN
0.692

QGHNSVFLIK_381.6_520.4
845
HEMO_HUMAN
0.692

TGISPLALIK_506.8_654.5
20
APOB_HUMAN
0.692

YGIEEHGK_311.5_599.3
810
CXA1_HUMAN
0.692

ELIEELVNITQNQK_557.6_517.3
807
IL13_HUMAN
0.689

IHWESASLLR_606.3_251.2
869
CO3_HUMAN
0.689

LIQDAVTGLTVNGQITGDK_972.0_798.4
844
ITIH3_HUMAN
0.689

ALALPPLGLAPLLNLWAKPQGR_770.5_256.2
801
SHBG_HUMAN
0.687

ALNFGGIGVVVGHELTHAFDDQGR_837.1_299.2
34
ECE1_HUMAN
0.687

AQETSGEEISK_589.8_850.4
876
IBP1_HUMAN
0.687

GVTGYFTFNLYLK_508.3_683.9
848
PSG5_HUMAN
0.687

ITLPDFTGDLR_624.3_288.2
852
LBP_HUMAN
0.687

LPDTPQGLLGEAR_683.87_427.2
811
EGLN_HUMAN
0.687

SVSLPSLDPASAK_636.4_885.5
15
APOB_HUMAN
0.687

TLAFVR_353.7_274.2
806
FA7_HUMAN
0.687

YTTEIIK_434.2_704.4
39
C1R_HUMAN
0.687

EFDDDTYDNDIALLQLK_1014.48_388.3
842
TPA_HUMAN
0.684

IALGGLLFPASNLR_481.3_657.4
55
SHBG_HUMAN
0.684

DFNQFSSGEK_386.8_189.1
839
FETA_HUMAN
0.681

EHSSLAFWK_552.8_838.4
823
APOH_HUMAN
0.681

ELPQSIVYK_538.8_409.2
808
FBLN3_HUMAN
0.681

ITGFLKPGK_320.9_301.2
833
LBP_HUMAN
0.681

ITGFLKPGK_320.9_429.3
833
LBP_HUMAN
0.681

AFQVWSDVTPLR_709.88_385.3
884
MMP2_HUMAN
0.678

GLQYAAQEGLLALQSELLR_1037.1_929.5
857
LBP_HUMAN
0.678

HYINLITR_515.3_301.1
885
NPY_HUMAN
0.678

NAVVQGLEQPHGLVVHPLR_688.4_890.6
870
LRP1_HUMAN
0.675

WWGGQPLWITATK_772.4_929.5
886
ENPP2_HUMAN
0.675

YNQLLR_403.7_288.2
875
ENOA_HUMAN
0.675

LDGSTHLNIFFAK_488.3_852.5
887
PAPP1_HUMAN
0.672

VVGGLVALR_442.3_784.5
5
FA12_HUMAN
0.672

WNFAYWAAHQPWSR_607.3_673.3
825
PRG2_HUMAN
0.672

NHYTESISVAK_624.8_252.1
888
NEUR1_HUMAN
0.670

NSDQEIDFK_548.3_409.2
864
S10A5_HUMAN
0.670

SGAQATWTELPWPHEK_613.3_510.3
865
HEMO_HUMAN
0.670

WNFAYWAAHQPWSR_607.3_545.3
825
PRG2_HUMAN
0.670

SFRPFVPR_335.9_272.2
850
LBP_HUMAN
0.670

AFQVWSDVTPLR_709.88_347.2
884
MMP2_HUMAN
0.667

DADPDTFFAK_563.8_825.4
49
AFAM_HUMAN
0.667

EHSSLAFWK_552.8_267.1
823
APOH_HUMAN
0.667

ITENDIQIALDDAK_779.9_632.3
18
APOB_HUMAN
0.667

ITLPDFTGDLR_624.3_920.5
852
LBP_HUMAN
0.667

VQEVLLK_414.8_373.3
837
HYOU1_HUMAN
0.667

VSFSSPLVAISGVALR_802.0_715.4
889
PAPP1_HUMAN
0.667

HFQNLGK_422.2_285.1
50
AFAM_HUMAN
0.664

ITENDIQIALDDAK_779.9_873.5
18
APOB_HUMAN
0.664

ALQDQLVLVAAK_634.9_289.2
890
ANGT_HUMAN
0.661

DLHLSDVFLK_396.2_260.2
77
CO6_HUMAN
0.661

DLHLSDVFLK_396.2_366.2
77
CO6_HUMAN
0.661

TAVTANLDIR_537.3_802.4
826
CHL1_HUMAN
0.661

DADPDTFFAK_563.8_302.1
49
AFAM_HUMAN
0.658

DPTFIPAPIQAK_433.2_461.2
891
ANGT_HUMAN
0.658

FAFNLYR_465.8_712.4
94
HEP2_HUMAN
0.658

IALGGLLFPASNLR_481.3_412.3
55
SHBG_HUMAN
0.658

IAQYYYTFK_598.8_395.2
25
F13B_HUMAN
0.658

LPNNVLQEK_527.8_730.4
46
AFAM_HUMAN
0.658

SLDFTELDVAAEK_719.4_874.5
97
ANGT_HUMAN
0.658

VELAPLPSWQPVGK_760.9_400.3
858
ICAM1_HUMAN
0.658

DIIKPDPPK_511.8_342.2
853
IL12B_HUMAN
0.655

EVFSKPISWEELLQ_852.9_376.2
803
FA40A_HUMAN
0.655

LSETNR_360.2_330.2
892
PSG1_HUMAN
0.655

NEIWYR_440.7_357.2
883
FA12_HUMAN
0.655

SFRPFVPR_335.9_635.3
850
LBP_HUMAN
0.655

SGAQATWTELPWPHEK_613.3_793.4
865
HEMO_HUMAN
0.655

TGAQELLR_444.3_530.3
893
GELS_HUMAN
0.655

VSEADSSNADWVTK_754.9_533.3
964
CFAB_HUMAN
0.655

VVGGLVALR_442.3_685.4
5
FA12_HUMAN
0.655

DISEVVTPR_508.3_787.4
85
CFAB_HUMAN
0.652

IHPSYTNYR_575.8_598.3
894
PSG2_HUMAN
0.652

VSFSSPLVAISGVALR_802.0_602.4
889
PAPP1_HUMAN
0.652

YNQLLR_403.7_529.3
875
ENOA_HUMAN
0.652

ALQDQLVLVAAK_634.9_956.6
890
ANGT_HUMAN
0.650

IHPSYTNYR_575.8_813.4
894
PSG2_HUMAN
0.650

TFLTVYWTPER_706.9_401.2
815
ICAM1_HUMAN
0.650

VQEVLLK_414.8_601.4
837
HYOU1_HUMAN
0.650

GDTYPAELYITGSILR_885.0_274.1
43
F13B_HUMAN
0.647

GVTGYFTFNLYLK_508.3_260.2
848
PSG5_HUMAN
0.647

SLDFTELDVAAEK_719.4_316.2
97
ANGT_HUMAN
0.647

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4
38
SHBG_HUMAN
0.647

YEFLNGR_449.7_293.1
124
PLMN_HUMAN
0.647

AQPVQVAEGSEPDGFWEALGGK_758.0_623.4
895
GELS_HUMAN
0.644

FLNWIK_410.7_561.3
835
HABP2_HUMAN
0.644

IAPQLSTEELVSLGEK_857.5_533.3
56
AFAM_HUMAN
0.644

NTVISVNPSTK_580.3_732.4
68
VCAM1_HUMAN
0.644

SFEGLGQLEVLTLDHNQLQEVK_833.1_503.3
896
ALS_HUMAN
0.644

TFLTVYWTPER_706.9_502.3
815
ICAM1_HUMAN
0.644

AGFAGDDAPR_488.7_701.3
874
ACTB_HUMAN
0.641

AIGLPEELIQK_605.86_355.2
813
FABPL_HUMAN
0.641

DISEVVTPR_508.3_472.3
85
CFAB_HUMAN
0.641

DPTFIPAPIQAK_433.2_556.3
891
ANGT_HUMAN
0.641

ENPAVIDFELAPIVDLVR_670.7_811.5
831
CO6_HUMAN
0.641

FAFNLYR_465.8_565.3
94
HEP2_HUMAN
0.641

IAPQLSTEELVSLGEK_857.5_333.2
56
AFAM_HUMAN
0.641

TNTNEFLIDVDK_704.85_849.5
812
TF_HUMAN
0.639

DVLLLVHNLPQNLTGHIWYK_791.8_883.0
805
PSG7_HUMAN
0.638

LDGSTHLNIFFAK_488.3_739.4
887
PAPP1_HUMAN
0.638

LPDTPQGLLGEAR_683.87_940.5
811
EGLN_HUMAN
0.638

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_768.5
38
SHBG_HUMAN
0.638

ALALPPLGLAPLLNLWAKPQGR_770.5_457.3
801
SHBG_HUMAN
0.635

LPNNVLQEK_527.8_844.5
46
AFAM_HUMAN
0.635

QINSYVK_426.2_496.3
897
CBG_HUMAN
0.635

QINSYVK_426.2_610.3
897
CBG_HUMAN
0.635

TGAQELLR_444.3_658.4
893
GELS_HUMAN
0.635

TLEAQLTPR_514.8_685.4
87
HEP2_HUMAN
0.635

WILTAAHTLYPK_471.9_621.4
898
C1R_HUMAN
0.635

SEPRPGVLLR_375.2_454.3
816
FA7_HUMAN
0.632

AGFAGDDAPR_488.7_630.3
874
ACTB_HUMAN
0.632

DFNQFSSGEK_386.8_333.2
839
FETA_HUMAN
0.632

DVLLLVHNLPQNLTGHIWYK_791.8_310.2
805
PSG7_HUMAN
0.632

NKPGVYTDVAYYLAWIR_677.0_545.3
67
FA12_HUMAN
0.632

SEYGAALAWEK_612.8_788.4
867
CO6_HUMAN
0.632

YNSQLLSFVR_613.8_734.5
827
TFR1_HUMAN
0.632

ALVLELAK_428.8_672.4
872
INHBE_HUMAN
0.630

ENPAVIDFELAPIVDLVR_670.7_601.4
831
CO6_HUMAN
0.630

NNQLVAGYLQGPNVNLEEK_700.7_999.5
822
IL1RA_HUMAN
0.630

WGAAPYR_410.7_577.3
63
PGRP2_HUMAN
0.630

HELTDEELQSLFTNFANVVDK_817.1_854.4
834
AFAM_HUMAN
0.627

AKPALEDLR_506.8_288.2
899
APOA1_HUMAN
0.624

AVLHIGEK_289.5_348.7
855
THBG_HUMAN
0.624

EDTPNSVWEPAK_686.8_630.3
40
C1S_HUMAN
0.624

SPELQAEAK_486.8_788.4
2
APOA2_HUMAN
0.624

YENYTSSFFIR_713.8_756.4
856
IL12B_HUMAN
0.624

NEIVFPAGILQAPFYTR_968.5_456.2
841
ECE1_HUMAN
0.621

TAVTANLDIR_537.3_288.2
826
CHL1_HUMAN
0.621

WWGGQPLWITATK_772.4_373.2
886
ENPP2_HUMAN
0.621

AVDIPGLEAATPYR_736.9_399.2
900
TENA_HUMAN
0.618

ALNFGGIGVVVGHELTHAFDDQGR_837.1_360.2
34
ECE1_HUMAN
0.618

ALNHLPLEYNSALYSR_621.0_696.4
52
CO6_HUMAN
0.618

FNAVLTNPQGDYDTSTGK_964.5_262.1
51
C1QC_HUMAN
0.618

GDTYPAELYITGSILR_885.0_922.5
43
F13B_HUMAN
0.618

IAQYYYTFK_598.8_884.4
25
F13B_HUMAN
0.618

LEQGENVFLQATDK_796.4_822.4
70
C1QB_HUMAN
0.618

LSITGTYDLK_555.8_696.4
901
A1AT_HUMAN
0.618

NTVISVNPSTK_580.3_845.5
68
VCAM1_HUMAN
0.618

TLAFVR_353.7_492.3
806
FA7_HUMAN
0.618

TLEAQLTPR_514.8_814.4
87
HEP2_HUMAN
0.618

TQIDSPLSGK_523.3_703.4
838
VCAM1_HUMAN
0.618

AVLHIGEK_289.5_292.2
855
THBG_HUMAN
0.615

FLIPNASQAESK_652.8_931.4
902
1433Z_HUMAN
0.615

FNAVLTNPQGDYDTSTGK_964.5_333.2
51
C1QC_HUMAN
0.615

FQSVFTVTR_542.8_722.4
903
C1QC_HUMAN
0.615

INPASLDK_429.2_630.4
904
C163A_HUMAN
0.615

IPKPEASFSPR_410.2_506.3
905
ITIH4_HUMAN
0.615

ITQDAQLK_458.8_803.4
906
CBG_HUMAN
0.615

TSYQVYSK_488.2_397.2
907
C163A_HUMAN
0.615

WGAAPYR_410.7_634.3
63
PGRP2_HUMAN
0.615

AVDIPGLEAATPYR_736.9_286.1
900
TENA_HUMAN
0.613

DVLLLVHNLPQNLPGYFWYK_810.4_328.2
908
PSG9_HUMAN
0.613

SFEGLGQLEVLTLDHNQLQEVK_833.1_662.8
896
ALS_HUMAN
0.613

TASDFITK_441.7_710.4
115
GELS_HUMAN
0.613

AGPLQAR_356.7_584.4
909
DEF4_HUMAN
0.610

DYWSTVK_449.7_347.2
28
APOC3_HUMAN
0.610

FQSVFTVTR_542.79_623.4
903
C1QC_HUMAN
0.610

FQSVFTVTR_542.79_722.4
903
C1QC_HUMAN
0.610

SYTITGLQPGTDYK_772.4_352.2
114
FINC_HUMAN
0.610

FQLSETNR_497.8_476.3
910
PSG2_HUMAN
0.607

IPKPEASFSPR_410.2_359.2
905
ITIH4_HUMAN
0.607

LIEIANHVDK_384.6_498.3
911
ADA12_HUMAN
0.607

SILFLGK_389.2_201.1
861
THBG_HUMAN
0.607

SLLQPNK_400.2_358.2
98
CO8A_HUMAN
0.607

VFQFLEK_455.8_811.4
912
CO5_HUMAN
0.607

VPGLYYFTYHASSR_554.3_720.3
913
C1QB_HUMAN
0.607

VSAPSGTGHLPGLNPL_506.3_860.5
914
PSG3_HUMAN
0.607

AGITIPR_364.2_486.3
915
IL17_HUMAN
0.604

FLIPNASQAESK_652.8_261.2
902
1433Z_HUMAN
0.604

FQSVFTVTR_542.8_623.4
903
C1QC_HUMAN
0.604

IRPFFPQQ_516.79_661.4
916
FIBB_HUMAN
0.604

LLELTGPK_435.8_644.4
840
A1BG_HUMAN
0.604

SETEIHQGFQHLHQLFAK_717.4_318.1
917
CBG_HUMAN
0.604

SILFLGK_389.2_577.4
861
THBG_HUMAN
0.604

STLFVPR_410.2_518.3
847
PEPD_HUMAN
0.604

TEQAAVAR_423.2_487.3
918
FA12_HUMAN
0.604

EDTPNSVWEPAK_686.8_315.2
40
C1S_HUMAN
0.601

FLNWIK_410.7_560.3
835
HABP2_HUMAN
0.601

ITQDAQLK_458.8_702.4
906
CBG_HUMAN
0.601

SPELQAEAK_486.8_659.4
2
APOA2_HUMAN
0.601

TLLPVSKPEIR_418.3_288.2
919
CO5_HUMAN
0.601

VFQFLEK_455.8_276.2
912
CO5_HUMAN
0.601

YGLVTYATYPK_638.3_843.4
84
CFAB_HUMAN
0.601

TABLE 14

Univariate AUC values early-middle combined windows

SEQ ID

Transition
NO:
Protein
AUC

LDFHFSSDR_375.2_611.3
6
INHBC_HUMAN
0.809

ETLLQDFR_511.3_565.3
9
AMBP_HUMAN
0.802

HHGPTITAK_321.2_275.1
33
AMBP_HUMAN
0.801

ATVVYQGER_511.8_652.3
10
APOH_HUMAN
0.799

ETLLQDFR_511.3_322.2
9
AMBP_HUMAN
0.796

ATVVYQGER_511.8_751.4
10
APOH_HUMAN
0.795

HHGPTITAK_321.2_432.3
33
AMBP_HUMAN
0.794

TVQAVLTVPK_528.3_855.5
7
PEDF_HUMAN
0.791

AHYDLR_387.7_566.3
42
FETUA_HUMAN
0.789

TVQAVLTVPK_528.3_428.3
7
PEDF_HUMAN
0.787

FICPLTGLWPINTLK_887.0_685.4
804
APOH_HUMAN
0.785

VNHVTLSQPK_374.9_244.2
3
B2MG_HUMAN
0.783

AHYDLR_387.7_288.2
42
FETUA_HUMAN
0.781

ELIEELVNITQNQK_557.6_618.3
807
IL13_HUMAN
0.780

FSVVYAK_407.2_381.2
1
FETUA_HUMAN
0.777

IQTHSTTYR_369.5_627.3
59
F13B_HUMAN
0.777

DTDTGALLFIGK_625.8_818.5
799
PEDF_HUMAN
0.774

FICPLTGLWPINTLK_887.0_756.9
804
APOH_HUMAN
0.773

DTDTGALLFIGK_625.8_217.1
799
PEDF_HUMAN
0.771

FSVVYAK_407.2_579.4
1
FETUA_HUMAN
0.770

IQTHSTTYR_369.5_540.3
59
F13B_HUMAN
0.769

LDFHFSSDR_375.2_464.2
6
INHBC_HUMAN
0.769

TLAFVR_353.7_274.2
806
FA7_HUMAN
0.769

FLYHK_354.2_447.2
802
AMBP_HUMAN
0.766

VNHVTLSQPK_374.9_459.3
3
B2MG_HUMAN
0.762

AIGLPEELIQK_605.86_856.5
813
FABPL_HUMAN
0.752

FLYHK_354.2_284.2
802
AMBP_HUMAN
0.752

ELIEELVNITQNQK_557.6_517.3
807
IL13_HUMAN
0.751

ETPEGAEAKPWYEPIYLGGVFQLEK_951.14_877.5
878
TNFA_HUMAN
0.751

HFQNLGK_422.2_527.2
50
AFAM_HUMAN
0.749

LIQDAVTGLTVNGQITGDK_972.0_640.4
844
ITIH3_HUMAN
0.749

LIQDAVTGLTVNGQITGDK_972.0_798.4
844
ITIH3_HUMAN
0.747

IAPQLSTEELVSLGEK_857.5_533.3
56
AFAM_HUMAN
0.745

HFQNLGK_422.2_285.1
50
AFAM_HUMAN
0.740

NNQLVAGYLQGPNVNLEEK_700.7_999.5
822
IL1RA_HUMAN
0.738

VVESLAK_373.2_646.4
809
IBP1_HUMAN
0.738

IAPQLSTEELVSLGEK_857.5_333.2
56
AFAM_HUMAN
0.737

IALGGLLFPASNLR_481.3_657.4
55
SHBG_HUMAN
0.734

ALALPPLGLAPLLNLWAKPQGR_770.5_256.2
801
SHBG_HUMAN
0.731

ELPQSIVYK_538.8_417.7
808
FBLN3_HUMAN
0.724

TFLTVYWTPER_706.9_401.2
815
ICAM1_HUMAN
0.723

GVTGYFTFNLYLK_508.3_260.2
848
PSG5_HUMAN
0.717

DVLLLVHNLPQNLTGHIWYK_791.8_310.2
805
PSG7_HUMAN
0.716

WNFAYWAAHQPWSR_607.3_545.3
825
PRG2_HUMAN
0.716

YTTEIIK_434.2_603.4
39
C1R_HUMAN
0.716

YTTEIIK_434.2_704.4
39
C1R_HUMAN
0.716

DIPHWLNPTR_416.9_600.3
880
PAPP1_HUMAN
0.715

WNFAYWAAHQPWSR_607.3_673.3
825
PRG2_HUMAN
0.715

IALGGLLFPASNLR_481.3_412.3
55
SHBG_HUMAN
0.713

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4
38
SHBG_HUMAN
0.713

GFQALGDAADIR_617.3_717.4
11
TIMP1_HUMAN
0.711

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_768.5
38
SHBG_HUMAN
0.711

DVLLLVHNLPQNLTGHIWYK_791.8_883.0
805
PSG7_HUMAN
0.708

YGIEEHGK_311.5_599.3
810
CXA1_HUMAN
0.706

AEHPTWGDEQLFQTTR_639.3_765.4
814
PGH1_HUMAN
0.705

VVESLAK_373.2_547.3
809
IBP1_HUMAN
0.705

DADPDTFFAK_563.8_825.4
49
AFAM_HUMAN
0.704

DAQYAPGYDK_564.3_813.4
83
CFAB_HUMAN
0.704

GFQALGDAADIR_617.3_288.2
11
TIMP1_HUMAN
0.704

AEHPTWGDEQLFQTTR_639.3_569.3
814
PGH1_HUMAN
0.702

NFPSPVDAAFR_610.8_959.5
824
HEMO_HUMAN
0.702

ALALPPLGLAPLLNLWAKPQGR_770.5_457.3
801
SHBG_HUMAN
0.701

GVTGYFTFNLYLK_508.3_683.9
848
PSG5_HUMAN
0.701

DFNQFSSGEK_386.8_189.1
839
FETA_HUMAN
0.699

GDTYPAELYITGSILR_885.0_274.1
43
F13B_HUMAN
0.699

TLEAQLTPR_514.8_685.4
87
HEP2_HUMAN
0.699

VEHSDLSFSK_383.5_468.2
800
B2MG_HUMAN
0.699

DAQYAPGYDK_564.3_315.1
83
CFAB_HUMAN
0.698

VSEADSSNADWVTK_754.9_347.2
964
CFAB_HUMAN
0.698

ILPSVPK_377.2_244.2
846
PGH1_HUMAN
0.695

DADPDTFFAK_563.8_302.1
49
AFAM_HUMAN
0.694

EVFSKPISWEELLQ_852.9_260.2
803
FA40A_HUMAN
0.694

HTLNQIDEVK_598.8_958.5
48
FETUA_HUMAN
0.694

NFPSPVDAAFR_610.8_775.4
824
HEMO_HUMAN
0.694

VSFSSPLVAISGVALR_802.0_715.4
889
PAPP1_HUMAN
0.694

TLAFVR_353.7_492.3
806
FA7_HUMAN
0.693

ILPSVPK_377.2_227.2
846
PGH1_HUMAN
0.691

LLEVPEGR_456.8_356.2
31
C1S_HUMAN
0.691

TLEAQLTPR_514.8_814.4
87
HEP2_HUMAN
0.691

IPSNPSHR_303.2_610.3
819
FBLN3_HUMAN
0.690

LPNNVLQEK_527.8_730.4
46
AFAM_HUMAN
0.690

NCSFSIIYPVVIK_770.4_555.4
818
CRHBP_HUMAN
0.690

NCSFSIIYPVVIK_770.4_831.5
818
CRHBP_HUMAN
0.690

VEHSDLSFSK_383.5_234.1
800
B2MG_HUMAN
0.690

ALDLSLK_380.2_185.1
817
ITIH3_HUMAN
0.688

IHWESASLLR_606.3_437.2
869
CO3_HUMAN
0.688

IPSNPSHR_303.2_496.3
819
FBLN3_HUMAN
0.688

LDGSTHLNIFFAK_488.3_852.5
887
PAPP1_HUMAN
0.687

QGHNSVFLIK_381.6_260.2
845
HEMO_HUMAN
0.687

AVLHIGEK_289.5_348.7
855
THBG_HUMAN
0.686

VSEADSSNADWVTK_754.9_533.3
964
CFAB_HUMAN
0.686

TNTNEFLIDVDK_704.85_849.5
812
TF_HUMAN
0.685

AVLHIGEK_289.5_292.2
855
THBG_HUMAN
0.683

HTLNQIDEVK_598.8_951.5
48
FETUA_HUMAN
0.683

VSFSSPLVAISGVALR_802.0_602.4
889
PAPP1_HUMAN
0.683

IAQYYYTFK_598.8_395.2
25
F13B_HUMAN
0.681

ALDLSLK_380.2_575.3
817
ITIH3_HUMAN
0.680

LLEVPEGR_456.8_686.4
31
C1S_HUMAN
0.680

QGHNSVFLIK_381.6_520.4
845
HEMO_HUMAN
0.680

SEPRPGVLLR_375.2_454.3
816
FA7_HUMAN
0.680

SFRPFVPR_335.9_272.2
850
LBP_HUMAN
0.680

AFQVWSDVTPLR_709.88_385.3
884
MMP2_HUMAN
0.679

FAFNLYR_465.8_712.4
94
HEP2_HUMAN
0.679

IAQYYYTFK_598.8_884.4
25
F13B_HUMAN
0.679

ITGFLKPGK_320.9_429.3
833
LBP_HUMAN
0.679

EHSSLAFWK_552.8_838.4
823
APOH_HUMAN
0.677

GLQYAAQEGLLALQSELLR_1037.1_858.5
857
LBP_HUMAN
0.676

YYLQGAK_421.7_327.1
832
ITIH4_HUMAN
0.676

LIENGYFHPVK_439.6_627.4
66
F13B_HUMAN
0.675

SFRPFVPR_335.9_635.3
850
LBP_HUMAN
0.675

AALAAFNAQNNGSNFQLEEISR_789.1_746.4
821
FETUA_HUMAN
0.674

ITGFLKPGK_320.9_301.2
833
LBP_HUMAN
0.673

VQEVLLK_414.8_373.3
837
HYOU1_HUMAN
0.673

YNSQLLSFVR_613.8_508.3
827
TFR1_HUMAN
0.673

EHSSLAFWK_552.8_267.1
823
APOH_HUMAN
0.672

FAFNLYR_465.8_565.3
94
HEP2_HUMAN
0.672

GDTYPAELYITGSILR_885.0_922.5
43
F13B_HUMAN
0.672

ITLPDFTGDLR_624.3_920.5
852
LBP_HUMAN
0.672

NSDQEIDFK_548.3_409.2
864
S10A5_HUMAN
0.672

TAVTANLDIR_537.3_802.4
826
CHL1_HUMAN
0.672

YYLQGAK_421.7_516.3
832
ITIH4_HUMAN
0.672

ITLPDFTGDLR_624.3_288.2
852
LBP_HUMAN
0.670

AIGLPEELIQK_605.86_355.2
813
FABPL_HUMAN
0.669

ALNFGGIGVVVGHELTHAFDDQGR_837.1_299.2
34
ECE1_HUMAN
0.668

AQETSGEEISK_589.8_979.5
876
IBP1_HUMAN
0.668

LPNNVLQEK_527.8_844.5
46
AFAM_HUMAN
0.668

TGISPLALIK_506.8_654.5
20
APOB_HUMAN
0.666

DFHINLFQVLPWLK_885.5_543.3
64
CFAB_HUMAN
0.665

VQEVLLK_414.8_601.4
837
HYOU1_HUMAN
0.665

YENYTSSFFIR_713.8_756.4
856
IL12B_HUMAN
0.665

CRPINATLAVEK_457.9_559.3
859
CGB1_HUMAN
0.663

LDGSTHLNIFFAK_488.3_739.4
887
PAPP1_HUMAN
0.663

TGISPLALIK_506.8_741.5
20
APOB_HUMAN
0.663

EVFSKPISWEELLQ_852.9_376.2
803
FA40A_HUMAN
0.662

SLDFTELDVAAEK_719.4_874.5
97
ANGT_HUMAN
0.662

TFLTVYWTPER_706.9_502.3
815
ICAM1_HUMAN
0.662

VRPQQLVK_484.3_609.4
866
ITIH4_HUMAN
0.662

GLQYAAQEGLLALQSELLR_1037.1_929.5
857
LBP_HUMAN
0.661

NAVVQGLEQPHGLVVHPLR_688.4_890.6
870
LRP1_HUMAN
0.661

SILFLGK_389.2_201.1
861
THBG_HUMAN
0.661

DFNQFSSGEK_386.8_333.2
839
FETA_HUMAN
0.659

IHWESASLLR_606.3_251.2
869
CO3_HUMAN
0.659

SILFLGK_389.2_577.4
861
THBG_HUMAN
0.658

SVSLPSLDPASAK_636.4_473.3
15
APOB_HUMAN
0.658

WWGGQPLWITATK_772.4_929.5
886
ENPP2_HUMAN
0.658

LNIGYIEDLK_589.3_950.5
830
PAI2_HUMAN
0.657

DFHINLFQVLPWLK_885.5_400.2
64
CFAB_HUMAN
0.657

YSHYNER_323.48_418.2
920
HABP2_HUMAN
0.657

STLFVPR_410.2_272.2
847
PEPD_HUMAN
0.656

AFQVWSDVTPLR_709.88_347.2
884
MMP2_HUMAN
0.655

FQSVFTVTR_542.8_722.4
903
C1QC_HUMAN
0.655

GPGEDFR_389.2_623.3
8
PTGDS_HUMAN
0.655

LEEHYELR_363.5_288.2
868
PAI2_HUMAN
0.655

LPDTPQGLLGEAR_683.87_427.2
811
EGLN_HUMAN
0.655

FQSVFTVTR_542.79_722.4
903
C1QC_HUMAN
0.654

FTFTLHLETPKPSISSSNLNPR_829.4_787.4
82
PSG1_HUMAN
0.654

NHYTESISVAK_624.8_252.1
888
NEUR1_HUMAN
0.654

YSHYNER_323.48_581.3
920
HABP2_HUMAN
0.654

FQSVFTVTR_542.79_623.4
903
C1QC_HUMAN
0.652

IEGNLIFDPNNYLPK_874.0_845.5
16
APOB_HUMAN
0.652

VRPQQLVK_484.3_722.4
866
ITIH4_HUMAN
0.652

WILTAAHTLYPK_471.9_621.4
898
C1R_HUMAN
0.652

ITQDAQLK_458.8_803.4
906
CBG_HUMAN
0.651

SVSLPSLDPASAK_636.4_885.5
15
APOB_HUMAN
0.651

ESDTSYVSLK_564.8_347.2
102
CRP_HUMAN
0.650

ESDTSYVSLK_564.8_696.4
102
CRP_HUMAN
0.650

FQSVFTVTR_542.8_623.4
903
C1QC_HUMAN
0.650

HELTDEELQSLFTNFANVVDK_817.1_854.4
834
AFAM_HUMAN
0.650

IEGNLIFDPNNYLPK_874.0_414.2
16
APOB_HUMAN
0.650

DIIKPDPPK_511.8_342.2
853
IL12B_HUMAN
0.648

SPELQAEAK_486.8_788.4
2
APOA2_HUMAN
0.648

VELAPLPSWQPVGK_760.9_400.3
858
ICAM1_HUMAN
0.648

AQETSGEEISK_589.8_850.4
876
IBP1_HUMAN
0.647

QTLSWTVTPK_580.8_545.3
881
PZP_HUMAN
0.647

DISEVVTPR_508.3_787.4
85
CFAB_HUMAN
0.645

DVLLLVHNLPQNLPGYFWYK_810.4_328.2
908
PSG9_HUMAN
0.645

QTLSWTVTPK_580.8_818.4
881
PZP_HUMAN
0.645

SGAQATWTELPWPHEK_613.3_510.3
865
HEMO_HUMAN
0.645

SLDFTELDVAAEK_719.4_316.2
97
ANGT_HUMAN
0.645

AVGYLITGYQR_620.8_523.3
879
PZP_HUMAN
0.644

DISEVVTPR_508.3_472.3
85
CFAB_HUMAN
0.644

FLNWIK_410.7_560.3
835
HABP2_HUMAN
0.644

IQHPFTVEEFVLPK_562.0_861.5
882
PZP_HUMAN
0.644

ALQDQLVLVAAK_634.9_289.2
890
ANGT_HUMAN
0.643

AVGYLITGYQR_620.8_737.4
879
PZP_HUMAN
0.643

FLNWIK_410.7_561.3
835
HABP2_HUMAN
0.643

LEQGENVFLQATDK_796.4_822.4
70
C1QB_HUMAN
0.643

LSITGTYDLK_555.8_797.4
901
A1AT_HUMAN
0.641

SEPRPGVLLR_375.2_654.4
816
FA7_HUMAN
0.641

VPGLYYFTYHASSR_554.3_720.3
913
C1QB_HUMAN
0.641

APLTKPLK_289.9_357.2
110
CRP_HUMAN
0.639

FNAVLTNPQGDYDTSTGK_964.5_333.2
51
C1QC_HUMAN
0.639

IQHPFTVEEFVLPK_562.0_603.4
882
PZP_HUMAN
0.639

LSSPAVITDK_515.8_743.4
78
PLMN_HUMAN
0.639

ALNFGGIGVVVGHELTHAFDDQGR_837.1_360.2
34
ECE1_HUMAN
0.637

FNAVLTNPQGDYDTSTGK_964.5_262.1
51
C1QC_HUMAN
0.637

LLELTGPK_435.8_227.2
840
A1BG_HUMAN
0.637

YNSQLLSFVR_613.8_734.5
827
TFR1_HUMAN
0.636

DLYHYITSYVVDGEIIIYGPAYSGR_955.5_707.3
828
PSG1_HUMAN
0.634

GPGEDFR_389.2_322.2
8
PTGDS_HUMAN
0.634

IHPSYTNYR_575.8_813.4
894
PSG2_HUMAN
0.634

SGAQATWTELPWPHEK_613.3_793.4
865
HEMO_HUMAN
0.634

SPELQAEAK_486.8_659.4
2
APOA2_HUMAN
0.634

ALQDQLVLVAAK_634.9_956.6
890
ANGT_HUMAN
0.633

ITENDIQIALDDAK_779.9_632.3
18
APOB_HUMAN
0.632

ITQDAQLK_458.8_702.4
906
CBG_HUMAN
0.632

LSSPAVITDK_515.8_830.5
78
PLMN_HUMAN
0.632

SLLQPNK_400.2_358.2
98
CO8A_HUMAN
0.632

VPGLYYFTYHASSR_554.3_420.2
913
C1QB_HUMAN
0.632

YGLVTYATYPK_638.3_843.4
84
CFAB_HUMAN
0.632

AGITIPR_364.2_486.3
915
IL17_HUMAN
0.630

IHPSYTNYR_575.8_598.3
894
PSG2_HUMAN
0.630

QINSYVK_426.2_610.3
897
CBG_HUMAN
0.630

SSNNPHSPIVEEFQVPYNK_729.4_261.2
4
C1S_HUMAN
0.630

ANDQYLTAAALHNLDEAVK_686.3_317.2
921
IL1A_HUMAN
0.629

ATWSGAVLAGR_544.8_730.4
922
A1BG_HUMAN
0.629

TLPFSR_360.7_506.3
820
LYAM1_HUMAN
0.629

TYLHTYESEI_628.3_515.3
923
ENPP2_HUMAN
0.629

EFDDDTYDNDIALLQLK_1014.48_388.3
842
TPA_HUMAN
0.627

EFDDDTYDNDIALLQLK_1014.48_501.3
842
TPA_HUMAN
0.627

VTGLDFIPGLHPILTLSK_641.04_771.5
871
LEP_HUMAN
0.627

HVVQLR_376.2_614.4
862
IL6RA_HUMAN
0.626

LIENGYFHPVK_439.6_343.2
66
F13B_HUMAN
0.626

LLELTGPK_435.8_644.4
840
A1BG_HUMAN
0.626

YEVQGEVFTKPQLWP_911.0_392.2
108
CRP_HUMAN
0.626

DPNGLPPEAQK_583.3_497.2
14
RET4_HUMAN
0.625

FTFTLHLETPKPSISSSNLNPR_829.4_874.4
82
PSG1_HUMAN
0.625

YGLVTYATYPK_638.3_334.2
84
CFAB_HUMAN
0.625

APLTKPLK_289.9_398.8
110
CRP_HUMAN
0.623

DSPSVWAAVPGK_607.31_301.2
877
PROF1_HUMAN
0.623

ENPAVIDFELAPIVDLVR_670.7_811.5
831
CO6_HUMAN
0.623

ILILPSVTR_506.3_559.3
679
PSGx_HUMAN
0.623

SFEGLGQLEVLTLDHNQLQEVK_833.1_503.3
896
ALS_HUMAN
0.623

TSESGELHGLTTEEEFVEGIYK_819.06_310.2
44
TTHY_HUMAN
0.623

AGITIPR_364.2_272.2
915
IL17_HUMAN
0.622

DPDQTDGLGLSYLSSHIANVER_796.4_328.1
101
GELS_HUMAN
0.622

ATWSGAVLAGR_544.8_643.4
922
A1BG_HUMAN
0.620

HVVQLR_376.2_515.3
862
IL6RA_HUMAN
0.620

QINSYVK_426.2_496.3
897
CBG_HUMAN
0.620

TLFIFGVTK_513.3_215.1
676
PSG4_HUMAN
0.620

YEVQGEVFTKPQLWP_911.0_293.1
108
CRP_HUMAN
0.620

YYGYTGAFR_549.3_771.4
924
TRFL_HUMAN
0.620

AALAAFNAQNNGSNFQLEEISR_789.1_633.3
821
FETUA_HUMAN
0.619

ALNHLPLEYNSALYSR_621.0_696.4
52
CO6_HUMAN
0.619

EDTPNSVWEPAK_686.8_630.3
40
C1S_HUMAN
0.619

NNQLVAGYLQGPNVNLEEK_700.7_357.2
822
IL1RA_HUMAN
0.619

ELANTIK_394.7_475.3
925
S10AC_HUMAN
0.618

ENPAVIDFELAPIVDLVR_670.7_601.4
831
CO6_HUMAN
0.618

GEVTYTTSQVSK_650.3_913.5
926
EGLN_HUMAN
0.616

NEIWYR_440.7_637.4
883
FA12_HUMAN
0.616

TLFIFGVTK_513.3_811.5
676
PSG4_HUMAN
0.616

DLYHYITSYVVDGEIIIYGPAYSGR_955.5_650.3
828
PSG1_HUMAN
0.615

DPTFIPAPIQAK_433.2_556.3
891
ANGT_HUMAN
0.615

VELAPLPSWQPVGK_760.9_342.2
858
ICAM1_HUMAN
0.615

DPNGLPPEAQK_583.3_669.4
14
RET4_HUMAN
0.614

GIVEECCFR_585.3_900.3
860
IGF2_HUMAN
0.614

ITENDIQIALDDAK_779.9_873.5
18
APOB_HUMAN
0.614

LSETNR_360.2_330.2
892
PSG1_HUMAN
0.614

LSNENHGIAQR_413.5_519.8
927
ITIH2_HUMAN
0.614

YEFLNGR_449.7_293.1
124
PLMN_HUMAN
0.614

AEIEYLEK_497.8_552.3
836
LYAM1_HUMAN
0.612

GIVEECCFR_585.3_771.3
860
IGF2_HUMAN
0.612

ILDDLSPR_464.8_587.3
103
ITIH4_HUMAN
0.611

IRPHTFTGLSGLR_485.6_545.3
928
ALS_HUMAN
0.611

VVGGLVALR_442.3_784.5
5
FA12_HUMAN
0.609

LEEHYELR_363.5_417.2
868
PAI2_HUMAN
0.609

LSNENHGIAQR_413.5_544.3
927
ITIH2_HUMAN
0.609

TYLHTYESEI_628.3_908.4
923
ENPP2_HUMAN
0.609

VLEPTLK_400.3_587.3
123
VTDB_HUMAN
0.609

ILILPSVTR_506.3_785.5
679
PSGx_HUMAN
0.608

TAVTANLDIR_537.3_288.2
826
CHL1_HUMAN
0.608

WWGGQPLWITATK_772.4_373.2
886
ENPP2_HUMAN
0.607

ALVLELAK_428.8_672.4
872
INHBE_HUMAN
0.605

EAQLPVIENK_570.8_329.2
929
PLMN_HUMAN
0.605

QRPPDLDTSSNAVDLLFFTDESGDSR_961.5_866.3
930
C1R_HUMAN
0.605

TDAPDLPEENQAR_728.3_613.3
121
CO5_HUMAN
0.605

TLPFSR_360.7_409.2
820
LYAM1_HUMAN
0.605

VQTAHFK_277.5_502.3
931
CO8A_HUMAN
0.605

ANLINNIFELAGLGK_793.9_299.2
932
LCAP_HUMAN
0.604

FQLPGQK_409.2_275.1
47
PSG1_HUMAN
0.604

NTVISVNPSTK_580.3_845.5
68
VCAM1_HUMAN
0.604

VLEPTLK_400.3_458.3
123
VTDB_HUMAN
0.604

YWGVASFLQK_599.8_849.5
17
RET4_HUMAN
0.604

AGPLQAR_356.7_584.4
909
DEF4_HUMAN
0.602

AHQLAIDTYQEFEETYIPK_766.0_521.3
933
CSH_HUMAN
0.602

DLHLSDVFLK_396.2_366.2
77
CO6_HUMAN
0.602

SSNNPHSPIVEEFQVPYNK_729.4_521.3
4
C1S_HUMAN
0.602

YWGVASFLQK_599.8_350.2
17
RET4_HUMAN
0.602

AGPLQAR_356.7_487.3
909
DEF4_HUMAN
0.601

ALNHLPLEYNSALYSR_621.0_538.3
52
CO6_HUMAN
0.601

EAQLPVIENK_570.8_699.4
929
PLMN_HUMAN
0.601

EDTPNSVWEPAK_686.8_315.2
40
C1S_HUMAN
0.601

NTVISVNPSTK_580.3_732.4
68
VCAM1_HUMAN
0.601

TABLE 15

Univariate AUC values middle-late combined windows

SEQ ID

Transition
NO:
Protein
AUC

GDTYPAELYITGSILR_885.0_274.1
43
F13B_HUMAN
0.7750

TVQAVLTVPK_528.3_428.3
7
PEDF_HUMAN
0.7667

IQTHSTTYR_369.5_627.3
59
F13B_HUMAN
0.7667

DVLLLVHNLPQNLTGHIWYK_791.8_310.2
805
PSG7_HUMAN
0.7667

IQTHSTTYR_369.5_540.3
59
F13B_HUMAN
0.7646

ALALPPLGLAPLLNLWAKPQGR_770.5_256.2
801
SHBG_HUMAN
0.7646

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_768.5
38
SHBG_HUMAN
0.7625

VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4
38
SHBG_HUMAN
0.7625

TVQAVLTVPK_528.3_855.5
7
PEDF_HUMAN
0.7604

GDTYPAELYITGSILR_885.0_922.5
43
F13B_HUMAN
0.7604

DVLLLVHNLPQNLTGHIWYK_791.8_883.0
805
PSG7_HUMAN
0.7604

TLPFSR_360.7_506.3
820
LYAM1_HUMAN
0.7563

ALALPPLGLAPLLNLWAKPQGR_770.5_457.3
801
SHBG_HUMAN
0.7563

IALGGLLFPASNLR_481.3_657.4
55
SHBG_HUMAN
0.7542

IALGGLLFPASNLR_481.3_412.3
55
SHBG_HUMAN
0.7542

DTDTGALLFIGK_625.8_217.1
799
PEDF_HUMAN
0.7500

QGFGNVATNTDGK_654.81_706.3
854
FIBB_HUMAN
0.7438

ETLLQDFR_511.3_565.3
9
AMBP_HUMAN
0.7438

ETLLQDFR_511.3_322.2
9
AMBP_HUMAN
0.7417

IAQYYYTFK_598.8_884.4
25
F13B_HUMAN
0.7396

DTDTGALLFIGK_625.8_818.5
799
PEDF_HUMAN
0.7396

AEIEYLEK_497.8_552.3
836
LYAM1_HUMAN
0.7396

LDFHFSSDR_375.2_611.3
6
INHBC_HUMAN
0.7354

YQISVNK_426.2_560.3
829
FIBB_HUMAN
0.7333

IAPQLSTEELVSLGEK_857.5_533.3
56
AFAM_HUMAN
0.7313

EVFSKPISWEELLQ_852.9_376.2
803
FA40A_HUMAN
0.7292

TLAFVR_353.7_274.2
806
FA7_HUMAN
0.7229

HHGPTITAK_321.2_275.1
33
AMBP_HUMAN
0.7229

SLQAFVAVAAR_566.8_487.3
873
IL23A_HUMAN
0.7208

IAQYYYTFK_598.8_395.2
25
F13B_HUMAN
0.7208

EVFSKPISWEELLQ_852.9_260.2
803
FA40A_HUMAN
0.7208

DPNGLPPEAQK_583.3_669.4
14
RET4_HUMAN
0.7208

DPNGLPPEAQK_583.3_497.2
14
RET4_HUMAN
0.7167

VEHSDLSFSK_383.5_468.2
800
B2MG_HUMAN
0.7146

YQISVNK_426.2_292.1
829
FIBB_HUMAN
0.7125

TLAFVR_353.7_492.3
806
FA7_HUMAN
0.7125

IAPQLSTEELVSLGEK_857.5_333.2
56
AFAM_HUMAN
0.7125

AEIEYLEK_497.8_389.2
836
LYAM1_HUMAN
0.7125

YWGVASFLQK_599.8_849.5
17
RET4_HUMAN
0.7104

TLPFSR_360.7_409.2
820
LYAM1_HUMAN
0.7104

HFQNLGK_422.2_527.2
50
AFAM_HUMAN
0.7104

TQILEWAAER_608.8_761.4
863
EGLN_HUMAN
0.7083

HFQNLGK_422.2_285.1
50
AFAM_HUMAN
0.7063

FTFTLHLETPKPSISSSNLNPR_829.4_787.4
82
PSG1_HUMAN
0.7063

DPDQTDGLGLSYLSSHIANVER_796.4_456.2
101
GELS_HUMAN
0.7063

DADPDTFFAK_563.8_825.4
49
AFAM_HUMAN
0.7042

YWGVASFLQK_599.8_350.2
17
RET4_HUMAN
0.7021

DADPDTFFAK_563.8_302.1
49
AFAM_HUMAN
0.7021

HHGPTITAK_321.2_432.3
33
AMBP_HUMAN
0.6979

NTVISVNPSTK_580.3_845.5
68
VCAM1_HUMAN
0.6958

FLYHK_354.2_447.2
802
AMBP_HUMAN
0.6958

FICPLTGLWPINTLK_887.0_685.4
804
APOH_HUMAN
0.6958

FTFTLHLETPKPSISSSNLNPR_829.4_874.4
82
PSG1_HUMAN
0.6938

FLYHK_354.2_284.2
802
AMBP_HUMAN
0.6938

EALVPLVADHK_397.9_390.2
849
HGFA_HUMAN
0.6938

LNIGYIEDLK_589.3_837.4
830
PAI2_HUMAN
0.6917

QGFGNVATNTDGK_654.81_319.2
854
FIBB_HUMAN
0.6896

EALVPLVADHK_397.9_439.8
849
HGFA_HUMAN
0.6896

TNTNEFLIDVDK_704.85_849.5
812
TF_HUMAN
0.6875

DTYVSSFPR_357.8_272.2
934
TCEA1_HUMAN
0.6813

VNHVTLSQPK_374.9_244.2
3
B2MG_HUMAN
0.6771

GPGEDFR_389.2_623.3
8
PTGDS_HUMAN
0.6771

GEVTYTTSQVSK_650.3_913.5
926
EGLN_HUMAN
0.6771

GEVTYTTSQVSK_650.3_750.4
926
EGLN_HUMAN
0.6771

FICPLTGLWPINTLK_887.0_756.9
804
APOH_HUMAN
0.6771

YEFLNGR_449.7_606.3
124
PLMN_HUMAN
0.6750

YEFLNGR_449.7_293.1
124
PLMN_HUMAN
0.6750

TLFIFGVTK_513.3_215.1
676
PSG4_HUMAN
0.6750

LNIGYIEDLK_589.3_950.5
830
PAI2_HUMAN
0.6750

LLELTGPK_435.8_227.2
840
A1BG_HUMAN
0.6750

TPSAAYLWVGTGASEAEK_919.5_849.4
935
GELS_HUMAN
0.6729

FQLPGQK_409.2_275.1
47
PSG1_HUMAN
0.6729

ELIEELVNITQNQK_557.6_618.3
807
IL13_HUMAN
0.6729

DLYHYITSYVVDGEIIIYGPAYSGR_955.5_707.3
828
PSG1_HUMAN
0.6729

AHYDLR_387.7_566.3
42
FETUA_HUMAN
0.6729

LLEVPEGR_456.8_356.2
31
C1S_HUMAN
0.6708

TLFIFGVTK_513.3_811.5
676
PSG4_HUMAN
0.6688

FQLPGQK_409.2_429.2
47
PSG1_HUMAN
0.6667

DLYHYITSYVVDGEIIIYGPAYSGR_955.5_650.3
828
PSG1_HUMAN
0.6667

YYLQGAK_421.7_516.3
832
ITIH4_HUMAN
0.6646

FSVVYAK_407.2_579.4
1
FETUA_HUMAN
0.6646

EQLGEFYEALDCLR_871.9_747.4
936
A1AG1_HUMAN
0.6646

LDFHFSSDR_375.2_464.2
6
INHBC_HUMAN
0.6625

ALNHLPLEYNSALYSR_621.0_696.4
52
CO6_HUMAN
0.6625

YYLQGAK_421.7_327.1
832
ITIH4_HUMAN
0.6604

YTTEIIK_434.2_704.4
39
C1R_HUMAN
0.6604

VEHSDLSFSK_383.5_234.1
800
B2MG_HUMAN
0.6604

SNPVTLNVLYGPDLPR_585.7_654.4
937
PSG6_HUMAN
0.6604

LWAYLTIQELLAK_781.5_300.2
938
ITIH1_HUMAN
0.6604

FSLVSGWGQLLDR_493.3_403.2
843
FA7_HUMAN
0.6604

ATVVYQGER_511.8_652.3
10
APOH_HUMAN
0.6604

TPSAAYLWVGTGASEAEK_919.5_428.2
935
GELS_HUMAN
0.6583

SEPRPGVLLR_375.2_454.3
816
FA7_HUMAN
0.6583

LSSPAVITDK_515.8_830.5
78
PLMN_HUMAN
0.6583

GPGEDFR_389.2_322.2
8
PTGDS_HUMAN
0.6583

EFDDDTYDNDIALLQLK_1014.48_501.3
842
TPA_HUMAN
0.6583

TFLTVYWTPER_706.9_502.3
815
ICAM1_HUMAN
0.6563

NTVISVNPSTK_580.3_732.4
68
VCAM1_HUMAN
0.6563

LPNNVLQEK_527.8_730.4
46
AFAM_HUMAN
0.6563

LPDTPQGLLGEAR_683.87_427.2
811
EGLN_HUMAN
0.6563

VANYVDWINDR_682.8_818.4
939
HGFA_HUMAN
0.6542

LSSPAVITDK_515.8_743.4
78
PLMN_HUMAN
0.6542

LPNNVLQEK_527.8_844.5
46
AFAM_HUMAN
0.6542

IPGIFELGISSQSDR_809.9_849.4
58
CO8B_HUMAN
0.6542

GAVHVVVAETDYQSFAVLYLER_822.8_580.3
940
CO8G_HUMAN
0.6542

FLNWIK_410.7_560.3
835
HABP2_HUMAN
0.6542

TFLTVYWTPER_706.9_401.2
815
ICAM1_HUMAN
0.6521

NKPGVYTDVAYYLAWIR_677.0_821.5
67
FA12_HUMAN
0.6521

AHYDLR_387.7_288.2
42
FETUA_HUMAN
0.6521

LLEVPEGR_456.8_686.4
31
C1S_HUMAN
0.6500

LIENGYFHPVK_439.6_627.4
66
F13B_HUMAN
0.6500

GFQALGDAADIR_617.3_717.4
11
TIMP1_HUMAN
0.6500

ELIEELVNITQNQK_557.6_517.3
807
IL13_HUMAN
0.6500

EAQLPVIENK_570.8_329.2
929
PLMN_HUMAN
0.6479

CRPINATLAVEK_457.9_559.3
859
CGB1_HUMAN
0.6479

ATVVYQGER_511.8_751.4
10
APOH_HUMAN
0.6479

ALNHLPLEYNSALYSR_621.0_538.3
52
CO6_HUMAN
0.6479

AHQLAIDTYQEFEETYIPK_766.0_634.4
933
CSH_HUMAN
0.6479

VTGLDFIPGLHPILTLSK_641.04_771.5
871
LEP_HUMAN
0.6458

VANYVDWINDR_682.8_917.4
939
HGFA_HUMAN
0.6458

SSNNPHSPIVEEFQVPYNK_729.4_261.2
4
C1S_HUMAN
0.6458

NKPGVYTDVAYYLAWIR_677.0_545.3
67
FA12_HUMAN
0.6458

GSLVQASEANLQAAQDFVR_668.7_735.4
851
ITIH1_HUMAN
0.6458

YTTEIIK_434.2_603.4
39
C1R_HUMAN
0.6438

NEIVFPAGILQAPFYTR_968.5_357.2
841
ECE1_HUMAN
0.6438

IPGIFELGISSQSDR_809.9_679.3
58
CO8B_HUMAN
0.6438

SNPVTLNVLYGPDLPR_585.7_817.4
937
PSG6_HUMAN
0.6417

LLELTGPK_435.8_644.4
840
A1BG_HUMAN
0.6417

EAQLPVIENK_570.8_699.4
929
PLMN_HUMAN
0.6417

AEHPTWGDEQLFQTTR_639.3_765.4
814
PGH1_HUMAN
0.6417

YGIEEHGK_311.5_599.3
810
CXA1_HUMAN
0.6396

TQIDSPLSGK_523.3_703.4
838
VCAM1_HUMAN
0.6396

YHFEALADTGISSEFYDNANDLLSK_940.8_301.1
941
CO8A_HUMAN
0.6375

SCDLALLETYCATPAK_906.9_315.2
942
IGF2_HUMAN
0.6375

NAVVQGLEQPHGLVVHPLR_688.4_285.2
870
LRP1_HUMAN
0.6375

HVVQLR_376.2_614.4
862
IL6RA_HUMAN
0.6375

NNQLVAGYLQGPNVNLEEK_700.7_999.5
822
IL1RA_HUMAN
0.6354

GIVEECCFR_585.3_771.3
860
IGF2_HUMAN
0.6354

DGSPDVTTADIGANTPDATK_973.5_531.3
72
PGRP2_HUMAN
0.6354

AEHPTWGDEQLFQTTR_639.3_569.3
814
PGH1_HUMAN
0.6354

YVVISQGLDKPR_458.9_400.3
943
LRP1_HUMAN
0.6333

WGAAPYR_410.7_577.3
63
PGRP2_HUMAN
0.6333

VRPQQLVK_484.3_609.4
866
ITIH4_HUMAN
0.6333

AVYEAVLR_460.8_750.4
81
PEPD_HUMAN
0.6333

TQIDSPLSGK_523.3_816.5
838
VCAM1_HUMAN
0.6313

IPKPEASFSPR_410.2_359.2
905
ITIH4_HUMAN
0.6313

HELTDEELQSLFTNFANVVDK_817.1_854.4
834
AFAM_HUMAN
0.6313

GSLVQASEANLQAAQDFVR_668.7_806.4
851
ITIH1_HUMAN
0.6313

GAVHVVVAETDYQSFAVLYLER_822.8_863.5
940
CO8G_HUMAN
0.6313

ENPAVIDFELAPIVDLVR_670.7_811.5
831
CO6_HUMAN
0.6313

VRPQQLVK_484.3_722.4
866
ITIH4_HUMAN
0.6292

IRPFFPQQ_516.79_372.2
916
FIBB_HUMAN
0.6292

LWAYLTIQELLAK_781.5_371.2
938
ITIH1_HUMAN
0.6271

EQLGEFYEALDCLR_871.9_563.3
936
A1AG1_HUMAN
0.6271

LLDFEFSSGR_585.8_553.3
944
G6PE_HUMAN
0.6250

LIENGYFHPVK_439.6_343.2
66
F13B_HUMAN
0.6250

ENPAVIDFELAPIVDLVR_670.7_601.4
831
CO6_HUMAN
0.6250

WNFAYWAAHQPWSR_607.3_545.3
825
PRG2_HUMAN
0.6229

TAVTANLDIR_537.3_802.4
826
CHL1_HUMAN
0.6229

WNFAYWAAHQPWSR_607.3_673.3
825
PRG2_HUMAN
0.6208

HTLNQIDEVK_598.8_951.5
48
FETUA_HUMAN
0.6208

DPDQTDGLGLSYLSSHIANVER_796.4_328.1
101
GELS_HUMAN
0.6208

WGAAPYR_410.7_634.3
63
PGRP2_HUMAN
0.6188

TEQAAVAR_423.2_487.3
918
FA12_HUMAN
0.6188

LEEHYELR_363.5_288.2
868
PAI2_HUMAN
0.6188

GIVEECCFR_585.3_900.3
860
IGF2_HUMAN
0.6188

YHFEALADTGISSEFYDNANDLLSK_940.8_874.5
941
CO8A_HUMAN
0.6167

TQILEWAAER_608.8_632.3
863
EGLN_HUMAN
0.6167

DSPSVWAAVPGK_607.31_301.2
877
PROF1_HUMAN
0.6167

DLHLSDVFLK_396.2_260.2
77
CO6_HUMAN
0.6167

AQPVQVAEGSEPDGFWEALGGK_758.0_574.3
895
GELS_HUMAN
0.6167

YSHYNER_323.48_581.3
920
HABP2_HUMAN
0.6146

YSHYNER_323.48_418.2
920
HABP2_HUMAN
0.6146

VNHVTLSQPK_374.9_459.3
3
B2MG_HUMAN
0.6146

EHSSLAFWK_552.8_267.1
823
APOH_HUMAN
0.6146

TATSEYQTFFNPR_781.4_386.2
945
THRB_HUMAN
0.6104

SGFSFGFK_438.7_732.4
79
CO8B_HUMAN
0.6104

GFQALGDAADIR_617.3_288.2
11
TIMP1_HUMAN
0.6104

FSVVYAK_407.2_381.2
1
FETUA_HUMAN
0.6104

QTLSWTVTPK_580.8_545.3
881
PZP_HUMAN
0.6083

QLGLPGPPDVPDHAAYHPF_676.7_263.1
61
ITIH4_HUMAN
0.6083

LSITGTYDLK_555.8_797.4
901
A1AT_HUMAN
0.6083

LPDTPQGLLGEAR_683.87_940.5
811
EGLN_HUMAN
0.6083

VVESLAK_373.2_646.4
809
IBP1_HUMAN
0.6063

VSEADSSNADWVTK_754.9_347.2
964
CFAB_HUMAN
0.6063

TEQAAVAR_423.2_615.4
918
FA12_HUMAN
0.6063

SEPRPGVLLR_375.2_654.4
816
FA7_HUMAN
0.6063

QTLSWTVTPK_580.8_818.4
881
PZP_HUMAN
0.6063

HYINLITR_515.3_301.1
885
NPY_HUMAN
0.6063

DPTFIPAPIQAK_433.2_461.2
891
ANGT_HUMAN
0.6063

VSEADSSNADWVTK_754.9_533.3
964
CFAB_HUMAN
0.6042

VQEVLLK_414.8_373.3
837
HYOU1_HUMAN
0.6042

SILFLGK_389.2_577.4
861
THBG_HUMAN
0.6042

IQHPFTVEEFVLPK_562.0_603.4
882
PZP_HUMAN
0.6042

ELPQSIVYK_538.8_417.7
808
FBLN3_HUMAN
0.6042

AVGYLITGYQR_620.8_737.4
879
PZP_HUMAN
0.6042

ATWSGAVLAGR_544.8_643.4
922
A1BG_HUMAN
0.6042

AKPALEDLR_506.8_288.2
899
APOA1_HUMAN
0.6042

SEYGAALAWEK_612.8_845.5
867
CO6_HUMAN
0.6021

NVNQSLLELHK_432.2_656.3
946
FRIH_HUMAN
0.6021

IQHPFTVEEFVLPK_562.0_861.5
882
PZP_HUMAN
0.6021

IPKPEASFSPR_410.2_506.3
905
ITIH4_HUMAN
0.6021

GVTGYFTFNLYLK_508.3_260.2
848
PSG5_HUMAN
0.6021

DGSPDVTTADIGANTPDATK_973.5_844.4
72
PGRP2_HUMAN
0.6021

AVGYLITGYQR_620.8_523.3
879
PZP_HUMAN
0.6021

ANDQYLTAAALHNLDEAVK_686.3_317.2
921
ILIA_HUMAN
0.6021

TLYSSSPR_455.7_696.3
71
IC1_HUMAN
0.6000

LHKPGVYTR_357.5_479.3
947
HGFA_HUMAN
0.6000

IIGGSDADIK_494.8_260.2
21
C1S_HUMAN
0.6000

HELTDEELQSLFTNFANVVDK_817.1_906.5
834
AFAM_HUMAN
0.6000

GGEGTGYFVDFSVR_745.9_869.5
35
HRG_HUMAN
0.6000

AVLHIGEK_289.5_348.7
855
THBG_HUMAN
0.6000

ALVLELAK_428.8_672.4
872
INHBE_HUMAN
0.6000

TABLE 16

Lasso Summed Coefficients All Windows

SEQ

ID

Transition
NO:
Protein
SumBestCoefs_All

TQILEWAAER_608.8_761.4
863
EGLN_HUMAN
26.4563

GFQALGDAADIR_617.3_717.4
11
TIMP1_HUMAN
17.6447

AVDIPGLEAATPYR_736.9_399.2
900
TENA_HUMAN
16.2270

TVQAVLTVPK_528.3_428.3
7
PEDF_HUMAN
15.1166

LDFHFSSDR_375.2_611.3
6
INHBC_HUMAN
15.0029

ATVVYQGER_511.8_652.3
10
APOH_HUMAN
13.2314

ETLLQDFR_511.3_565.3
9
AMBP_HUMAN
13.1219

GFQALGDAADIR_617.3_288.2
11
TIMP1_HUMAN
12.1693

IQTHSTTYR_369.5_627.3
59
F13B_HUMAN
9.4737

GDTYPAELYITGSILR_885.0_274.1
43
F13B_HUMAN
6.1820

ELPQSIVYK_538.8_417.7
808
FBLN3_HUMAN
6.1607

NEIVFPAGILQAPFYTR_968.5_357.2
841
ECE1_HUMAN
5.5493

AHYDLR_387.7_566.3
42
FETUA_HUMAN
5.4415

HHGPTITAK_321.2_275.1
33
AMBP_HUMAN
5.0751

SERPPIFEIR_415.2_564.3
948
LRP1_HUMAN
4.5620

ALDLSLK_380.2_185.1
817
ITIH3_HUMAN
4.4275

DTDTGALLFIGK_625.8_217.1
799
PEDF_HUMAN
4.3562

ALNHLPLEYNSALYSR_621.0_696.4
52
CO6_HUMAN
3.9022

ETLLQDFR_511.3_322.2
9
AMBP_HUMAN
3.3017

YGIEEHGK_311.5_599.3
810
CXA1_HUMAN
2.8410

IHWESASLLR_606.3_437.2
869
CO3_HUMAN
2.6618

GEVTYTTSQVSK_650.3_750.4
926
EGLN_HUMAN
2.5328

ELIEELVNITQNQK_557.6_618.3
807
IL13_HUMAN
2.5088

DLHLSDVFLK_396.2_260.2
77
CO6_HUMAN
2.4010

SYTITGLQPGTDYK_772.4_352.2
114
FINC_HUMAN
2.3304

SPELQAEAK_486.8_788.4
2
APOA2_HUMAN
2.2657

VNHVTLSQPK_374.9_459.3
3
B2MG_HUMAN
2.1480

DTDTGALLFIGK_625.8_818.5
799
PEDF_HUMAN
2.0051

LLDFEFSSGR_585.8_944.4
944
G6PE_HUMAN
1.7763

GPGEDFR_389.2_623.3
8
PTGDS_HUMAN
1.6782

DPNGLPPEAQK_583.3_669.4
14
RET4_HUMAN
1.6581

IQTHSTTYR_369.5_540.3
59
F13B_HUMAN
1.6107

VNHVTLSQPK_374.9_244.2
3
B2MG_HUMAN
1.4779

STLFVPR_410.2_518.3
847
PEPD_HUMAN
1.3961

GEVTYTTSQVSK_650.3_913.5
926
EGLN_HUMAN
1.3306

ALVLELAK_428.8_672.4
872
INHBE_HUMAN
1.2973

ANDQYLTAAALHNLDEAVK_686.3_317.2
921
ILIA_HUMAN
1.1850

STLFVPR_410.2_272.2
847
PEPD_HUMAN
1.1842

GPGEDFR_389.2_322.2
8
PTGDS_HUMAN
1.1742

IPSNPSHR_303.2_610.3
819
FBLN3_HUMAN
1.0868

HHGPTITAK_321.2_432.3
33
AMBP_HUMAN
1.0813

TLAFVR_353.7_274.2
806
FA7_HUMAN
1.0674

DLHLSDVFLK_396.2_366.2
77
CO6_HUMAN
0.9887

EFDDDTYDNDIALLQLK_1014.48_501.3
842
TPA_HUMAN
0.9468

AIGLPEELIQK_605.86_856.5
813
FABPL_HUMAN
0.7740

LIENGYFHPVK_439.6_343.2
66
F13B_HUMAN
0.7740

LPDTPQGLLGEAR_683.87_427.2
811
EGLN_HUMAN
0.6748

EHSSLAFWK_552.8_267.1
823
APOH_HUMAN
0.6035

NCSFSIIYPVVIK_770.4_831.5
818
CRHBP_HUMAN
0.6014

ALNSIIDVYHK_424.9_661.3
949
S10A8_HUMAN
0.5987

WGAAPYR_410.7_577.3
63
PGRP2_HUMAN
0.5699

TQILEWAAER_608.8_632.3
863
EGLN_HUMAN
0.5395

IPSNPSHR_303.2_496.3
819
FBLN3_HUMAN
0.4845

VEHSDLSFSK_383.5_234.1
800
B2MG_HUMAN
0.4398

VEHSDLSFSK_383.5_468.2
800
B2MG_HUMAN
0.3883

FLYHK_354.2_284.2
802
AMBP_HUMAN
0.3410

LPDTPQGLLGEAR_683.87_940.5
811
EGLN_HUMAN
0.3282

EALVPLVADHK_397.9_390.2
849
HGFA_HUMAN
0.3091

IEGNLIFDPNNYLPK_874.0_845.5
16
APOB_HUMAN
0.2933

LIENGYFHPVK_439.6_627.4
66
F13B_HUMAN
0.2896

VPLALFALNR_557.3_620.4
29
PEPD_HUMAN
0.2875

FICPLTGLWPINTLK_887.0_685.4
804
APOH_HUMAN
0.2823

NAVVQGLEQPHGLVVHPLR_688.4_890.6
870
LRP1_HUMAN
0.2763

ALNFGGIGVVVGHELTHAFDDQGR_837.1_299.2
34
ECE1_HUMAN
0.2385

SPELQAEAK_486.8_659.4
2
AP0A2_HUMAN
0.2232

EVFSKPISWEELLQ_852.9_260.2
803
FA40A_HUMAN
0.1608

VANYVDWINDR_682.8_917.4
939
HGFA_HUMAN
0.1507

EVFSKPISWEELLQ_852.9_376.2
803
FA40A_HUMAN
0.1487

HVVQLR_376.2_614.4
862
IL6RA_HUMAN
0.1256

TVQAVLTVPK_528.3_855.5
7
PEDF_HUMAN
0.1170

ELIEELVNITQNQK_557.6_517.3
807
IL13_HUMAN
0.1159

EALVPLVADHK_397.9_439.8
849
HGFA_HUMAN
0.0979

AITPPHPASQANIIFDITEGNLR_825.8_917.5
125
FBLN1_HUMAN
0.0797

FLYHK_354.2_447.2
802
AMBP_HUMAN
0.0778

SLLQPNK_400.2_358.2
98
CO8A_HUMAN
0.0698

TGISPLALIK_506.8_654.5
20
APOB_HUMAN
0.0687

ALNFGGIGVVVGHELTHAFDDQGR_837.1_360.2
34
ECE1_HUMAN
0.0571

DYWSTVK_449.7_347.2
28
APOC3_HUMAN
0.0357

AITPPHPASQANIIFDITEGNLR_825.8_459.3
125
FBLN1_HUMAN
0.0313

AALAAFNAQNNGSNFQLEEISR_789.1_633.3
821
FETUA_HUMAN
0.0279

DPNGLPPEAQK_583.3_497.2
14
RET4_HUMAN
0.0189

TLAFVR_353.7_492.3
806
FA7_HUMAN
0.0087

TABLE 17

Lasso Summed Coefficients Early Window

SEQ ID

Transition
NO:
Protein
SumBestCoefs_Early

LDFHFSSDR_375.2_611.3
6
INHBC_HUMAN
40.2030

ELPQSIVYK_538.8_417.7
808
FBLN3_HUMAN
22.6926

GFQALGDAADIR_617.3_288.2
11
TIMP1_HUMAN
17.4169

GFQALGDAADIR_617.3_717.4
11
TIMP1_HUMAN
3.4083

VNHVTLSQPK_374.9_459.3
3
B2MG_HUMAN
3.2559

EFDDDTYDNDIALLQLK_1014.48_388.3
842
TPA_HUMAN
2.4073

STLFVPR_410.2_272.2
847
PEPD_HUMAN
2.3984

WGAAPYR_410.7_634.3
63
PGRP2_HUMAN
2.3564

LDFHFSSDR_375.2_464.2
6
INHBC_HUMAN
1.9038

VNHVTLSQPK_374.9_244.2
3
B2MG_HUMAN
1.7999

DTDTGALLFIGK_625.8_217.1
799
PEDF_HUMAN
1.5802

GPGEDFR_389.2_623.3
8
PTGDS_HUMAN
1.4223

IHWESASLLR_606.3_437.2
869
CO3_HUMAN
1.2735

ELIEELVNITQNQK_557.6_618.3
807
IL13_HUMAN
1.2652

AQPVQVAEGSEPDGFWEALGGK_758.0_623.4
895
GELS_HUMAN
1.2361

FAFNLYR_465.8_565.3
94
HEP2_HUMAN
1.0876

SGFSFGFK_438.7_732.4
79
CO8B_HUMAN
1.0459

VVGGLVALR_442.3_784.5
5
FA12_HUMAN
0.9572

IEGNLIFDPNNYLPK_874.0_845.5
16
APOB_HUMAN
0.9571

ETLLQDFR_511.3_565.3
9
AMBP_HUMAN
0.7851

LSIPQITTK_500.8_687.4
950
PSG5_HUMAN
0.7508

TASDFITK_441.7_710.4
115
GELS_HUMAN
0.6549

YGIEEHGK_311.5_599.3
810
CXA1_HUMAN
0.6179

AFQVWSDVTPLR_709.88_347.2
884
MMP2_HUMAN
0.6077

TVQAVLTVPK_528.3_855.5
7
PEDF_HUMAN
0.5889

LSITGTYDLK_555.8_696.4
901
A1AT_HUMAN
0.5857

ELIEELVNITQNQK_557.6_517.3
807
IL13_HUMAN
0.5334

LIENGYFHPVK_439.6_627.4
66
F13B_HUMAN
0.5257

NEIVFPAGILQAPFYTR_968.5_357.2
841
ECE1_HUMAN
0.4601

SLLQPNK_400.2_358.2
98
CO8A_HUMAN
0.4347

LSIPQITTK_500.8_800.5
950
PSG5_HUMAN
0.4329

GVTGYFTFNLYLK_508.3_683.9
848
PSG5_HUMAN
0.4302

IQTHSTTYR_369.5_627.3
59
F13B_HUMAN
0.4001

ATVVYQGER_511.8_652.3
10
APOH_HUMAN
0.3909

LPDTPQGLLGEAR_683.87_427.2
811
EGLN_HUMAN
0.3275

NNQLVAGYLQGPNVNLEEK_700.7_999.5
822
IL1RA_HUMAN
0.3178

SERPPIFEIR_415.2_564.3
948
LRP1_HUMAN
0.3112

AHYDLR_387.7_566.3
42
FETUA_HUMAN
0.2900

NEIWYR_440.7_637.4
883
FA12_HUMAN
0.2881

ALDLSLK_380.2_575.3
817
ITIH3_HUMAN
0.2631

NKPGVYTDVAYYLAWIR_677.0_545.3
67
FA12_HUMAN
0.2568

SYTITGLQPGTDYK_772.4_352.2
114
FINC_HUMAN
0.2277

LFIPQITPK_528.8_683.4
951
PSG11_HUMAN
0.2202

IIGGSDADIK_494.8_260.2
21
C1S_HUMAN
0.2182

AVDIPGLEAATPYR_736.9_399.2
900
TENA_HUMAN
0.2113

DTDTGALLFIGK_625.8_818.5
799
PEDF_HUMAN
0.2071

AEIEYLEK_497.8_389.2
836
LYAM1_HUMAN
0.1925

EHSSLAFWK_552.8_838.4
823
APOH_HUMAN
0.1899

LPDTPQGLLGEAR_683.87_940.5
811
EGLN_HUMAN
0.1826

WGAAPYR_410.7_577.3
63
PGRP2_HUMAN
0.1669

LFIPQITPK_528.8_261.2
951
PSG11_HUMAN
0.1509

WWGGQPLWITATK_772.4_929.5
886
ENPP2_HUMAN
0.1446

DSPSVWAAVPGK_607.31_301.2
877
PROF1_HUMAN
0.1425

LIQDAVTGLTVNGQITGDK_972.0_798.4
844
ITIH3_HUMAN
0.1356

ALDLSLK_380.2_185.1
817
ITIH3_HUMAN
0.1305

TVQAVLTVPK_528.3_428.3
7
PEDF_HUMAN
0.1249

NAVVQGLEQPHGLVVHPLR_688.4_890.6
870
LRP1_HUMAN
0.1092

NSDQEIDFK_548.3_409.2
864
S10A5_HUMAN
0.0937

YNSQLLSFVR_613.8_508.3
827
TFR1_HUMAN
0.0905

LLDFEFSSGR_585.8_553.3
944
G6PE_HUMAN
0.0904

ALNFGGIGVVVGHELTHAFDDQGR_837.1_299.2
34
ECE1_HUMAN
0.0766

STLFVPR_410.2_518.3
847
PEPD_HUMAN
0.0659

DLHLSDVFLK_396.2_260.2
77
CO6_HUMAN
0.0506

EHSSLAFWK_552.8_267.1
823
APOH_HUMAN
0.0452

TQIDSPLSGK_523.3_703.4
838
VCAM1_HUMAN
0.0447

HHGPTITAK_321.2_432.3
33
AMBP_HUMAN
0.0421

AFQVWSDVTPLR_709.88_385.3
884
MMP2_HUMAN
0.0417

TGISPLALIK_506.8_741.5
20
APOB_HUMAN
0.0361

DLHLSDVFLK_396.2_366.2
77
CO6_HUMAN
0.0336

NTVISVNPSTK_580.3_845.5
68
VCAM1_HUMAN
0.0293

DIIKPDPPK_511.8_342.2
853
IL12B_HUMAN
0.0219

TGISPLALIK_506.8_654.5
20
APOB_HUMAN
0.0170

GAVHVVVAETDYQSFAVLYLER_822.8_580.3
940
CO8G_HUMAN
0.0151

LNIGYIEDLK_589.3_837.4
830
PAI2_HUMAN
0.0048

GPGEDFR_389.2_322.2
8
PTGDS_HUMAN
0.0008

TABLE 18

Lasso Summed Coefficients Early Middle Combined Windows

SEQ ID

Transition
NO:
Protein
SumBestCoefs_EM

ELPQSIVYK_538.8_417.7
808
FBLN3_HUMAN
24.8794

AHYDLR_387.7_566.3
42
FETUA_HUMAN
20.8397

LDFHFSSDR_375.2_611.3
6
INHBC_HUMAN
18.6630

GFQALGDAADIR_617.3_288.2
11
TIMP1_HUMAN
14.7270

HHGPTITAK_321.2_432.3
33
AMBP_HUMAN
11.1473

VNHVTLSQPK_374.9_459.3
3
B2MG_HUMAN
10.9421

NNQLVAGYLQGPNVNLEEK_700.7_999.5
822
IL1RA_HUMAN
10.4646

HHGPTITAK_321.2_275.1
33
AMBP_HUMAN
7.7034

ETLLQDFR_511.3_565.3
9
AMBP_HUMAN
6.7435

TVQAVLTVPK_528.3_428.3
7
PEDF_HUMAN
5.7356

SLQAFVAVAAR_566.8_487.3
873
IL23A_HUMAN
4.8684

YGIEEHGK_311.5_599.3
810
CXA1_HUMAN
4.4936

ATVVYQGER_511.8_652.3
10
APOH_HUMAN
3.9524

VNHVTLSQPK_374.9_244.2
3
B2MG_HUMAN
3.8937

ELIEELVNITQNQK_557.6_618.3
807
IL13_HUMAN
3.8022

ALNFGGIGVVVGHELTHAFDDQGR_837.1_299.2
34
ECE1_HUMAN
3.7603

ETLLQDFR_511.3_322.2
9
AMBP_HUMAN
3.1792

TVQAVLTVPK_528.3_855.5
7
PEDF_HUMAN
3.1046

AALAAFNAQNNGSNFQLEEISR_789.1_633.3
821
FETUA_HUMAN
3.0021

AVDIPGLEAATPYR_736.9_399.2
900
TENA_HUMAN
2.6899

DLHLSDVFLK_396.2_366.2
77
CO6_HUMAN
2.5525

DTDTGALLFIGK_625.8_818.5
799
PEDF_HUMAN
2.4794

SYTITGLQPGTDYK_772.4_352.2
114
FINC_HUMAN
2.4535

IQTHSTTYR_369.5_627.3
59
F13B_HUMAN
2.3395

AHYDLR_387.7_288.2
42
FETUA_HUMAN
2.1058

NCSFSIIYPVVIK_770.4_831.5
818
CRHBP_HUMAN
2.0427

AIGLPEELIQK_605.86_856.5
813
FABPL_HUMAN
1.5354

GFQALGDAADIR_617.3_717.4
11
TIMP1_HUMAN
1.4175

TGISPLALIK_506.8_654.5
20
APOB_HUMAN
1.3562

YTTEIIK_434.2_603.4
39
C1R_HUMAN
1.2855

ETPEGAEAKPWYEPIYLGGVFQLEK_951.14_877.5
878
TNFA_HUMAN
1.1198

ANDQYLTAAALHNLDEAVK_686.3_317.2
921
IL1A_HUMAN
1.0574

ILPSVPK_377.2_244.2
846
PGH1_HUMAN
1.0282

ALDLSLK_380.2_185.1
817
ITIH3_HUMAN
1.0057

NAVVQGLEQPHGLVVHPLR_688.4_890.6
870
LRP1_HUMAN
0.9884

IEGNLIFDPNNYLPK_874.0_845.5
16
APOB_HUMAN
0.9846

ALDLSLK_380.2_575.3
817
ITIH3_HUMAN
0.9327

LDFHFSSDR_375.2_464.2
6
INHBC_HUMAN
0.8852

LSIPQITTK_500.8_800.5
950
PSG5_HUMAN
0.7740

SERPPIFEIR_415.2_564.3
948
LRP1_HUMAN
0.7013

AEAQAQYSAAVAK_654.3_709.4
89
ITIH4_HUMAN
0.6752

IHWESASLLR_606.3_437.2
869
CO3_HUMAN
0.6176

LFIPQITPK_528.8_261.2
951
PSG11_HUMAN
0.5345

FICPLTGLWPINTLK_887.0_685.4
804
APOH_HUMAN
0.5022

DFNQFSSGEK_386.8_189.1
839
FETA_HUMAN
0.4932

TATSEYQTFFNPR_781.4_272.2
945
THRB_HUMAN
0.4725

SPELQAEAK_486.8_788.4
2
APOA2_HUMAN
0.4153

FIVGFTR_420.2_261.2
952
CCL20_HUMAN
0.4111

TLLPVSKPEIR_418.3_288.2
919
CO5_HUMAN
0.3409

DIIKPDPPK_511.8_342.2
853
IL12B_HUMAN
0.3403

DTDTGALLFIGK_625.8_217.1
799
PEDF_HUMAN
0.3073

YTTEIIK_434.2_704.4
39
C1R_HUMAN
0.3050

SPELQAEAK_486.8_659.4
2
APOA2_HUMAN
0.3047

TGISPLALIK_506.8_741.5
20
APOB_HUMAN
0.3031

VVGGLVALR_442.3_784.5
5
FA12_HUMAN
0.2960

WWGGQPLWITATK_772.4_373.2
886
ENPP2_HUMAN
0.2498

TQILEWAAER_608.8_632.3
863
EGLN_HUMAN
0.2342

STLFVPR_410.2_272.2
847
PEPD_HUMAN
0.2035

DYWSTVK_449.7_347.2
28
APOC3_HUMAN
0.2018

WWGGQPLWITATK_772.4_929.5
886
ENPP2_HUMAN
0.1614

SILFLGK_389.2_201.1
861
THBG_HUMAN
0.1593

AFQVWSDVTPLR_709.88_385.3
884
MMP2_HUMAN
0.1551

IQTHSTTYR_369.5_540.3
59
F13B_HUMAN
0.1434

AFQVWSDVTPLR_709.88_347.2
884
MMP2_HUMAN
0.1420

LSITGTYDLK_555.8_797.4
901
A1AT_HUMAN
0.1395

LSITGTYDLK_555.8_696.4
901
A1AT_HUMAN
0.1294

WGAAPYR_410.7_634.3
63
PGRP2_HUMAN
0.1259

IAPQLSTEELVSLGEK_857.5_533.3
56
AFAM_HUMAN
0.1222

FICPLTGLWPINTLK_887.0_756.9
804
APOH_HUMAN
0.1153

QINSYVK_426.2_496.3
897
CBG_HUMAN
0.1055

TATSEYQTFFNPR_781.4_386.2
945
THRB_HUMAN
0.0921

AFLEVNEEGSEAAASTAVVIAGR_764.4_685.4
953
ANT3_HUMAN
0.0800

AKPALEDLR_506.8_288.2
899
APOA1_HUMAN
0.0734

GPGEDFR_389.2_623.3
8
PTGDS_HUMAN
0.0616

SLLQPNK_400.2_358.2
98
CO8A_HUMAN
0.0565

ESDTSYVSLK_564.8_347.2
102
CRP_HUMAN
0.0497

FFQYDTWK_567.8_712.3
954
IGF2_HUMAN
0.0475

FSVVYAK_407.2_579.4
1
FETUA_HUMAN
0.0437

TQIDSPLSGK_523.3_703.4
838
VCAM1_HUMAN
0.0401

LNIGYIEDLK_589.3_837.4
830
PAI2_HUMAN
0.0307

IPSNPSHR_303.2_496.3
819
FBLN3_HUMAN
0.0281

NEIVFPAGILQAPFYTR_968.5_456.2
841
ECE1_HUMAN
0.0276

TLAFVR_353.7_274.2
806
FA7_HUMAN
0.0220

AEAQAQYSAAVAK_654.3_908.5
89
ITIH4_HUMAN
0.0105

AQPVQVAEGSEPDGFWEALGGK_758.0_623.4
895
GELS_HUMAN
0.0103

QINSYVK_426.2_610.3
897
CBG_HUMAN
0.0080

NSDQEIDFK_548.3_409.2
864
S10A5_HUMAN
0.0017

TABLE 19

Lasso Summed Coefficients Middle-Late Combined Windows

SEQ ID

Transtion
NO:
Protein
SumBestCoefs_ML

TQILEWAAER_608.8_761.4
863
EGLN_HUMAN
45.0403

GDTYPAELYITGSILR_885.0_274.1
43
F13B_HUMAN
31.4888

GEVTYTTSQVSK_650.3_750.4
926
EGLN_HUMAN
22.3322

GEVTYTTSQVSK_650.3_913.5
926
EGLN_HUMAN
17.0298

AVDIPGLEAATPYR_736.9_286.1
900
TENA_HUMAN
8.6029

AVDIPGLEAATPYR_736.9_399.2
900
TENA_HUMAN
7.9874

NEIVFPAGILQAPFYTR_968.5_357.2
841
ECE1_HUMAN
7.8773

ALNHLPLEYNSALYSR_621.0_696.4
52
CO6_HUMAN
6.8534

DPNGLPPEAQK_583.3_669.4
14
RET4_HUMAN
5.0045

GFQALGDAADIR_617.3_717.4
11
TIMP1_HUMAN
4.6191

ATVVYQGER_511.8_652.3
10
APOH_HUMAN
4.2522

IAQYYYTFK_598.8_395.2
25
F13B_HUMAN
3.5721

NAVVQGLEQPHGLVVHPLR_688.4_285.2
870
LRP1_HUMAN
3.2886

IAQYYYTFK_598.8_884.4
25
F13B_HUMAN
2.9205

SERPPIFEIR_415.2_564.3
948
LRP1_HUMAN
2.4237

TLAFVR_353.7_274.2
806
FA7_HUMAN
2.1925

EVFSKPISWEELLQ_852.9_260.2
803
FA40A_HUMAN
2.1591

EVFSKPISWEELLQ_852.9_376.2
803
FA40A_HUMAN
2.1586

EFDDDTYDNDIALLQLK_1014.48_501.3
842
TPA_HUMAN
2.0892

TLAFVR_353.7_492.3
806
FA7_HUMAN
2.0399

EALVPLVADHK_397.9_439.8
849
HGFA_HUMAN
1.8856

ETLLQDFR_511.3_565.3
9
AMBP_HUMAN
1.7809

ALNSIIDVYHK_424.9_661.3
949
S10A8_HUMAN
1.6114

AITPPHPASQANIIFDITEGNLR_825.8_917.5
125
FBLN1_HUMAN
1.3423

EQLGEFYEALDCLR_871.9_747.4
936
A1AG1_HUMAN
1.2473

TFLTVYWTPER_706.9_502.3
815
ICAM1_HUMAN
0.9851

NTVISVNPSTK_580.3_845.5
68
VCAM1_HUMAN
0.9845

FLNWIK_410.7_560.3
835
HABP2_HUMAN
0.9798

ETPEGAEAKPWYEPIYLGGVFQLEK_951.14_990.6
878
TNFA_HUMAN
0.9679

NVNQSLLELHK_432.2_656.3
946
FRIH_HUMAN
0.8280

VPLALFALNR_557.3_620.4
29
PEPD_HUMAN
0.7851

IAPQLSTEELVSLGEK_857.5_533.3
56
AFAM_HUMAN
0.7731

AVYEAVLR_460.8_750.4
81
PEPD_HUMAN
0.7452

LPDTPQGLLGEAR_683.87_427.2
811
EGLN_HUMAN
0.7145

TVQAVLTVPK_528.3_428.3
7
PEDF_HUMAN
0.6584

YSHYNER_323.48_418.2
920
HABP2_HUMAN
0.5244

LLELTGPK_435.8_644.4
840
A1BG_HUMAN
0.5072

DTDTGALLFIGK_625.8_818.5
799
PEDF_HUMAN
0.5010

DPNGLPPEAQK_583.3_497.2
14
RET4_HUMAN
0.4803

AHYDLR_387.7_566.3
42
FETUA_HUMAN
0.4693

LPNNVLQEK_527.8_844.5
46
AFAM_HUMAN
0.4640

VTGLDFIPGLHPILTLSK_641.04_771.5
871
LEP_HUMAN
0.4584

LLELTGPK_435.8_227.2
840
A1BG_HUMAN
0.4515

YTTEIIK_434.2_704.4
39
C1R_HUMAN
0.4194

SSNNPHSPIVEEFQVPYNK_729.4_261.2
4
C1S_HUMAN
0.3886

ALNHLPLEYNSALYSR_621.0_538.3
52
CO6_HUMAN
0.3405

HFQNLGK_422.2_527.2
50
AFAM_HUMAN
0.3368

EQLGEFYEALDCLR_871.9_563.3
936
A1AG1_HUMAN
0.3348

TQILEWAAER_608.8_632.3
863
EGLN_HUMAN
0.2943

ALVLELAK_428.8_672.4
872
INHBE_HUMAN
0.2895

LSNENHGIAQR_413.5_519.8
927
ITIH2_HUMAN
0.2835

LPNNVLQEK_527.8_730.4
46
AFAM_HUMAN
0.2764

DTDTGALLFIGK_625.8_217.1
799
PEDF_HUMAN
0.2694

GDTYPAELYITGSILR_885.0_922.5
43
F13B_HUMAN
0.2594

GPITSAAELNDPQSILLR_632.3_601.4
955
EGLN_HUMAN
0.2388

ANLINNIFELAGLGK_793.9_834.5
932
LCAP_HUMAN
0.2158

SEPRPGVLLR_375.2_454.3
816
FA7_HUMAN
0.1921

EQSLNVSQDLDTIR_539.9_557.8
956
SYNE2_HUMAN
0.1836

FICPLTGLWPINTLK_887.0_685.4
804
APOH_HUMAN
0.1806

ALNFGGIGVVVGHELTHAFDDQGR_837.1_360.2
34
ECE1_HUMAN
0.1608

ANDQYLTAAALHNLDEAVK_686.3_317.2
921
IL1A_HUMAN
0.1607

AQETSGEEISK_589.8_979.5
876
IBP1_HUMAN
0.1598

QINSYVK_426.2_610.3
897
CBG_HUMAN
0.1592

SILFLGK_389.2_577.4
861
THBG_HUMAN
0.1412

DAVVYPILVEFTR_761.4_286.1
957
HYOU1_HUMAN
0.1298

LIEIANHVDK_384.6_683.3
911
ADA12_HUMAN
0.1297

LSSPAVITDK_515.8_830.5
78
PLMN_HUMAN
0.1272

LIENGYFHPVK_439.6_343.2
66
F13B_HUMAN
0.1176

AALAAFNAQNNGSNFQLEEISR_789.1_633.3
821
FETUA_HUMAN
0.1160

IQTHSTTYR_369.5_540.3
59
F13B_HUMAN
0.1146

IPKPEASFSPR_410.2_506.3
905
ITIH4_HUMAN
0.1001

LLDFEFSSGR_585.8_944.4
944
G6PE_HUMAN
0.0800

YYLQGAK_421.7_516.3
832
ITIH4_HUMAN
0.0793

VRPQQLVK_484.3_722.4
866
ITIH4_HUMAN
0.0744

GPGEDFR_389.2_322.2
8
PTGDS_HUMAN
0.0610

ITQDAQLK_458.8_803.4
906
CBG_HUMAN
0.0541

TATSEYQTFFNPR_781.4_272.2
945
THRB_HUMAN
0.0511

ETLLQDFR_511.3_322.2
9
AMBP_HUMAN
0.0472

YEFLNGR_449.7_293.1
124
PLMN_HUMAN
0.0345

TLYSSSPR_455.7_696.3
71
IC1_HUMAN
0.0316

SLLQPNK_400.2_599.4
98
CO8A_HUMAN
0.0242

LLEVPEGR_456.8_686.4
31
C1S_HUMAN
0.0168

GGEGTGYFVDFSVR_745.9_722.4
35
HRG_HUMAN
0.0110

IQTHSTTYR_369.5_627.3
59
F13B_HUMAN
0.0046

TABLE 20

Random Forest SummedGini All Windows

SEQ ID

Transition
NO:
Protein
SumBestGini
Probability

TVQAVLTVPK_528.3_428.3
7
PEDF_HUMAN
12.6521
1.0000

DTDTGALLFIGK_625.8_818.5
799
PEDF_HUMAN
11.9585
0.9985

ALALPPLGLAPLLNLWAKPQG
801
SHBG_HUMAN
10.5229
0.9971

R_770.5_256.2

DVLLLVHNLPQNLTGHIWYK_791.8_883.0
805
PSG7_HUMAN
10.2666
0.9956

ETLLQDFR_511.3_565.3
9
AMBP_HUMAN
8.9862
0.9941

ALALPPLGLAPLLNLWAKPQG
801
SHBG_HUMAN
8.6349
0.9927

R_770.5_457.3

IALGGLLFPASNLR_481.3_657.4
55
SHBG_HUMAN
8.5838
0.9912

DTDTGALLFIGK_625.8_217.1
799
PEDF_HUMAN
8.2463
0.9897

IQTHSTTYR_369.5_627.3
59
F13B_HUMAN
8.1199
0.9883

DVLLLVHNLPQNLTGHIWYK_791.8_310.2
805
PSG7_HUMAN
7.7393
0.9868

IALGGLLFPASNLR_481.3_412.3
55
SHBG_HUMAN
7.5601
0.9853

HHGPTITAK_321.2_432.3
33
AMBP_HUMAN
7.5181
0.9838

ETLLQDFR_511.3_322.2
9
AMBP_HUMAN
7.4043
0.9824

FICPLTGLWPINTLK_887.0_685.4
804
APOH_HUMAN
7.2072
0.9809

GPGEDFR_389.2_623.3
8
PTGDS_HUMAN
7.1422
0.9794

IQTHSTTYR_369.5_540.3
59
F13B_HUMAN
6.9809
0.9780

TVQAVLTVPK_528.3_855.5
7
PEDF_HUMAN
6.6191
0.9765

ATVVYQGER_511.8_652.3
10
APOH_HUMAN
6.5813
0.9750

VVLSSGSGPGLDLPLVLGLPLQ
38
SHBG_HUMAN
6.3244
0.9736

LK_791.5_598.4

HHGPTITAK_321.2_275.1
33
AMBP_HUMAN
6.3081
0.9721

VVLSSGSGPGLDLPLVLGLPLQ
38
SHBG_HUMAN
6.0654
0.9706

LK_791.5_768.5

GDTYPAELYITGSILR_885.0_274.1
43
F13B_HUMAN
5.9580
0.9692

ATVVYQGER_511.8_751.4
10
APOH_HUMAN
5.9313
0.9677

LDFHFSSDR_375.2_611.3
6
INHBC_HUMAN
5.8533
0.9662

LDFHFSSDR_375.2_464.2
6
INHBC_HUMAN
5.8010
0.9648

EVFSKPISWEELLQ_852.9_260.2
803
FA40A_HUMAN
5.6648
0.9633

DTYVSSFPR_357.8_272.2
934
TCEA1_HUMAN
5.6549
0.9618

LPDTPQGLLGEAR_683.87_427.2
811
EGLN_HUMAN
5.3806
0.9604

FLYHK_354.2_447.2
802
AMBP_HUMAN
5.3764
0.9589

SPELQAEAK_486.8_659.4
2
APOA2_HUMAN
5.1896
0.9574

GPGEDFR_389.2_322.2
8
PTGDS_HUMAN
5.1876
0.9559

SGVDLADSNQK_567.3_662.3
958
VGFR3_HUMAN
5.1159
0.9545

TNTNEFLIDVDK_704.85_849.5
812
TF_HUMAN
4.7216
0.9530

FICPLTGLWPINTLK_887.0_756.9
804
APOH_HUMAN
4.6421
0.9515

LNIGYIEDLK_589.3_950.5
830
PAI2_HUMAN
4.6250
0.9501

EVFSKPISWEELLQ_852.9_376.2
803
FA40A_HUMAN
4.4215
0.9486

SYTITGLQPGTDYK_772.4_680.3
114
FINC_HUMAN
4.4103
0.9471

TLPFSR_360.7_409.2
820
LYAM1_HUMAN
4.2148
0.9457

SPELQAEAK_486.8_788.4
2
APOA2_HUMAN
4.2081
0.9442

GDTYPAELYITGSILR_885.0_922.5
43
F13B_HUMAN
4.0672
0.9427

AEIEYLEK_497.8_552.3
836
LYAM1_HUMAN
3.9248
0.9413

FSLVSGWGQLLDR_493.3_403.2
843
FA7_HUMAN
3.9034
0.9398

FLYHK_354.2_284.2
802
AMBP_HUMAN
3.8982
0.9383

SGVDLADSNQK_567.3_591.3
958
VGFR3_HUMAN
3.8820
0.9369

LDGSTHLNIFFAK_488.3_739.4
887
PAPP1_HUMAN
3.8770
0.9354

HFQNLGK_422.2_527.2
50
AFAM_HUMAN
3.7628
0.9339

IAQYYYTFK_598.8_884.4
25
F13B_HUMAN
3.7040
0.9325

GFQALGDAADIR_617.3_717.4
11
TIMP1_HUMAN
3.6538
0.9310

ELPQSIVYK_538.8_417.7
808
FBLN3_HUMAN
3.6148
0.9295

IAQYYYTFK_598.8_395.2
25
F13B_HUMAN
3.5820
0.9280

GSLVQASEANLQAAQDFVR_668.7_735.4
851
ITIH1_HUMAN
3.5283
0.9266

TLPFSR_360.7_506.3
820
LYAM1_HUMAN
3.5064
0.9251

VNHVTLSQPK_374.9_244.2
3
B2MG_HUMAN
3.5045
0.9236

IAPQLSTEELVSLGEK_857.5_533.3
56
AFAM_HUMAN
3.4990
0.9222

VEHSDLSFSK_383.5_468.2
800
B2MG_HUMAN
3.4514
0.9207

TQILEWAAER_608.8_761.4
863
EGLN_HUMAN
3.4250
0.9192

AHQLAIDTYQEFEETYIPK_766.0_521.3
933
CSH_HUMAN
3.3634
0.9178

TEFLSNYLTNVDDITLVPGTLG
959
ENPP2_HUMAN
3.3512
0.9163

R_846.8_600.3

HFQNLGK_422.2_285.1
50
AFAM_HUMAN
3.3375
0.9148

VEHSDLSFSK_383.5_234.1
800
B2MG_HUMAN
3.3371
0.9134

TELRPGETLNVNFLLR_624.68_875.5
960
CO3_HUMAN
3.1889
0.9119

YQISVNK_426.2_292.1
829
FIBB_HUMAN
3.1668
0.9104

YGFYTHVFR_397.2_659.4
961
THRB_HUMAN
3.1188
0.9075

SEPRPGVLLR_375.2_454.3
816
FA7_HUMAN
3.1068
0.9060

IAPQLSTEELVSLGEK_857.5_333.2
56
AFAM_HUMAN
3.0917
0.9046

ILILPSVTR_506.3_785.5
679
PSGx_HUMAN
3.0346
0.9031

TLAFVR_353.7_492.3
806
FA7_HUMAN
3.0237
0.9016

AKPALEDLR_506.8_288.2
899
APOA1_HUMAN
3.0189
0.9001

TABLE 21

Random Forest SummedGini Early Window

SEQ ID

Transition
NO:
Protein
SumBestGini
Probability

LSETNR_360.2_330.2
892
PSG1_HUMAN
26.3610
1.0000

ALNFGGIGVVVGHELTHAFDD
34
ECE1_HUMAN
24.8946
0.9985

QGR_837.1_299.2

ELPQSIVYK_538.8_417.7
808
FBLN3_HUMAN
24.8817
0.9971

LDFHFSSDR_375.2_464.2
6
INHBC_HUMAN
24.3229
0.9956

LDFHFSSDR_375.2_611.3
6
INHBC_HUMAN
22.2162
0.9941

FSLVSGWGQLLDR_493.3_403.2
843
FA7_HUMAN
19.6528
0.9927

TSESGELHGLTTEEEFVEGIYK_819.06_310.2
44
TTHY_HUMAN
19.2430
0.9912

ATVVYQGER_511.8_751.4
10
APOH_HUMAN
19.1321
0.9897

IQTHSTTYR_369.5_627.3
59
F13B_HUMAN
17.1528
0.9883

ATVVYQGER_511.8_652.3
10
APOH_HUMAN
17.0214
0.9868

HYINLITR_515.3_301.1
885
NPY_HUMAN
16.6713
0.9853

FICPLTGLWPINTLK_887.0_685.4
804
APOH_HUMAN
15.0826
0.9838

AFLEVNEEGSEAAASTAVVIA
953
ANT3_HUMAN
14.6110
0.9824

GR_764.4_614.4

IQTHSTTYR_369.5_540.3
59
F13B_HUMAN
14.5473
0.9809

AHQLAIDTYQEFEETYIPK_766.0_521.3
933
CSH_HUMAN
14.0287
0.9794

TGAQELLR_444.3_530.3
893
GELS_HUMAN
13.1389
0.9780

DSPSVWAAVPGK_607.31_301.2
877
PROF1_HUMAN
12.9571
0.9765

NCSFSIIYPVVIK_770.4_555.4
818
CRHBP_HUMAN
12.5867
0.9750

ALALPPLGLAPLLNLWAKPQG
801
SHBG_HUMAN
12.1138
0.9721

R_770.5_256.2

DTDTGALLFIGK_625.8_818.5
799
PEDF_HUMAN
11.7054
0.9706

TSDQIHFFFAK_447.6_512.3
13
ANT3_HUMAN
11.4261
0.9692

IALGGLLFPASNLR_481.3_657.4
55
SHBG_HUMAN
11.0968
0.9677

DTDTGALLFIGK_625.8_217.1
799
PEDF_HUMAN
10.9040
0.9662

EQSLNVSQDLDTIR_539.9_758.4
956
SYNE2_HUMAN
10.6572
0.9648

IALGGLLFPASNLR_481.3_412.3
55
SHBG_HUMAN
10.0629
0.9633

FGFGGSTDSGPIR_649.3_745.4
962
ADA12_HUMAN
10.0449
0.9618

ETPEGAEAKPWYEPIYLGGVF
878
TNFA_HUMAN
10.0286
0.9604

QLEK_951.14_877.5

LPDTPQGLLGEAR_683.87_427.2
811
EGLN_HUMAN
9.8980
0.9589

FSVVYAK_407.2_381.2
1
FETUA_HUMAN
9.7971
0.9574

YGIEEHGK_311.5_599.3
810
CXA1_HUMAN
9.7850
0.9559

GFQALGDAADIR_617.3_717.4
11
TIMP1_HUMAN
9.7587
0.9545

VVLSSGSGPGLDLPLVLGLPLQ
38
SHBG_HUMAN
9.3421
0.9530

LK_791.5_598.4

HHGPTITAK_321.2_275.1
33
AMBP_HUMAN
9.2728
0.9515

ALALPPLGLAPLLNLWAKPQG
801
SHBG_HUMAN
9.2431
0.9501

R_770.5_457.3

LIEIANHVDK_384.6_498.3
911
ADA12_HUMAN
9.1368
0.9486

AFQVWSDVTPLR_709.88_347.2
884
MMP2_HUMAN
8.6789
0.9471

AFQVWSDVTPLR_709.88_385.3
884
MMP2_HUMAN
8.6339
0.9457

ETLLQDFR_511.3_322.2
9
AMBP_HUMAN
8.6252
0.9442

ETLLQDFR_511.3_565.3
9
AMBP_HUMAN
8.3957
0.9427

VNHVTLSQPK_374.9_459.3
3
B2MG_HUMAN
8.3179
0.9413

HHGPTITAK_321.2_432.3
33
AMBP_HUMAN
8.2567
0.9398

DTYVSSFPR_357.8_272.2
934
TCEA1_HUMAN
8.2028
0.9383

GGEGTGYFVDFSVR_745.9_722.4
35
HRG_HUMAN
8.0751
0.9369

DFNQFSSGEK_386.8_189.1
839
FETA_HUMAN
8.0401
0.9354

DVLLLVHNLPQNLTGHIWYK_791.8_883.0
805
PSG7_HUMAN
7.9924
0.9339

VSEADSSNADWVTK_754.9_347.2
964
CFAB_HUMAN
7.8630
0.9325

QGHNSVFLIK_381.6_260.2
845
HEMO_HUMAN
7.8588
0.9310

AQETSGEEISK_589.8_979.5
876
IBP1_HUMAN
7.7787
0.9295

DIPHWLNPTR_416.9_600.3
880
PAPP1_HUMAN
7.6393
0.9280

SPELQAEAK_486.8_788.4
2
APOA2_HUMAN
7.6248
0.9266

QGHNSVFLIK_381.6_520.4
845
HEMO_HUMAN
7.6042
0.9251

LIENGYFHPVK_439.6_343.2
66
F13B_HUMAN
7.5771
0.9236

DIIKPDPPK_511.8_342.2
853
IL12B_HUMAN
7.5523
0.9222

VNHVTLSQPK_374.9_244.2
3
B2MG_HUMAN
7.5296
0.9207

TELRPGETLNVNFLLR_624.68_875.5
960
CO3_HUMAN
7.4484
0.9178

QINSYVK_426.2_496.3
897
CBG_HUMAN
7.3266
0.9163

YNSQLLSFVR_613.8_734.5
827
TFR1_HUMAN
7.3262
0.9148

TVQAVLTVPK_528.3_855.5
7
PEDF_HUMAN
7.1408
0.9134

QTLSWTVTPK_580.8_818.4
881
PZP_HUMAN
6.9764
0.9119

DVLLLVHNLPQNLPGYFWYK_810.4_328.2
908
PSG9_HUMAN
6.9663
0.9104

FICPLTGLWPINTLK_887.0_756.9
804
APOH_HUMAN
6.8924
0.9090

TSYQVYSK_488.2_397.2
907
C163A_HUMAN
6.5617
0.9075

VVLSSGSGPGLDLPLVLGLPLQ
38
SHBG_HUMAN
6.4615
0.9060

LK_791.5_768.5

QINSYVK_426.2_610.3
897
CBG_HUMAN
6.4595
0.9046

LHKPGVYTR_357.5_479.3
947
HGFA_HUMAN
6.4062
0.9031

ALVLELAK_428.8_672.4
872
INHBE_HUMAN
6.3684
0.9016

YNSQLLSFVR_613.8_508.3
827
TFR1_HUMAN
6.3628
0.9001

TABLE 22

Random Forest SummedGini Early-Middle Combined Windows

SEQ ID

Transition
NO:
Protein
SumBestGini
Probability

ATVVYQGER_511.8_652.3
10
APOH_HUMAN
120.6132
1.0000

ATVVYQGER_511.8_751.4
10
APOH_HUMAN
99.7548
0.9985

IQTHSTTYR_369.5_627.3
59
F13B_HUMAN
57.5339
0.9971

IQTHSTTYR_369.5_540.3
59
F13B_HUMAN
55.0267
0.9956

FICPLTGLWPINTLK_887.0_685.4
804
APOH_HUMAN
49.9116
0.9941

AHQLAIDTYQEFEETYIPK_766.0_521.3
933
CSH_HUMAN
48.9796
0.9927

HHGPTITAK_321.2_432.3
33
AMBP_HUMAN
45.7432
0.9912

SPELQAEAK_486.8_659.4
2
APOA2_HUMAN
42.1848
0.9897

AHYDLR_387.7_566.3
42
FETUA_HUMAN
41.4591
0.9883

ETLLQDFR_511.3_565.3
9
AMBP_HUMAN
39.7301
0.9868

HHGPTITAK_321.2_275.1
33
AMBP_HUMAN
39.2096
0.9853

ETLLQDFR_511.3_322.2
9
AMBP_HUMAN
36.8033
0.9838

FICPLTGLWPINTLK_887.0_756.9
804
APOH_HUMAN
31.8246
0.9824

TVQAVLTVPK_528.3_855.5
7
PEDF_HUMAN
31.1356
0.9809

IALGGLLFPASNLR_481.3_657.4
55
SHBG_HUMAN
30.5805
0.9794

DVLLLVHNLPQNLTGHIWYK_791.8_883.0
805
PSG7_HUMAN
29.5729
0.9780

AHYDLR_387.7_288.2
42
FETUA_HUMAN
29.0239
0.9765

SPELQAEAK_486.8_788.4
2
APOA2_HUMAN
28.6741
0.9750

ETPEGAEAKPWYEPIYLGGVF
878
TNFA_HUMAN
26.8117
0.9736

QLEK_951.14_877.5

LDFHFSSDR_375.2_611.3
6
INHBC_HUMAN
26.0001
0.9721

DFNQFSSGEK_386.8_189.1
839
FETA_HUMAN
25.9113
0.9706

HFQNLGK_422.2_527.2
50
AFAM_HUMAN
25.7497
0.9692

DPDQTDGLGLSYLSSHIANVE
101
GELS_HUMAN
25.7418
0.9677

R_796.4_328.1

VVLSSGSGPGLDLPLVLGLPLQ
38
SHBG_HUMAN
25.6425
0.9662

LK_791.5_598.4

IALGGLLFPASNLR_481.3_412.3
55
SHBG_HUMAN
25.1737
0.9648

LDFHFSSDR_375.2_464.2
6
INHBC_HUMAN
25.0674
0.9633

LIQDAVTGLTVNGQITGDK_972.0_640.4
844
ITIH3_HUMAN
24.5613
0.9618

VVLSSGSGPGLDLPLVLGLPLQ
38
SHBG_HUMAN
23.2995
0.9604

LK_791.5_768.5

DIPHWLNPTR_416.9_600.3
880
PAPP1_HUMAN
22.9504
0.9589

VNHVTLSQPK_374.9_459.3
3
B2MG_HUMAN
22.2821
0.9574

QINSYVK_426.2_496.3
897
CBG_HUMAN
22.2233
0.9559

ALALPPLGLAPLLNLWAKPQG
801
SHBG_HUMAN
22.1160
0.9545

R_770.5_256.2

TELRPGETLNVNFLLR_624.68_875.5
960
CO3_HUMAN
21.9043
0.9530

ITQDAQLK_458.8_803.4
906
CBG_HUMAN
21.8933
0.9515

IAPQLSTEELVSLGEK_857.5_533.3
56
AFAM_HUMAN
21.4577
0.9501

QINSYVK_426.2_610.3
897
CBG_HUMAN
21.3414
0.9486

LIQDAVTGLTVNGQITGDK_972.0_798.4
844
ITIH3_HUMAN
21.2843
0.9471

DTDTGALLFIGK_625.8_818.5
799
PEDF_HUMAN
21.2631
0.9457

DVLLLVHNLPQNLPGYFWYK_810.4_328.2
908
PSG9_HUMAN
21.2547
0.9442

HFQNLGK_422.2_285.1
50
AFAM_HUMAN
20.8051
0.9427

DTDTGALLFIGK_625.8_217.1
799
PEDF_HUMAN
20.2572
0.9413

FLYHK_354.2_447.2
802
AMBP_HUMAN
19.6822
0.9398

NNQLVAGYLQGPNVNLEEK_700.7_999.5
822
IL1RA_HUMAN
19.2156
0.9383

VSFSSPLVAISGVALR_802.0_715.4
889
PAPP1_HUMAN
18.9721
0.9369

TVQAVLTVPK_528.3_428.3
7
PEDF_HUMAN
18.9392
0.9354

TFVNITPAEVGVLVGK_822.47_968.6
963
PROF1_HUMAN
18.9351
0.9339

LQVLGK_329.2_416.3
669
A2GL_HUMAN
18.6613
0.9325

TLAFVR_353.7_274.2
806
FA7_HUMAN
18.5095
0.9310

ITQDAQLK_458.8_702.4
906
CBG_HUMAN
18.5046
0.9295

DVLLLVHNLPQNLTGHIWYK_791.8_310.2
805
PSG7_HUMAN
18.4015
0.9280

VSFSSPLVAISGVALR_802.0_602.4
889
PAPP1_HUMAN
17.5397
0.9266

IAPQLSTEELVSLGEK_857.5_333.2
56
AFAM_HUMAN
17.5338
0.9251

TLFIFGVTK_513.3_215.1
676
PSG4_HUMAN
17.5245
0.9236

ALNFGGIGVVVGHELTHAFDD
34
ECE1_HUMAN
17.1108
0.9222

QGR_837.1_299.2

FLYHK_354.2_284.2
802
AMBP_HUMAN
16.9237
0.9207

LDGSTHLNIFFAK_488.3_739.4
887
PAPP1_HUMAN
16.8260
0.9192

ELIEELVNITQNQK_557.6_618.3
807
IL13_HUMAN
16.5607
0.9178

YNSQLLSFVR_613.8_734.5
827
TFR1_HUMAN
16.5425
0.9163

AFQVWSDVTPLR_709.88_385.3
884
MMP2_HUMAN
16.3293
0.9148

LDGSTHLNIFFAK_488.3_852.5
887
PAPP1_HUMAN
15.9820
0.9134

TPSAAYLWVGTGASEAEK_919.5_428.2
935
GELS_HUMAN
15.9084
0.9119

YTTEIIK_434.2_603.4
39
C1R_HUMAN
15.7998
0.9104

FSVVYAK_407.2_381.2
1
FETUA_HUMAN
15.4991
0.9090

VNHVTLSQPK_374.9_244.2
3
B2MG_HUMAN
15.2938
0.9075

SYTITGLQPGTDYK_772.4_680.3
114
FINC_HUMAN
14.9898
0.9060

DIPHWLNPTR_416.9_373.2
880
PAPP1_HUMAN
14.6923
0.9046

AFQVWSDVTPLR_709.88_347.2
884
MMP2_HUMAN
14.4361
0.9031

IAQYYYTFK_598.8_884.4
25
F13B_HUMAN
14.4245
0.9016

FSLVSGWGQLLDR_493.3_403.2
843
FA7_HUMAN
14.3848
0.9001

BIOMARKERS AND METHODS FOR PREDICTING PREECLAMPSIA

Information

Publication Number

Date Filed

Date Published

Inventors

Original Assignees

CPC

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International Classifications

Abstract

Description

Claims

Parent Case Info

Provisional Applications (1)