BIOMARKERS AND METHODS FOR PREDICTING PREECLAMPSIA

Abstract

The disclosure provides biomarker panels, methods and kits for determining the probability for preeclampsia in a pregnant female. 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.

Description

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, SPELQAEAK, VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK, and VVGGLVALR. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of LDFHFSSDR, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, GFQALGDAADIR. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of FSVVYAK, SPELQAEAK, VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK, VVGGLVALR, LDFHFSSDR, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, and GFQALGDAADIR.

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 L1 CAM (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 L1 CAM (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, SPELQAEAK, VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK, and VVGGLVALR.

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, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, GFQALGDAADIR.

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, SPELQAEAK, VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK, VVGGLVALR, LDFHFSSDR, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, and GFQALGDAADIR.

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 L1 CAM (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 SPELQAEAK, SSNNPHSPIVEEFQVPYN, VNHVTLSQPK, VVGGLVALR, and FSVVYAK. 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 SPELQAEAK, SSNNPHSPIVEEFQVPYN, VNHVTLSQPK, VVGGLVALR, and FSVVYAK.

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, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, GFQALGDAADIR. 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, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, GFQALGDAADIR.

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, SPELQAEAK, VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK, VVGGLVALR, LDFHFSSDR, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, and GFQALGDAADIR. 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, SPELQAEAK, VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK, VVGGLVALR, LDFHFSSDR, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, and GFQALGDAADIR.

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, SPELQAEAK, VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK, and VVGGLVALR.

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 L1 CAM (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 L1 CAM (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, SPELQAEAK, VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK, and VVGGLVALR.

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, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, GFQALGDAADIR.

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, SPELQAEAK, VNHVTLSQPK, SSNNPHSPIVEEFQVPYNK, VVGGLVALR, LDFHFSSDR, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, and GFQALGDAADIR

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 L1 CAM (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 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 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 she same experiment on she chromatographic time scale by rapidly toggling between the different precursor/fragment pairs to perform an MRM experiment. A series of transitions (precursor/fragment ion pairs) in combination with the retention time of the targeted analyte (e.g., peptide or small molecule such as chemical entity, steroid, hormone) can constitute a definitive assay. A large number of analytes can be quantified during a single LC-MS experiment. The term “scheduled,” or “dynamic” in reference to MRM or SRM, refers to a variation of the assay wherein the transitions for a particular analyte are only acquired in a time window around the expected retention time, significantly increasing the number of analytes that can be detected and quantified in a single LC-MS experiment and contributing to the selectivity of the test, as retention time is a property dependent on the physical nature of the analyte. A single analyte can also be monitored with more than one transition. Finally, included in the assay can be standards that correspond to the analytes of interest (e.g., same amino acid sequence), but differ by the inclusion of stable isotopes. Stable isotopic standards (SIS) can be incorporated into the assay at precise levels and used to quantify the corresponding unknown analyte. An additional level of specificity is contributed by the co-elution of the unknown analyte and its corresponding SIS and properties of their transitions (e.g., the similarity in the ratio of the level of two transitions of the unknown and the ratio of the two transitions of its corresponding SIS).

Mass spectrometry assays, instruments and systems suitable for biomarker peptide analysis can include, without limitation, matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) MS; MALDI-TOF post-source-decay (PSD); MALDI-TOF/TOF; surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF) MS; electrospray ionization mass spectrometry (ESI-MS); ESI-MS/MS; ESI-MS/(MS), (n is an integer greater than zero); ESI 3D or linear (2D) ion trap MS; ESI triple quadrupole MS; ESI quadrupole orthogonal TOF (Q-TOF); ESI Fourier transform MS systems; desorption/ionization on silicon (DIOS); secondary ion mass spectrometry (SIMS); atmospheric pressure chemical ionization mass spectrometry (APCI-MS); APCI-MS/MS; APCI-(MS)_n; 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 (MA), dot blotting, and FACS. In certain embodiments, the immunoassay is an ELISA. In yet a further embodiment, the ELISA is direct ELISA (enzyme-linked immunosorbent assay), indirect ELISA, sandwich ELISA, competitive ELISA, multiplex ELISA, ELISPOT technologies, and other similar techniques known in the art. Principles of these immunoassay methods are known in the art, for example John R. Crowther, The ELISA Guidebook, 1st ed., Humana Press 2000, ISBN 0896037282. Typically ELISAs are performed with antibodies but they can be performed with any capture agents that bind specifically to one or more biomarkers of the invention and that can be detected. Multiplex ELISA allows simultaneous detection of two or more analytes within a single compartment (e.g., microplate well) usually at a plurality of array addresses (Nielsen and Geierstanger 2004. J Immunol Methods 290: 107-20 (2004) and Ling et al. 2007. Expert Rev Mol Diagn 7: 87-98 (2007)).

In some embodiments, Radioimmunoassay (MA) can be used to detect one or more biomarkers in the methods of the invention. MA 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 (CIEF), capillary isotachophoresis (CITP), capillary electrochromatography (CEC), and the like, one-dimensional polyacrylamide gel electrophoresis (PAGE), two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), capillary gel electrophoresis (CGE), capillary zone electrophoresis (CZE), micellar electrokinetic chromatography (MEKC), free flow electrophoresis (FFE), etc.

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

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

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

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

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

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

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

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

Some embodiments disclosed herein relate to diagnostic and prognostic methods of determining the probability for 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 T² statistic; 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).
TSDQIHFFFA_K_447.56_512.3	0.00	ANT3_HUMAN
DPNGLPPEAQK_583.3_669.4	0.00	RET4_HUMAN
SVSLPSLDPASAK_636.35_885.5	0.00	APOB_HUMAN
SSNNPHSPIVEEFQVPYNK_729.36_261.2	0.00	C1S_HUMAN
IEGNLIFDPNNYLPK_873.96_414.2	0.00	APOB_HUMAN
YWGVASFLQK_599.82_849.5	0.00	RET4_HUMAN
ITENDIQIALDDAK_779.9_632.3	0.00	APOB_HUMAN
IEGNLIFDPNNYLPK_873.96_845.5	0.00	APOB_HUMAN
GWVTDGFSSLK_598.8_953.5	0.00	APOC3_HUMAN
TGISPLALIK_506.82_741.5	0.00	APOB_HUMAN
SVSLPSLDPASAK_636.35_473.3	0.00	APOB_HUMAN
IIGGSDADIK_494.77_762.4	0.00	C1S_HUMAN
TGISPLALIK_506.82_654.5	0.00	APOB_HUMAN
TLLIANETLR_572.34_703.4	0.00	IL5_HUMAN
YWGVASFLQK_599.82_350.2	0.00	RET4_HUMAN
VSALLTPAEQTGTWK_801.43_371.2	0.00	APOB_HUMAN
DPNGLPPEAQK_583.3_497.2	0.00	RET4_HUMAN
VNHVTLSQPK_561.82_673.4	0.00	B2MG_HUMAN
DALSSVQESQVAQQAR_572.96_502.3	0.00	APOC3_HUMAN
IAQYYYTFK_598.8_884.4	0.00	F13B_HUMAN
IEEIAAK_387.22_531.3	0.00	CO5_HUMAN
GWVTDGFSSLK_598.8_854.4	0.00	APOC3_HUMAN
VNHVTLSQPK_561.82_351.2	0.00	B2MG_HUMAN
ITENDIQIALDDAK_779.9_873.5	0.00	APOB_HUMAN
VSALLTPAEQTGTWK_801.43_585.4	0.00	APOB_HUMAN
VILGAHQEVNLEPHVQEIEVSR_832.78_860.4	0.00	PLMN_HUMAN
SPELQAEAK_486.75_788.4	0.00	APOA2_HUMAN
SPELQAEAK_486.75_659.4	0.00	APOA2_HUMAN
DYWSTVK_449.72_620.3	0.00	APOC3_HUMAN
VPLALFALNR_557.34_620.4	0.00	PEPD_HUMAN
TSDQIHFFFAK_447.56_659.4	0.00	ANT3_HUMAN
DALSSVQESQVAQQAR_572.96_672.4	0.00	APOC3_HUMAN
VIAVNEVGR_478.78_284.2	0.00	CHL1_HUMAN
LLEVPEGR_456.76_686.3	0.00	C1S_HUMAN
VEPLYELVTATDFAYSSTVR_754.38_549.3	0.00	CO8B_HUMAN
HHGPTITAK_321.18_275.1	0.01	AMBP_HUMAN
ALNFGGIGVVVGHELTHAFDDQGR_837.09_299.2	0.01	ECE1_HUMAN
ETLLQDFR_511.27_565.3	0.01	AMBP_HUMAN
HHGPTITAK_321.18_432.3	0.01	AMBP_HUMAN
IIGGSDADIK_494.77_260.2	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 byprotein ID.
Transition	cox pvalues	protein
HHGPTITAK_321.1_275.1	0.01	AMBP_HUMAN
ETLLQDFR_511.27_565.3	0.01	AMBP_HUMAN
HHGPTITAK_321.18_432.3	0.01	AMBP_HUMAN
TSDQIHFFFAK_447.56_512.3	0.00	ANT3_HUMAN
TSDQIHFFFAK_447.56_659.4	0.00	ANT3_HUMAN
SPELQAEAK_486.75_788.4	0.00	APOA2_HUMAN
SPELQAEAK_486.75_659.4	0.00	APOA2_HUMAN
SVSLPSLDPASAK_636.35_885.5	0.00	APOB_HUMAN
IEGNLIFDPNNYLPK_873.96_414.2	0.00	APOB_HUMAN
ITENDIQIALDDAK_779.9_632.3	0.00	APOB_HUMAN
IEGNLIFDPNNYLPK_873.96_845.5	0.00	APOB_HUMAN
TGISPLALIK_506.82_741.5	0.00	APOB_HUMAN
SVSLPSLDPASAK_636.35_73.3	0.00	APOB_HUMAN
TGISPLALIK_506.82_654.5	0.00	APOB_HUMAN
VSALLTPAEQTGTWK_801.43_371.2	0.00	APOB_HUMAN
ITENDIQIALDDAK_779.9_873.5	0.00	APOB_HUMAN
VSALLTPAEQTGTWK_801.43_585.4	0.00	APOB_HUMAN
GWVTDGFSSLK_598.8_953.5	0.00	APOC3_HUMAN
DALSSVQESQVAQQAR_572.96_502.3	0.00	APOC3_HUMAN
GWVTDGFSSLK_598.8_854.4	0.00	APOC3_HUMAN
DYWSTVK_449.72_620.3	0.00	APOC3_HUMAN
DALSSVQESQVAQQAR_572.96_672.4	0.00	APOC3_HUMAN
VNHVTLSQPK_561.82_673.4	0.00	B2MG_HUMAN
VNHVTLSQPK_561.82_351.2	0.00	B2MG_HUMAN
SSNNPHSPIVEEFQVPYNK_729.36_261.2	0.00	C1S_HUMAN
IIGGSDADIK_494.77_762.4	0.00	C1S_HUMAN
LLEVPEGR_456.76_686.3	0.00	C1S_HUMAN
IIGGSDADIK_494.77_260.2	0.01	C1S_HUMAN
VIAVNEVGR_478.78_284.2	0.00	CHL1_HUMAN
IEEIAAK_387.22_531.3	0.00	CO5_HUMAN
VEPLYELVTATDFAYSSTVR_754.38_549.3	0.00	CO8B_HUMAN
ALNFGGIGVVVGHELTHAFDDQGR_837.09_299.2	0.01	ECE1_HUMAN
IAQYYYTFK_598.8_884.4	0.00	F13B_HUMAN
TLLIANETLR_572.34_703.4	0.00	IL5_HUMAN
VPLALFALNR_557.34_620.4	0.00	PEPD_HUMAN
VILGAHQEVNLEPHVQEIEVSR_832.78_860.4	0.00	PLMN_HUMAN
DPNGLPPEAQK_583.3_669.4	0.00	RET4_HUMAN
YWGVASFLQK_599.82_849.5	0.00	RET4_HUMAN
YWGVASFLQK_599.82_350.2	0.00	RET4_HUMAN
DPNGLPPEAQK_583.3_497.2	0.00	RET4_HUMAN

TABLE 3
Transitions selected by Cox stepwise AIC analysis
Transition	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	44.40	1.91E+19	18.20	2.44	0.01
GGEGTGYFVDFSVR_745.85_869.5	6.91	1.00E+03	2.76	2.51	0.01
SPEQQETVLDGNLIIR_906.48_685.4	17.28	3.21E+07	7.49	2.31	0.02
EPGLCTWQSLR_673.83_790.4	−2.08	1.25E−01	1.02	−2.05	0.04

TABLE 4
Transitions selected by Cox lasso analysis
Transition	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	0.68781	1.98936	0.4278	1.608	0.1079
SSNNPHSPIVEEFQVPYNK_72_9.36261.2	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 withthe highest ROC area are shown.
Transition                       ROC area
SPELQAEAK_486.75_788.4           0.92
SSNNPHSPIVEEFQVPYNK_729.36_261.2 0.88
VNHVTLSQPK_561.82_673.4          0.85
TLLIANETLR_572.34_703.4          0.84
SSNNPHSPIVEEFQVPYNK_729.36_521.3 0.83
IIGGSDADIK_494.77_762.4          0.82
VVGGLVALR_442.29_784.5           0.82
ALNFGGIGVVVGHELTHAFDDQGR_837.09_299.2 0.81
DYWSTVK_449.72_620.3             0.81
FSVVYAK_407.23_579.4             0.81
GWVTDGFSSLK_598.8_953.5          0.81
IIGGSDADIK_494.77_260.2          0.81
LLEVPEGR_456.76_356.2            0.81
DALSSVQESQVAQQAR_572.96_672.4    0.80
DPNGLPPEAQK_583.3_497.2          0.80
FSVVYAK_407.23_381.2             0.80
LLEVPEGR_456.76_686.3            0.80
SPELQAEAK_486.75_659.4           0.80
VVLSSGSGPGLDLPLVLGLPLQLK_791.48_598.4 0.79
ETLLQDFR_511.27_565.3            0.79
VNHVTLSQPK_561.82_351.2          0.79
VVGGLVALR_442.29_685.4           0.79
YTTEIIK_434.25_603.4             0.79
DPNGLPPEAQK_583.3_669.4          0.78
EDTPNSVWEPAK_686.82_315.2        0.78
GWVTDGFSSLK_598.8_854.4          0.78
HHGPTITAK_321.18_432.3           0.78
LHEAFSPVSYQHDLALLR_699.37_251.2  0.78
GA.ofTime.to.Event.in.Days       0.77
DALSSVQESQVAQQAR_572.96_502.3    0.77
DYWSTVK_449.72_347.2             0.77
IAQYYYTFK_598.8_395.2            0.77
YWGVASFLQK_599.82_849.5          0.77
AHYDLR_387.7_288.2               0.76
EDTPNSVWEPAK_686.82_630.3        0.76
GDTYPAELYITGSILR_884.96_922.5    0.76
SVSLPSLDPASAK_636.35_885.5       0.76
TSESGELHGLTTEEEFVEGIYK_819.06_310.2 0.76
ALEQDLPVNIK_620.35_570.4         0.75
HHGPTITAK_321.18_275.1           0.75
IAQYYYTFK_598.8_884.4            0.75
ITENDIQIALDDAK_779.9_632.3       0.75
LPNNVLQEK_527.8_844.5            0.75
YWGVASFLQK_599.82_350.2          0.75
FQLPGQK_409.23_276.1             0.75
HTLNQIDEVK_598.82_958.5          0.75
VVLSSGSGPGLDLPLVLGLPLQLK_791.48_768.5 0.75
DADPDTFFAK_563.76_302.1          0.74
DADPDTFFAK_563.76_825.4          0.74
FQLPGQK_409.23_429.2             0.74
HFQNLGK_422.23_527.2             0.74
VIAVNEVGR_478.78_284.2           0.74
VPLALFALNR_557.34_620.4          0.74
ETLLQDFR_511.27_322.2            0.73
FNAVLTNPQGDYDTSTGK_964.46_262.1  0.73
SVSLPSLDPASAK_636.35_473.3       0.73
AHYDLR_387.7_566.3               0.72
ALNHLPLEYNSALYSR_620.99_538.3    0.72
AWVAWR_394.71_258.1              0.72
AWVAWR_394.71_531.3              0.72
ETAASLLQAGYK_626.33_879.5        0.72
IALGGLLFPASNLR_481.29_657.4      0.72
IAPQLSTEELVSLGEK_857.47_533.3    0.72
ITENDIQIALDDAK_779.9_873.5       0.72
VAPEEHPVLLTEAPLNPK_652.03_869.5  0.71
EPGLCTWQSLR_673.83_375.2         0.71
IAPQLSTEELVSLGEK_857.47_333.2    0.71
SPEQQETVLDGNLIIR_906.48_699.3    0.71
VSALLTPAEQTGTWK_801.43_371.2     0.71
VSALLTPAEQTGTWK_801.43_585.4     0.71
VSEADSSNADWVTK_754.85_347.2      0.71
GDTYPAELYITGSILR_884.96_274.1    0.70
IPGIFELGISSQSDR_809.93_849.4     0.70
IQTHSTTYR_369.52_540.3           0.70
LLDSLPSDTR_558.8_890.4           0.70
QLGLPGPPDVPDHAAYHPF_676.67_299.2 0.70
SYELPDGQVITIGNER_895.95_251.1    0.70
VILGAHQEVNLEPHVQEIEVSR_832.78_860.4 0.70
WGAAPYR_410.71_577.3             0.69
DFHINLFQVLPWLK_885.49_543.3      0.69
LLDSLPSDTR_558.8_76.2            0.69
VEPLYELVTATDFAYSSTVR_754.38_549.3 0.69
VPTADLEDVLPLAEDITNILSK_789.43_841.4 0.69
GGEGTGYFVDFSVR_745.85_869.5      0.69
HTLNQIDEVK_598.82_951.5          0.69
LIENGYFHPVK_439.57_627.4         0.69
LPNNVLQEK_527.8_730.4            0.69
NKPGVYTDVAYYLAWIR_677.02_545.3   0.69
NTVISVNPSTK_580.32_845.5         0.69
QLGLPGPPDVPDHAAYHPF_676.67_263.1 0.69
YTTEIIK_434.25_704.4             0.69
LPDATPK_371.21-628.3             0.68
IEGNLIFDPNNYLPK_873.96_845.5     0.68
LEQGENVFLQATDK_796.4_822.4       0.68
TLYSSSPR_455.74_533.3            0.68
TLYSSSPR_455.74_696.3            0.68
VSEADSSNADWVTK_754.85_533.3      0.68
DGSPDVTTADIGANTPDATK_973.45_844.4 0.67
EWVAIESDSVQPVPR_856.44_486.2     0.67
IALGGLLFPASNLR_481.29_412.3      0.67
IEEIAAK_387.22_531.3             0.67
IEGNLIFDPNNYLPK_873.96_414.2     0.67
LYYGDDEK_501.72_726.3            0.67
TGISPLALIK_506.82_741.5          0.67
VPTADLEDVLPLAEDITNILSK_89.43_940.5 0.67
ADSQAQLLLSTVVGVFTAPGLHLK_822.46_983.6 0.66
AYSDLSR_406.2_577.3              0.66
DFHINLFQVLPWLK_885.49_400.2      0.66
DLHLSDVFLK_396.22_260.2          0.66
EWVAIESDSVQPVPR_856.44_468.3     0.66
FNAVLTNPQGDYDTSTGK_964.46_333.2  0.66
LSSPAVITDK_515.79_743.4          0.66
LYYGDDEK_501.72_563.2            0.66
SGFSFGFK_438.72_732.4            0.66
IIEVEEEQEDPYLNDR_995.97_777.4    0.66
AVYEAVLR_460.76_750.4            0.66
WGAAPYR_410.71_34.3              0.66
FTFTLHLETPKPSISSSNLNPR_829.44_874.4 0.65
DAQYAPGYDK_564.25_315.1          0.65
YGLVTYATYPK_638.33_334.2         0.65
DGSPDVTTADIGANTPDATK_973.45_531.3 0.65
ETAASLLQAGYK_626.33_679.4        0.65
ALNHLPLEYNSALYSR_620.99_696.4    0.65
DISEVVTPR_508.27_787.4           0.65
IS.2_662.3_313.1                 0.65
IVLGQEQDSYGGK_697.35_261.2       0.65
IVLGQEQDSYGGK_697.35_754.3       0.65
TLEAQLTPR_514.79_685.4           0.65
VPVAVQGEDTVQSLTQGDGVAK_733.38_775.4 0.65
VAPEEHPVLLTEAPLNPK_652.03_568.3  0.64
ADSQAQLLLSTVVGVFTAPGLHLK_822.46_664.4 0.64
AEAQAQYSAAVAK_654.33_908.5       0.64
DISEVVTPR_508.27_472.3           0.64
ELLESYIDGR_597.8_710.3           0.64
TGISPLALIK_506.82_654.5          0.64
TNLESILSYPK_632.84_807.5         0.64
DAQYAPGYDK_564.25_813.4          0.63
LPTAVVPLR_483.31_755.5           0.63
DSPVLIDFFEDTER_841.9_512.3       0.63
FAFNLYR_465.75_712.4             0.63
FVFGTTPEDILR_697.87_843.5        0.63
GDSGGAFAVQDPNDK_739.33_473.2     0.63
SLDFTELDVAAEK_719.36_316.2       0.63
SLLQPNK_400.24_599.4             0.63
TLLIANETLR_572.34_816.5          0.63
VILGAHQEVNLEPHVQEIEVSR_832.78_603.3 0.63
VQEAHLTEDQIFYFPK_655.66_701.4    0.63
FTFTLHLETPKPSISSSNLNPR_829.44_787.4 0.63
AYSDLSR_406.2_375.2              0.62
DDLYVSDAFHK_655.31_344.1         0.62
DDLYVSDAFHK_655.31_704.3         0.62
DPDQTDGLGLSYLSSHIANVER_796.39_456.2 0.62
ESDTSYVSLK_564.77_347.2          0.62
ESDTSYVSLK_564.77_696.4          0.62
FVFGTTPEDILR_697.87_742.4        0.62
ILDDLSPR_464.76_587.3            0.62
LEQGENVFLQATDK_796.4_675.4       0.62
LHEAFSPVSYQHDLALLR_699.37_380.2  0.62
LIENGYFHPVK_439.57_343.2         0.62
SLPVSDSVLSGFEQR_810.92_836.4     0.62
TWDPEGVIFYGDTNPK_919.93_403.2    0.62
VGEYSLYIGR_578.8_708.4           0.62
VIAVNEVGR_478.78_744.4           0.62
VPGTSTSATLTGLTR_731.4_761.5      0.62
YEVQGEVFTKPQLWP_910.96_293.1     0.62
AFTECCVVASQLR_770.87_673.4       0.61
APLTKPLK_289.86_357.3            0.61
DSPVLIDFFEDTER_841.9_399.2       0.61
ELLESYIDGR_597.8_839.4           0.61
FLQEQGHR_338.84_369.2            0.61
IQTHSTTYR_369.52_627.3           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      0.61
LQGTLPVEAR_542.31_842.5          0.61
NKPGVYTDVAYYLAWIR_677.02_821.5   0.61
SLDFTELDVAAEK_719.36_874.5       0.61
SYTITGLQPGTDYK_772.39_352.2      0.61
TASDFITK_441.73_710.4            0.61
VLSALQAVQGLLVAQGR_862.02_941.6   0.61
VTGWGNLK_437.74_617.3            0.61
YEVQGEVFTKPQLWP_910.96_392.2     0.61
AFIQLWAFDAVK_704.89_650.4        0.60
APLTKPLK_289.86_260.2            0.60
GYVIIKPLVWV_643.9_304.2          0.60
IITGLLEFEVYLEYLQNR_738.4_822.4   0.60
ILDDLSPR_464.76_702.3            0.60
LSSPAVITDK_515.79_830.5          0.60
TDAPDLPEENQAR_728.34_843.4       0.60
TFTLLDPK_467.77_359.2            0.60
TFTLLDPK_467.77_686.4            0.60
VLEPTLK_400.25_587.3             0.60
YEFLNGR_449.72_606.3             0.60
YGLVTYATYPK_638.3_843.4          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.
	rf	boosting
1	FSVVYAK_	DPNGLPPEAQK_
	407.23_579.4	583.3_497.2
2	SPELQAEAK_	ALNFGGIGVVVGHELTHAFDDQGR_
	486.75_788.4	_837.09_299.2
3	VNHVTLSQPK_	ALEQDLPVNIK_
	561.82_673.4	620.35_570.4
4	SSNNPHSPIVEEFQVPYNK_	DALSSVQESQVAQQAR_
	729.36_261.2	572.96_502.3
5	SSNNPHSPIVEEFQVPYNK_	AHYDLR_
	729.36_521.3	387.7_288.2
6	VVGGLVALR_	FQLPGQK_
	442.29_784.5	409.23_276.1
7	FQLPGQK_	AFTECCVVASQLR_
	409.23_276.1	770.87_673.4
8	TLLIANETLR_	ALNHLPLEYNSALYSR_
	572.34_703.4	620.99_538.3
9	DYWSTVK_	ADSQAQLLLSTVVGVFTAPGLHLK_
	449.72_620.3	822.46_664.4
10	VVGGLVALR_	AEAQAQYSAAVAK_
	442.29_685.4	654.33_908.5
11	DPNGLPPEAQK_	ADSQAQLLLSTVVGVFTAPGLHLK_
	583.3_497.2	822.46_983.6
12	LLEVPEGR_	AITPPHPASQANIIFDITEGNLR_
	456.76_356.2	825.77_459.3
13	GWVTDGFSSLK_	Collection.Window.
	598.8_953.5	GA.in.Days
14	VILGAHQEVNLEPHVQEIEVSR_	AEAQAQYSAAVAK_
	832.78_860.4	654.33_709.4
15	FQLPGQK_	AFIQLWAFDAVK_
	409.23_429.2	704.89_650.4
	lasso	logit
1	SPELQAEAK_	AFIQLWAFDAVK_
	486.75_788.4	704.89_650.4
2	VILGAHQEVNLEPHVQEIEVSR_	AFIQLWAFDAVK_
	832.78_860.4	704.89_836.4
3	VVGGLVALR_	AEAQAQYSAAVAK_
	442.29_784.5	654.33_709.4
4	TSESGELHGLTTEEEFVEGIYK_	AFTECCVVASQLR_
	819.06_310.2	770.87_574.3
5	SSNNPHSPIVEEFQVPYNK_	ADSQAQLLLSTVVGVFTAPGLHLK_
	729.36_261.2	822.46_664.4
6	VVLSSGSGPGLDLPLVLGLPLQLK_	AEAQAQYSAAVAK_
	_791.48_598.4	654.33908.5
7	ALEQDLPVNIK_	ADSQAQLLLSTVVGVFTAPGLHLK_
	620.35_570.4	822.46_983.6
8	IQTHSTTYR_	AFTECCVVASQLR_
	369.52_540.3	770.87_673.4
9	SSNNPHSPIVEEFQVPYNK_	Collection.Window.GA.
	729.36_521.3	in.Days
10	FSVVYAK_	AHYDLR_
	407.23_579.4	387.7_288.2
11	IAQYYYTFK_	AHYDLR_
	598.8_884.4	387.7_566.3
12	IAQYYYTFK_	AITPPHPASQANIIFDITEGNLR_
	598.8_395.2	825.77_459.3
13	GDTYPAELYITGSILR_	AITPPHPASQANIIFDITEGNLR_
	884.96_922.5	825.77_917.5
14	SPEQQETVLDGNLIIR_	ALEQDLPVNIK_
	906.48_699.3	620.35_570.4
15	IAPQLSTEELVSLGEK_	ALEQDLPVNIK_
	857.47_533.3	620.35_798.5

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, SSNNPHSPIVEEFQVPYN, VNHVTLSQPK, VVGGLVALR, and FSVVYAK, LDFHFSSDR, TVQAVLTVPK, GPGEDFR, ETLLQDFR, ATVVYQGER, and GFQALGDAADIR. 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 (MARS 14), 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 (le) 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 (le) 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, 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

SPELQAEAK_486.75_788.4

VNHVTLSQPK_561.82_673.4

SSNNPHSPIVEEFQVPYNK_729.36_261.2

SSNNPHSPIVEEFQVPYNK_729.36_521.3

VVGGLVALR_442.29_784.5

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.

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.

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

Parent and fragment ion data were used to search the Human RefSeq database using the Sequest (Eng et al., J. Am. Soc. Mass Spectrom 1994; 5:976-989) and X!Tandem (Craig and Beavis, Bioinformatics 2004; 20:1466-1467) algorithms. For Sequest, data was searched with a 20 ppm tolerance for the parent ion and 1 AMU for the fragment ion. Two missed trypsin cleavages were allowed, and modifications included static cysteine carboxyamidomethylation and methionine oxidation. After searching the data was filtered by charge state vs. Xcorr scores (charge +1≥1.5 Xcorr, charge +2≥2.0, charge +3≥2.5). Similar search parameters were used for X!tandem, except the mass tolerance for the fragment ion was 0.8 AMU and there is no Xcorr filtering. Instead, the PeptideProphet algorithm (Keller et al., Anal. Chem 2002; 74:5383-5392) was used to validate each X!Tandem peptide-spectrum assignment and protein assignments were validated using ProteinProphet algorithm (Nesvizhskii et al., Anal. Chem 2002; 74:5383-5392). Data was filtered to include only the peptide-spectrum matches that had PeptideProphet probability of 0.9 or more. After compiling peptide and protein identifications, spectral count data for each peptide were imported into DAnTE software (Polpitiya et al., Bioinformatics. 2008; 24:1556-1558). Log transformed data was mean centered and missing values were filtered, by requiring that a peptide had to be identified in at least 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
Protein			XT_	S_
description	Uniprot ID (name)	Peptide	AUC	AUC
afamin	P43652	R.IVQIYKDLLR.N	0.67	0.63
	(AFAM_HUMAN)
afamin	P43652	K.VMNHICSK.Q	0.73	0.74
	(AFAM_HUMAN)
afamin	P43652	R.RHPDLSIPELLR.I	0.86	0.83
	(AFAM_HUMAN)
afamin	P43652	K.HFQNLGK.D	0.71	0.75
	(AFAM_HUMAN)
alpha-1-	P01011	K.ITLLSALVETR.T	0.68	0.70
antichymotrypsin	(AACT_HUMAN)
alpha-1-	P01011	R.LYGSEAFATDFQDSAAAK.K	0.70	0.78
antichymotrypsin	(AACT_HUMAN)
alpha-1-	P01011	R.NLAVSQVVHK.A	0.81	0.79
antichymotrypsin	(AACT_HUMAN)
alpha-1B-	P04217	R.CEGPIPDVTFELLR.E	0.78	0.60
glycoprotein	(A1BG_HUMAN)
alpha-1B-	P04217	R.LHDNQNGWSGDSAPVELILSDETLPAPEFSPEPESGR.	0.72	0.66
glycoprotein	(A1BG_HUMAN)	A
alpha-1B-	P04217	R.CEGPIPDVTFELLR.E	0.64	0.60
glycoprotein	(A1BG_HUMAN)
alpha-1B-	P04217	R.TP GAAANLELIFVGPQHAGNYR.C	0.71	0.67
glycoprotein	(A1BG_HUMAN)
alpha-1B-	P04217	K.LLELTGPK.S	0.70	0.66
glycoprotein	(A1BG_HUMAN)
alpha-1B-	P04217	R.ATW SGAVLAGR.D	0.84	0.74
glycoprotein	(A1BG_HUMAN)
alpha-2-	P08697	K.HQM*DLVATLSQLGLQELFQAPDLR.G	0.67	0.67
antiplasmin	(A2AP_HUMAN)
alpha-2-	P08697	K.LGNQEPGGQTALK.S	0.83	0.83
antiplasmin	(A2AP_HUMAN)
alpha-2-	P08697	K.GFPIKEDFLEQSEQLFGAKPVSLTGK.Q	0.68	0.65
antiplasmin	(A2AP_HUMAN)
alpha-2-HS-	P02765	R.QPNCDDPETEEAALVAIDYINQNLPWGYK.H	0.61	0.61
glycoprotein	(FETUA_HUMAN)
preproprotein
alpha-2-HS-	P02765	K.VWPQQPSGELFEIEIDTLETTCHVLDPTPVAR.C	0.79	0.67
glycoprotein	(FETUA_HUMAN)
preproprotein
alpha-2-HS-	P02765	K.EHAVEGDCDFQLLK.L	0.90	0.77
glycoprotein	(FETUA_HUMAN)
preproprotein
alpha-2-HS-	P02765	R.QPNCDDPETEEAALVAIDYINQNLPWGYK.H	0.63	0.61
glycoprotein	(FETUA_HUMAN)
preproprotein
alpha-2-HS-	P02765	K.HTLNQIDEVK.V	0.70	0.68
glycoprotein	(FETUA_HUMAN)
preproprotein
alpha-2-HS-	P02765	R.TVVQPSVGAAAGPVVPPCPGR.I	0.83	0.83
glycoprotein	(FETUA_HUMAN)
preproprotein
angiotensinogen	P01019	K.TGCSLMGASVDSTLAFNTYVHFQGK.M	0.75	0.67
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	R.AAM*VGMLANFLGFR.I	0.65	0.63
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	R.AAMVGMLANFLGFR.I	0.65	0.64
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	R.AAM*VGM*LANFLGFR.I	0.65	0.65
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	R.AAMVGM*LANFLGFR.I	0.65	0.74
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	K.QPFVQGLALYTPVVLPR.S	0.60	0.74
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	R.AAM*VGMLANFLGFR.I	0.64	0.63
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	R.AAMVGMLANFLGFR.I	0.64	0.64
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	R.AAM*VGM*LANFLGFR.I	0.64	0.65
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	R.AAMVGM*LANFLGFR.I	0.64	0.74
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	K.VLSALQAVQGLLVAQGR.A	0.74	0.77
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	K.QPFVQGLALYTPVVLPR.S	0.75	0.74
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	R.ADSQAQLLLSTVVGVFTAPGLHLK.Q	0.78	0.77
preproprotein	(ANGT_HUMAN)
antithrombin-III	P01008	R.ITDVIPSEAINELTVLVLVNTIYFK.G	0.78	0.78
	(ANT3_HUMAN)
antithrombin-III	P01008	K.NDNDNIFLSPLSISTAFAMTK.L	0.87	0.83
	(ANT3_HUMAN)
antithrombin-III	P01008	R.EVPLNTIIFMGR.V	0.69	0.62
	(ANT3_HUMAN)
antithrombin-III	P01008	R.EVPLNTIIFM*GR.V	0.69	0.69
	(ANT3_HUMAN)
antithrombin-III	P01008	R.VAEGTQVLELPFKGDDITM*VLILPKPEK.S	0.83	0.92
	(ANT3_HUMAN)
antithrombin-III	P01008	R.VAEGTQVLELPFKGDDITMVLILPKPEK.S	0.83	0.96
	(ANT3_HUMAN)
antithrombin-III	P01008	K.EQLQDMGLVDLFSPEK.S	0.85	0.86
	(ANT3_HUMAN)
antithrombin-III	P01008	R.VAEGTQVLELPFKGDDITM*VLILPKPEK.S	0.94	0.92
	(ANT3_HUMAN)
antithrombin-III	P01008	R.VAEGTQVLELPFKGDDITMVLILPKPEK.S	0.94	0.96
	(ANT3_HUMAN)
antithrombin-III	P01008	R.EVPLNTIIFMGR.V	0.63	0.62
	(ANT3_HUMAN)
antithrombin-III	P01008	R.EVPLNTIIFM*GR.V	0.63	0.69
	(ANT3_HUMAN)
antithrombin-III	P01008	R.DIPMNPMCIYR.S	0.71	0.70
	(ANT3_HUMAN)
apolipoprotein	P02652	K.EPCVESLVSQYFQTVTDYGK.D	0.83	0.83
A-IT	(APOA2_HUMAN)
preproprotein
apolipoprotein	P06727	K.SLAELGGHLDQQVEEFR.R	0.67	0.67
A-IV	(APOA4_HUMAN)
apolipoprotein	P06727	R.LAPLAEDVR.G	0.67	0.90
A-IV	(APOA4_HUMAN)
apolipoprotein	P06727	R.VLRENADSLQASLRPHADELK.A	0.79	0.63
A-IV	(APOA4_HUMAN)
apolipoprotein	P06727	R.SLAPYAQDTQEKLNHQLEGLTFQMK.K	0.90	0.65
A-IV	(APOA4_HUMAN)
apolipoprotein	P06727	R.SLAPYAQDTQEKLNHQLEGLTFQM*K.K	0.90	0.69
A-IV	(APOA4_HUMAN)
apolipoprotein	P06727	K.LGPHAGDVEGHLSFLEK.D	0.63	0.73
A-IV	(APOA4_HUMAN)
apolipoprotein	P06727	K.SELTQQLNALFQDKLGEVNTYAGDLQK.K	0.68	0.68
A-IV	(APOA4_HUMAN)
apolipoprotein	P06727	R.SLAPYAQDTQEKLNHQLEGLTFQMK.K	0.71	0.65
A-IV	(APOA4_HUMAN)
apolipoprotein	P06727	R.SLAPYAQDTQEKLNHQLEGLTFQM*K.K	0.71	0.69
A-IV	(APOA4_HUMAN)
apolipoprotein	P06727	R.LLPHANEVSQK.I	0.62	0.79
A-IV	(APOA4_HUMAN)
apolipoprotein	P06727	K.SLAELGGHLDQQVEEFRR.R	0.67	0.69
A-IV	(APOA4_HUMAN)
apolipoprotein	P06727	K.SELTQQLNALFQDK.L	0.68	0.62
A-IV	(APOA4_HUMAN)
apolipoprotein	P04114	K.GFEPTLEALFGK.Q	0.73	0.76
B-100	(APOB_HUMAN)
apolipoprotein	P04114	K.ALYWVNGQVPDGVSK.V	0.78	0.67
B-100	(APOB_HUMAN)
apolipoprotein	P04114	K.FIIPSPK.R	0.90	0.90
B-100	(APOB_HUMAN)
apolipoprotein	P04114	R.TPALHFK.S	0.68	0.81
B-100	(APOB_HUMAN)
apolipoprotein	P04114	K.TEVIPPLIENR.Q	0.62	0.64
B-100	(APOB_HUMAN)
apolipoprotein	P04114	R.NLQNNAEWVYQGAIR.Q	0.65	0.60
B-100	(APOB_HUMAN)
apolipoprotein	P04114	K.LPQQANDYLNSFNWER.Q	0.65	0.62
B-100	(APOB_HUMAN)
apolipoprotein	P04114	R.LAAYLMLMR.S	0.60	0.73
B-100	(APOB_HUMAN)
apolipoprotein	P04114	R.VIGNMGQTMEQLTPELK.S	0.68	0.67
B-100	(APOB_HUMAN)
apolipoprotein	P04114	K.LIVAMSSWLQK.A	0.74	0.86
B-100	(APOB_HUMAN)
apolipoprotein	P04114	R.TSSFALNLPTLPEVK.F	0.79	0.70
B-100	(APOB_HUMAN)
apolipoprotein	P04114	K.IADFELPTIIVPEQTIEIPSIK.F	0.62	0.61
B-100	(APOB_HUMAN)
apolipoprotein	P04114	K.IEGNLIFDPNNYLPK.E	0.63	0.62
B-100	(APOB_HUMAN)
apolipoprotein	P04114	R.TSSFALNLPTLPEVKFPEVDVLTK.Y	0.66	0.72
B-100	(APOB_HUMAN)
apolipoprotein	P04114	R.LELELRPTGEIEQYSVSATYELQR.E	0.78	0.78
B-100	(APOB_HUMAN)
apolipoprotein	P02655	K.STAAMSTYTGIFTDQVLSVLK.G	0.73	0.73
C-II	(APOC2_HUMAN)
apolipoprotein	P02656	R.GWVTDGFSSLKDYWSTVKDK.F	1.00	1.00
C-III	(APOC3_HUMAN)
apolipoprotein E	P02649	R.WELALGR.F	0.60	0.63
	(APOE_HUMAN)
apolipoprotein E	P02649	R.LAVYQAGAR.E	0.61	0.64
	(APOE_HUMAN)
apolipoprotein E	P02649	K.SWFEPLVEDMQR.Q	0.83	0.73
	(APOE_HUMAN)
apolipoprotein E	P02649	R.AATVGSLAGQPLQER.A	0.67	0.67
	(APOE_HUMAN)
apolipoprotein(a)	P08519	R.TPEYYPNAGLIMNYCR.N	0.72	0.61
	(APOA_HUMAN)
beta-2-	P02749	K.TFYEPGEEITYSCKPGYVSR.G	0.66	0.76
glycoprotein 1	(APOH_HUMAN)
beta-2-	P02749	K.FICPLTGLWPINTLK.C	0.72	0.70
glycoprotein 1	(APOH_HUMAN)
bone marrow	P13727	R.SLQTFSQAWFTCR.R	0.82	0.72
proteoglycan	(PRG2_HUMAN)
ceruloplasmin	P00450	K.HYYIGIIETTWDYASDHGEKK.L	0.78	0.89
	(CERU_HUMAN)
ceruloplasmin	P00450	R.EYTDASFTNRK.E	0.63	0.63
	(CERU_HUMAN)
ceruloplasmin	P00450	K.M*YYSAVDPTKDIFTGLIGPMK.I	0.66	0.68
	(CERU_HUMAN)
ceruloplasmin	P00450	K.M*YYSAVDPTKDIFTGLIGPM*K.I	0.66	0.76
	(CERU_HUMAN)
ceruloplasmin	P00450	R.SGAGTEDSACIPWAYYSTVDQVKDLYSGLIGPLIVCR.	0.95	0.95
	(CERU_HUMAN)	R
ceruloplasmin	P00450	R.KAEEEHLGILGPQLHADVGDKVK.I	0.85	0.77
	(CERU_HUMAN)
ceruloplasmin	P00450	K.EVGPTNADPVCLAK.M	0.62	0.77
	(CERU_HUMAN)
ceruloplasmin	P00450	R.MYSVNGYTFGSLPGLSMCAEDR.V	0.63	0.71
	(CERU_HUMAN)
ceruloplasmin	P00450	K.DIASGLIGPLIICK.K	0.63	0.66
	(CERU_HUMAN)
ceruloplasmin	P00450	R.QKDVDKEFYLFPTVFDENESLLLEDNIR.M	0.64	0.66
	(CERU_HUMAN)
ceruloplasmin	P00450	R.GPEEEHLGILGPVIWAEVGDTIR.V	0.65	0.61
	(CERU_HUMAN)
ceruloplasmin	P00450	K.M*YYSAVDPTKDIFTGLIGPMK.I	0.67	0.68
	(CERU_HUMAN)
ceruloplasmin	P00450	K.M*YYSAVDPTKDIFTGLIGPM*K.I	0.67	0.76
	(CERU_HUMAN)
ceruloplasmin	P00450	K.M*YYSAVDPTKDIFTGLIGPMK.I	0.67	0.68
	(CERU_HUMAN)
ceruloplasmin	P00450	K.M*YYSAVDPTKDIFTGLIGPM*K.I	0.67	0.76
	(CERU_HUMAN)
ceruloplasmin	P00450	K.GAYPLSIEPIGVR.F	0.67	0.63
	(CERU_HUMAN)
ceruloplasmin	P00450	R.GVYS SDVFDIFPGTYQTLEM*FPR.T	0.67	0.67
	(CERU_HUMAN)
ceruloplasmin	P00450	K.DIASGLIGPLIICKK.D	0.67	0.73
	(CERU_HUMAN)
ceruloplasmin	P00450	R.SGAGTEDSACIPWAYYSTVDQVK.D	0.70	0.70
	(CERU_HUMAN)
ceruloplasmin	P00450	R.IYHSHIDAPK.D	0.77	0.76
	(CERU_HUMAN)
ceruloplasmin	P00450	R.ADDKVYPGEQYTYMLLATEEQSPGEGDGNCVTR.I	0.77	0.80
	(CERU_HUMAN)
ceruloplasmin	P00450	K.DLYSGLIGPLIVCR.R	0.78	0.82
	(CERU_HUMAN)
ceruloplasmin	P00450	R.TTIEKPVWLGFLGPIIK.A	0.88	0.85
	(CERU_HUMAN)
cholinesterase	P06276	K.IFFPGVSEFGK.E	0.87	0.76
	(CHLE_HUMAN)
cholinesterase	P06276	R.AILQSGSFNAPWAVTSLYEAR.N	1.00	0.83
	(CHLE_HUMAN)
coagulation	P00748	R.LHEAFSPVSYQHDLALLR.L	0.72	0.76
factor XII	(FA12_HUMAN)
coagulation	P05160	R.GDTYPAELYITGSILR.M	0.67	0.83
factor XIII B	(F13B_HUMAN)
chain
coagulation	P05160	K.VLHGDLIDFVCK.Q	0.69	0.60
factor XIII B	(F13B_HUMAN)
chain
complement C1r	P00736	K.LVFQQFDLEPSEGCFYDYVK.I	0.69	0.66
subcomponent	(C1R_HUMAN)
complement C1s	P09871	R.VKNYVDWIMK.T	0.69	0.60
subcomponent	(C1S_HUMAN)
complement C1s	P09871	K.SNALDIIFQTDLTGQK.K	0.75	0.70
subcomponent	(C1S_HUMAN)
complement C2	P06681	R.DFHINLFR.M	0.75	0.72
	(CO2_HUMAN)
complement C2	P06681	R.GALISDQWVLTAAHCFR.	0.60	0.75
	(CO2_HUMAN)	D
complement C2	P06681	K.KNQGILEFYGDDIALLK.	0.62	0.67
	(CO2_HUMAN)	L
complement C3	P01024	R.IHWESASLLR.S	0.80	0.77
	(CO3_HUMAN)
complement C4-	P0C0L5	R.VHYTVCIWR.N	0.67	0.65
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.AEMADQAAAWLTR.Q	0.78	0.89
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.M*RPSTDTITVMVENSHGLR.V	0.65	0.65
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.MRPSTDTITVMVENSHGLR.V	0.65	0.72
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	R.VQQPDCREPFLSCCQFAESLRK.K	0.67	0.60
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.LVNGQSHISLSK.A	0.73	0.73
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	R.GQIVFMNREPK.R	0.80	0.62
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.VGLSGM*AIADVTLLSGFHALR.A	0.80	0.80
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.VGLSGMAIADVTLLSGFHALR.A	0.80	0.83
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	R.GHLFLQTDQPIYNPGQR.V	0.70	0.68
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.M*RPSTDTITVMVENSHGLR.V	0.75	0.65
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.MRPSTDTITVMVENSHGLR.V	0.75	0.72
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.SHALQLNNR.Q	0.76	0.70
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	R.YVSHFETEGPHVLLYFDSVPTSR.E	0.88	0.89
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	R.GSSTWLTAFVLK.V	0.61	0.72
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	R.YIYGKPVQGVAYVR.F	0.63	0.73
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.SCGLHQLLR.G	0.65	0.65
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	R.GPEVQLVAHSPWLK.D	0.69	0.73
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	R.KKEVYM*PSSIFQDDFVIPDISEPGTWK.I	0.70	0.67
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	R.KKEVYMPSSIFQDDFVIPDISEPGTWK.I	0.70	0.69
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	R.VQQPDCREPFLSCCQFAESLR.K	0.76	0.74
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.VGLSGM*AIADVTLLSGFHALR.A	0.80	0.80
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.VGLSGMAIADVTLLSGFHALR.A	0.80	0.83
B-like	(CO4B_HUMAN)
preproprotein
complement C4-	P0C0L5	K.ASAGLLGAHAAAITAYALTLTK.A	0.85	0.83
B-like	(CO4B_HUMAN)
preproprotein
complement C5	P01031	K.ITHYNYLILSK.G	0.73	0.73
preproprotein	(CO5_HUMAN)
complement C5	P01031	R.KAFDICPLVK.I	0.83	0.87
preproprotein	(CO5_HUMAN)
complement C5	P01031	R.IPLDLVPK.T	0.90	0.63
preproprotein	(CO5_HUMAN)
complement C5	P01031	R.MVETTAYALLTSLNLKDINYVNPVIK.W	0.92	0.75
preproprotein	(CO5_HUMAN)
complement C5	P01031	K.ALLVGEHLNIIVTPK.S	1.00	0.87
preproprotein	(CO5_HUMAN)
complement C5	P01031	K.LKEGMLSIMSYR.N	0.62	0.75
preproprotein	(CO5_HUMAN)
complement C5	P01031	R.YIYPLDSLTWIEYWPR.D	0.70	0.69
preproprotein	(CO5_HUMAN)
complement C5	P01031	K.GGSASTWLTAFALR.V	0.63	0.83
preproprotein	(CO5_HUMAN)
complement C5	P01031	R.YGGGFYSTQDTINAIEGLTEYSLLVK.Q	0.73	0.74
preproprotein	(CO5_HUMAN)
complement	P13671	K.AKDLHLSDVFLK.A	0.63	0.62
component C6	(CO6_HUMAN)
complement	P13671	K.ALNHLPLEYNSALYSR.I	0.60	0.62
component C6	(CO6_HUMAN)
complement	P10643	R.LSGNVLSYTFQVK.I	0.71	0.63
component C7	(CO7_HUMAN)
complement	P07357	R.KDDIMLDEGMLQSLMELPDQYNYGMYAK.F	0.78	0.89
component C8	(CO8A_HUMAN)
alpha chain
complement	P07358	R.DFGTHYITEAVLGGIYEYTLVMNK.E	0.80	0.73
component C8	(CO8B_HUMAN)
beta chain
preproprotein
complement	P07358	R.DTMVEDLVVLVR.G	0.88	0.76
component C8	(CO8B_HUMAN)
beta chain
preproprotein
complement	P07358	R.YYAGGCSPHYILNTR.F	0.70	0.71
component C8	(CO8B_HUMAN)
beta chain
preproprotein
complement	P07360	R.SLPVSDSVLSGFEQR.V	0.79	0.81
component C8	(CO8G_HUMAN)
gamma chain
complement	P07360	R.VQEAHLTEDQIFYFPK.Y	0.98	0.84
component C8	(CO8G_HUMAN)
gamma chain
complement	P02748	R.TAGYGINILGMDPLSTPFDNEFYNGLCNR.D	0.62	0.64
component C9	(CO9_HUMAN)
complement	P02748	R.RPWNVASLIYETK.G	0.60	0.74
component C9	(CO9_HUMAN)
complement	P02748	R.AIEDYINEFSVRK.C	0.67	0.67
component C9	(CO9_HUMAN)
complement	P02748	R.AIEDYINEFSVR.K	0.77	0.79
component C9	(CO9_HUMAN)
complement	P00751	R.LEDSVTYHCSR.G	0.60	0.60
factor B	(CFAB_HUMAN)
preproprotein
complement	P00751	R.FIQVGVISWGVVDVCK.N	0.67	0.79
factor B	(CFAB_HUMAN)
preproprotein
complement	P00751	R.DFHINLFQVLPWLK.E	0.78	0.76
factor B	(CFAB_HUMAN)
preproprotein
complement	P00751	K.YGQTIRPICLPCTEGTTR.	0.60	0.70
factor B	(CFAB_HUMAN)	A
preproprotein
complement	P00751	R.LLQEGQALEYVCPSGFYPYPVQTR.T	0.74	0.74
factor B	(CFAB_HUMAN)
preproprotein
complement	P08603	R.RPYFPVAVGK.Y	0.67	0.70
factor H	(CFAH_HUMAN)
complement	P08603	K.CTSTGWIPAPR.C	0.70	0.66
factor H	(CFAH_HUMAN)
complement	P08603	K.CLHPCVISR.E	0.94	0.64
factor H	(CFAH_HUMAN)
complement	P08603	R.EIMENYNIALR.W	0.67	0.71
factor H	(CFAH_HUMAN)
complement	P08603	K.CLHPCVISR.E	0.75	0.64
factor H	(CFAH_HUMAN)
complement	P08603	K.AVYTCNEGYQLLGEINYR.E	0.73	0.62
factor H	(CFAH_HUMAN)
complement	P08603	R.SITCIFIGVWTQLPQCVAIDK.L	0.61	0.61
factor H	(CFAH_HUMAN)
complement	P08603	R.WQSIPLCVEK.I	0.65	0.65
factor H	(CFAH_HUMAN)
complement	P08603	K.TDCLSLPSFENAIPMGEK.K	0.74	0.77
factor H	(CFAH_HUMAN)
complement	P08603	K.CFEGFGIDGPAIAK.C	0.76	0.69
factor H	(CFAH_HUMAN)
complement	P08603	K.CFEGFGIDGPAIAK.C	0.83	0.69
factor H	(CFAH_HUMAN)
complement	P08603	K.IDVHLVPDR.K	0.61	0.67
factor H	(CFAH_HUMAN)
complement	P08603	K.SSNLIILEEHLK.N	0.77	0.69
factor H	(CFAH_HUMAN)
complement	P05156	R.AQLGDLPWQVAIK.D	0.66	0.69
factor I	(CFAI_HUMAN)
preproprotein
complement	P05156	R.VFSLQWGEVK.L	0.69	0.77
factor I	(CFAI_HUMAN)
preproprotein
corticosteroid-	P08185	R.WSAGLTSSQVDLYIPK.V	0.63	0.61
binding globulin	(CBG_HUMAN)
fibrinogen alpha	P02671	K.TFPGFFSPMLGEFVSETESR.G	0.80	0.78
chain	(FIBA_HUMAN)
gelsolin	P06396	R.IEGSNKVPVDPATYGQFYGGDSYIILYNYR.H	0.78	0.78
	(GELS_HUMAN)
gelsolin	P06396	R.AQPVQVAEGSEPDGFWEALGGK.A	0.62	0.65
	(GELS_HUMAN)
gelsolin	P06396	K.TPSAAYLWVGTGASEAEKTGAQELLR.V	0.78	0.78
	(GELS_HUMAN)
gelsolin	P06396	R.VEKFDLVPVPTNLYGDFFTGDAYVILK.T	0.61	0.63
	(GELS_HUMAN)
gelsolin	P06396	R.EVQGFESATFLGYFK.S	0.87	0.88
	(GELS_HUMAN)
gelsolin	P06396	K.NWRDPDQTDGLGLSYLSSHIANVER.V	0.89	0.89
	(GELS_HUMAN)
gelsolin	P06396	K.TPSAAYLWVGTGASEAEK.T	0.87	0.77
	(GELS_HUMAN)
glutathione	P22352	K.FLVGPDGIPIIVIR.W	0.85	0.77
peroxidase 3	(GPX3_HUMAN)
hemopexin	P02790	R.LEKEVGTPHGIILDSVDAAFICPGSSR.L	0.93	0.74
	(HEMO_HUMAN)
hemopexin	P02790	R.WKNFPSPVDAAFR.Q	0.64	0.82
	(HEMO_HUMAN)
hemopexin	P02790	R.GECQAEGVLFFQGDREWFWDLATGTMK.E	0.60	0.64
	(HEMO_HUMAN)
hemopexin	P02790	R.GECQAEGVLFFQGDREWFWDLATGTM*K.E	0.60	0.83
	(HEMO_HUMAN)
hemopexin	P02790	R.GECQAEGVLFFQGDREWFWDLATGTMK.E	0.93	0.64
	(HEMO_HUMAN)
hemopexin	P02790	R.GECQAEGVLFFQGDREWFWDLATGTM*K.E	0.93	0.83
	(HEMO_HUMAN)
hemopexin	P02790	K.EVGTPHGIILDSVDAAFICPGSSR.L	0.62	0.69
	(HEMO_HUMAN)
hemopexin	P02790	R.LWWLDLK.S	0.64	0.64
	(HEMO_HUMAN)
hemopexin	P02790	K.NFPSPVDAAFR.Q	0.65	0.72
	(HEMO_HUMAN)
hemopexin	P02790	R.EWFWDLATGTMK.E	0.68	0.65
	(HEMO_HUMAN)
hemopexin	P02790	K.GGYTLVSGYPK.R	0.69	0.65
	(HEMO_HUMAN)
hemopexin	P02790	K.LYLVQGTQVYVFLTK.G	0.69	0.76
	(HEMO_HUMAN)
heparin cofactor	P05546	R.EYYFAEAQIADFSDPAFISK.T	0.80	0.78
2	(HEP2_HUMAN)
heparin cofactor	P05546	K.QFPILLDFK.T	0.62	1.00
2	(HEP2 HUMAN)
heparin cofactor	P05546	K.QFPILLDFK.T	0.64	1.00
2	(HEP2 HUMAN)
heparin cofactor	P05546	K.FAFNLYR.V	0.70	0.60
2	(HEP2 HUMAN)
histidine-rich	P04196	R.DGYLFQLLR.I	0.65	0.65
glycoprotein	(HRG HUMAN)
insulin-like	P35858	R.SFEGLGQLEVLTLDHNQLQEVK.A	0.75	0.83
growth factor-	(ALS_HUMAN)
binding protein
complex acid
labile subunit
insulin-like	P35858	R.TFTPQPPGLER.L	0.75	0.60
growth factor-	(ALS_HUMAN)
binding protein
complex acid
labile subunit
insulin-like	P35858	R.AFWLDVSHNR.L	0.77	0.75
growth factor-	(ALS_HUMAN)
binding protein
complex acid
labile subunit
insulin-like	P35858	R.LAELPADALGPLQR.A	0.66	0.64
growth factor-	(ALS_HUMAN)
binding protein
complex acid
labile subunit
insulin-like	P35858	R.LEALPNSLLAPLGR.L	0.70	0.67
growth factor-	(ALS_HUMAN)
binding protein
complex acid
labile subunit
insulin-like	P35858	R.NLIAAVAPGAFLGLK.A	0.70	0.68
growth factor-	(ALS_HUMAN)
binding protein
complex acid
labile subunit
inter-alpha-	P19827	R.QAVDTAVDGVFIR.S	0.60	0.64
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19827	K.TAFISDFAVTADGNAFIGDIK.D	0.81	0.86
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19827	R.GHMLENHVER.L	0.63	0.61
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19827	R.GHM*LENHVER.L	0.63	0.70
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19827	K.TAFISDFAVTADGNAFIG	0.75	0.60
trypsin inhibitor	DIKDKVTAWK.Q
heavy chain H1	(ITIH1_HUMAN)
inter-alpha-	P19827	R.GIEILNQVQESLPELSNHASILIMLTDGDPTEGVTDR.	0.80	0.80
trypsin inhibitor	(ITIH1_HUMAN)	S
heavy chain H1
inter-alpha-	P19827	K.ILGDM*QPGDYFDLVLFGTR.V	0.85	0.79
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19827	K.LDAQASFLPK.E	0.88	0.75
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19827	R.GFSLDEATNLNGGLLR.G	0.80	0.80
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19827	K.TAFISDFAVTADGNAFIGDIKDK.V	0.93	0.96
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19827	K.GSLVQASEANLQAAQDFVR.G	0.60	0.65
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19827	R.GHMLENHVER.L	0.64	0.61
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19827	R.GHM*LENHVER.L	0.64	0.70
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19827	R.LWAYLTIQELLAK.R	0.72	0.74
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19827	R.EVAFDLEIPK.T	0.78	0.62
trypsin inhibitor	(ITIH1_HUMAN)
heavy chain H1
inter-alpha-	P19823	R.SILQMSLDHHIVTPLTSLVIENEAGDER.M	0.76	0.76
trypsin inhibitor	(ITIH2_HUMAN)
heavy chain H2
inter-alpha-	P19823	R.SILQM*SLDHHIVTPLTSLVIENEAGDER.M	0.76	0.80
trypsin inhibitor	(ITIH2_HUMAN)
heavy chain H2
inter-alpha-	P19823	R.SILQMSLDHHIVTPLTSLVIENEAGDER.M	0.77	0.76
trypsin inhibitor	(ITIH2_HUMAN)
heavy chain H2
inter-alpha-	P19823	R.SILQM*SLDHHIVTPLTSLVIENEAGDER.M	0.77	0.80
trypsin inhibitor	(ITIH2_HUMAN)
heavy chain H2
inter-alpha-	P19823	K.AGELEVFNGYFVHFFAPDNLDPIPK.N	0.79	0.76
trypsin inhibitor	(ITIH2_HUMAN)
heavy chain H2
inter-alpha-	P19823	R.ETAVDGELVVLYDVK.R	0.94	0.97
trypsin inhibitor	(ITIH2_HUMAN)
heavy chain H2
inter-alpha-	P19823	R.NVQFNYPHTSVTDVTQNNFHNYFGGSEIVVAGK.F	0.74	0.83
trypsin inhibitor	(ITIH2_HUMAN)
heavy chain H2
inter-alpha-	P19823	R.FLHVPDTFEGHFDGVPVISK.G	0.81	0.81
trypsin inhibitor	(ITIH2_HUMAN)
heavy chain H2
inter-alpha-	Q14624	K.YIFHNFM*ER.L	0.70	0.73
trypsin inhibitor	(ITIH4_HUMAN)
heavy chain H4
inter-alpha-	Q14624	R.SFAAGIQALGGTNINDAMLMAVQLLDSSNQEER.L	0.75	0.75
trypsin inhibitor	(ITIH4_HUMAN)
heavy chain H4
inter-alpha-	Q14624	R.NMEQFQVSVSVAPNAK.I	1.00	1.00
trypsin inhibitor	(ITIH4_HUMAN)
heavy chain H4
inter-alpha-	Q14624	R.VQGNDHSATR.E	0.85	0.86
trypsin inhibitor	(ITIH4_HUMAN)
heavy chain H4
inter-alpha-	Q14624	K.WKETLFSVMPGLK.M	0.66	0.69
trypsin inhibitor	(ITIH4_HUMAN)
heavy chain H4
inter-alpha-	Q14624	K.AGFSWIEVTFK.N	0.78	0.82
trypsin inhibitor	(ITIH4_HUMAN)
heavy chain H4
inter-alpha-	Q14624	R.DQFNLIVFSTEATQWRPSLVPASAENVNK.A	0.61	0.60
trypsin inhibitor	(ITIH4_HUMAN)
heavy chain H4
inter-alpha-	Q14624	R.LWAYLTIQQLLEQTVSASDADQQALR.N	0.66	0.66
trypsin inhibitor	(ITIH4_HUMAN)
heavy chain H4
kallistatin	P29622	K.FSISGSYVLDQILPR.L	0.79	0.72
	(KAIN_HUMAN)
kininogen-1	P01042	K.AATGECTATVGKR.S	0.76	0.60
	(KNG1_HUMAN)
kininogen-1	P01042	K.ENFLFLTPDCK.S	0.71	0.68
	(KNG1_HUMAN)
kininogen-1	P01042	R.DIPTNSPELEETLTHTITK.L	0.65	0.64
	(KNG1_HUMAN)
kininogen-1	P01042	K.IYPTVNCQPLGM*ISLMK.R	0.66	0.60
	(KNG1_HUMAN)
kininogen-1	P01042	K.IYPTVNCQPLGMISLMK.R	0.66	0.62
	(KNG1_HUMAN)
kininogen-1	P01042	K.IYPTVNCQPLGMISLM*K.R	0.66	0.63
	(KNG1_HUMAN)
kininogen-1	P01042	R.IGEIKEETTSHLR.S	0.67	0.70
	(KNG1_HUMAN)
kininogen-1	P01042	K.YNSQNQSNNQFVLYR.I	0.76	0.65
	(KNG1_HUMAN)
kininogen-1	P01042	K.TVGSDTFYSFK.Y	0.78	0.77
	(KNG1_HUMAN)
leucine-rich	P02750	R.DGFDISGNPWICDQNLSDLYR.W	0.73	0.73
alpha-2-	(A2GL_HUMAN)
glycoprotein
leucine-rich	P02750	R.NALTGLPPGLFQASATLDTLVLK.E	0.79	0.79
alpha-2-	(A2GL_HUMAN)
glycoprotein
leucine-rich	P02750	K.ALGHLDLSGNR.L	0.71	0.71
alpha-2-	(A2GL_HUMAN)
glycoprotein
leucine-rich	P02750	R.VAAGAFQGLR.Q	0.71	0.77
alpha-2-	(A2GL_HUMAN)
glycoprotein
lipopolysaccharide-	P18428	R.SPVTLLAAVMSLPEEHNK.M	0.65	0.61
binding	(LBP_HUMAN)
protein
lumican	P51884	K.SLEYLDLSFNQIAR.L	0.93	0.96
	(LUM_HUMAN)
monocyte	P08571	R.LTVGAAQVPAQLLVGALR.V	0.68	0.63
differentiation	(CD14_HUMAN)
antigen CD14
N-	Q96PD5	R.EGKEYGVVLAPDGSTVAVEPLLAGLEAGLQGR.R	0.64	0.64
acetylmuramoyl-	(PGRP2_HUMAN)
L-alanine
amidase
N-	Q96PD5	K.EFTEAFLGCPAIHPR.C	0.63	0.62
acetylmuramoyl-	(PGRP2_HUMAN)
L-alanine
amidase
N-	Q96PD5	R.TDCPGDALFDLLR.T	0.88	0.86
acetylmuramoyl-	(PGRP2_HUMAN)
L-alanine
amidase
phosphatidylinos itol-	P80108	K.VAFLTVTLHQGGATR.M	0.63	0.65
glycan-	(PHLD_HUMAN)
specific
phospholipase D
pigment	P36955	R.ALYYDLISSPDIHGTYKELLDTVTAPQK.N	0.69	0.65
epithelium-	(PEDF_HUMAN)
derived factor
pigment	P36955	K.TVQAVLTVPK.L	0.72	0.62
epithelium-	(PEDF_HUMAN)
derived factor
pigment	P36955	R.LDLQEINNWVQAQMK.G	0.67	0.68
epithelium-	(PEDF_HUMAN)
derived factor
plasma kallikrein	P03952	R.LVGITSWGEGCAR.R	1.00	0.67
preproprotein	(KLKB1_HUMAN)
plasma protease	P05155	K.TNLESILSYPKDFTCVHQALK.G	0.83	0.83
C1 inhibitor	(IC1_HUMAN)
plasma protease	P05155	R.LVLLNAIYLSAK.W	0.64	0.61
C1 inhibitor	(IC1_HUMAN)
plasma protease	P05155	K.FQPTLLTLPR.I	0.86	0.77
C1 inhibitor	(IC1_HUMAN)
plasminogen	P00747	R.HSIFTPETNPR.A	0.66	0.64
	(PLMN_HUMAN)
plasminogen	P00747	R.FVTWIEGVMR.N	0.65	0.74
	(PLMN_HUMAN)
PREDICTED:	P0C0L4	R.GQIVFMNR.E	0.75	0.61
complement C4-	(CO4A_HUMAN)
A
PREDICTED:	P0C0L4	R.DSSTWLTAFVLK.V	0.65	0.67
complement C4-	(CO4A_HUMAN)
A
PREDICTED:	P0C0L4	R.YLDKTEQWSTLPPETK.D	0.70	0.60
complement C4-	(CO4A_HUMAN)
A
PREDICTED:	P0C0L4	R.DFALLSLQVPLK.D	0.78	0.62
complement C4-	(CO4A_HUMAN)
A
PREDICTED:	P0C0L4	R.TLEIPGNSDPNMIPDGDFNSYVR.V	0.74	0.78
complement C4-	(CO4A_HUMAN)
A
PREDICTED:	P0C0L4	R.EMSGSPASGIPVK.V	0.88	0.88
complement C4-	(CO4A_HUMAN)
A
PREDICTED:	P0C0L4	K.LHLETDSLALVALGALDTALYAAGSK.S	0.68	0.64
complement C4-	(CO4A_HUMAN)
A
PREDICTED:	P0C0L4	R.GCGEQTMIYLAPTLAASR.Y	0.71	0.67
complement C4-	(CO4A_HUMAN)
A
pregnancy zone	P20742	R.NELIPLIYLENPR.R	1.00	0.67
protein	(PZP_HUMAN)
pregnancy zone	P20742	K.LEAGINQLSFPLSSEPIQGSYR.V	1.00	0.73
protein	(PZP_HUMAN)
pregnancy zone	P20742	R.NQGNTWLTAFVLK.T	0.73	0.78
protein	(PZP_HUMAN)
pregnancy zone	P20742	R.AFQPFFVELTMPYSVIR.G	0.83	0.88
protein	(PZP_HUMAN)
pregnancy zone	P20742	R.IQHPFTVEEFVLPK.F	0.65	0.79
protein	(PZP_HUMAN)
pregnancy zone	P20742	K.ALLAYAFSLLGK.Q	0.69	0.74
protein	(PZP_HUMAN)
pregnancy-	P11464	R.TLFLLGVTK.Y	0.74	0.83
specific beta-1-	(PSG1_HUMAN)/
glycoprotein 1/	Q9UQ74
8/4	(PSG8_HUMAN)/
	Q00888
	(PSG4_HUMAN)
protein AMBP	P02760	R.TVAACNLPIVR.G	0.78	0.77
preproprotein	(AMBP_HUMAN)
protein AMBP	P02760	K.WYNLAIGSTCPWLK.K	0.80	0.80
preproprotein	(AMBP_HUMAN)
protein Z-	Q9UK55	K.LILVDYILFK.G	0.69	0.62
dependent	(ZPI_HUMAN)
protease inhibitor
prothrombin	P00734	R.KSPQELLCGASLISDR.W	0.63	0.65
preproprotein	(THRB_HUMAN)
prothrombin	P00734	R.TATSEYQTFFNPR.T	0.79	0.61
preproprotein	(THRB_HUMAN)
prothrombin	P00734	R.VTGWGNLKETWTANVGK.G	1.00	0.71
preproprotein	(THRB_HUMAN)
prothrombin	P00734	R.IVEGSDAEIGMSPWQVMLFR.K	0.65	0.61
preproprotein	(THRB_HUMAN)
prothrombin	P00734	K.HQDFNSAVQLVENFCR.N	0.65	0.64
preproprotein	(THRB_HUMAN)
prothrombin	P00734	R.IVEGSDAEIGM*SPWQVMLFR.K	0.65	0.80
preproprotein	(THRB_HUMAN)
prothrombin	P00734	R.IVEGSDAEIGMSPWQVM*LFR.K	0.65	1.00
preproprotein	(THRB_HUMAN)
prothrombin	P00734	R.RQECSIPVCGQDQVTVAMTPR.S	0.74	0.73
preproprotein	(THRB_HUMAN)
prothrombin	P00734	R.LAVTTHGLPCLAWASAQAK.A	0.76	0.80
preproprotein	(THRB_HUMAN)
prothrombin	P00734	K.GQPSVLQVVNLPIVERPVCK.D	0.76	0.67
preproprotein	(THRB_HUMAN)
retinol-binding	P02753	R.LLNLDGTCADSYSFVFSR.D	0.70	0.66
protein 4	(RET4_HUMAN)
sex hormone-	P04278	R.LFLGALPGEDSSTSFCLNGLWAQGQR.L	0.72	0.72
binding globulin	(SHBG_HUMAN)
sex hormone-	P04278	R.TWDPEGVIFYGDTNPKDDWFMLGLR.D	0.75	0.76
binding globulin	(SHBG_HUMAN)
sex hormone-	P04278	R.IALGGLLFPASNLR.L	0.62	0.72
binding globulin	(SHBG_HUMAN)
sex hormone-	P04278	K.VVLSSGSGPGLDLPLVLGLPLQLK.L	0.65	0.68
binding globulin	(SHBG_HUMAN)
thyroxine-	P05543	K.AVLHIGEK.G	0.64	0.75
binding globulin	(THBG_HUMAN)
thyroxine-	P05543	K.GWVDLFVPK.F	0.60	0.61
binding globulin	(THBG_HUMAN)
thyroxine-	P05543	K.FSISATYDLGATLLK.M	0.62	0.64
binding globulin	(THBG_HUMAN)
thyroxine-	P05543	R.SILFLGK.V	0.66	0.63
binding globulin	(THBG_HUMAN)
transforming	Q15582	R.LTLLAPLNSVFK.D	0.78	0.65
growth factor-	(BGH3_HUMAN)
beta-induced
protein ig-h3
vitamin D-	P02774	K.EYANQFMWEYSTNYGQAPLSLLVSYTK.S	0.67	0.64
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.EYANQFM*WEYSTNYGQAPLSLLVSYTK.S	0.67	0.67
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.ELPEHTVK.L	0.79	0.74
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	R.RTHLPEVFLSK.V	0.63	0.76
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.TAMDVFVCTYFMPAAQLPELPDVELPTNK.D	0.66	0.63
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.LPDATPTELAK.L	0.67	0.73
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.EYANQFMWEYSTNYGQAPLSLLVSYTK.S	0.65	0.64
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.EYANQFM*WEYSTNYGQAPLSLLVSYTK.S	0.65	0.67
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.ELSSFIDKGQELCADYSENTFTEYKK.K	0.71	0.73
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.EDFTSLSLVLYSR.K	0.71	0.75
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.HQPQEFPTYVEPTNDEICEAFRK.D	0.77	0.75
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.HQPQEFPTYVEPTNDEICEAFR.K	0.60	0.67
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	R.KFPSGTFEQVSQLVK.E	0.62	0.61
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.ELSSFIDKGQELCADYSENTFTEYK.K	0.64	0.64
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.EFSHLGKEDFTSLSLVLYSR.K	0.66	0.64
binding protein	(VTDB_HUMAN)
vitamin D-	P02774	K.SYLSMVGSCCTSASPTVCFLK.E	0.68	0.77
binding protein	(VTDB_HUMAN)
vitronectin	P04004	R.IYISGMAPRPSLAK.K	0.63	0.66
	(VTNC_HUMAN)
vitronectin	P04004	R.IYISGMAPRPSLAK.K	0.64	0.66
	(VTNC_HUMAN)
vitronectin	P04004	K.LIRDVWGIEGPIDAAFTR.I	0.81	0.75
	(VTNC_HUMAN)
von Willebrand	P04275	R.IGWPNAPILIQDFETLPR.E	0.67	0.67
factor	(VWF_HUMAN)
preproprotein
*= Oxidation of Methionine

TABLE 9
Preeclampsia: Additional peptides significant with AUC > 0.6 by Sequest
only
Protein description	Uniprot ID (name)	Peptide	S_AUC
afamin	P43652	R.LCFFYNKK.S	0.67
	(AFAM_HUMAN)
afamin	P43652	R.RP CFE SLK.A	0.81
	(AFAM_HUMAN)
afamin	P43652	R.IVQIYK.D	0.61
	(AFAM_HUMAN)
afamin	P43652	R.FLVNLVK.L	0.60
	(AFAM_HUMAN)
afamin	P43652	K.LPNNVLQEK.I	0.67
	(AFAM_HUMAN)
alpha-1-	P01011	R.LYGSEAFATDFQDSAAAKK.	0.61
antichymotrypsin	(AACT_HUMAN)	L
alpha-1-	P01011	K.EQLSLLDRFTEDAKR.L	0.71
antichymotrypsin	(AACT_HUMAN)
alpha-1-	P01011	R.EIGELYLPK.F	0.68
antichymotrypsin	(AACT_HUMAN)
alpha-1-	P01011	R.WRDSLEFR.E	0.71
antichymotrypsin	(AACT_HUMAN)
alpha-1-	P01011	K.RLYGSEAFATDFQDSAAAK.	0.89
antichymotrypsin	(AACT_HUMAN)	K
alpha-1B-	P04217	R.FALVR.E	1.00
glycoprotein	(A1BG_HUMAN)
alpha-1B-	P04217	R.GVTFLLRR.E	0.67
glycoprotein	(A1BG_HUMAN)
alpha-1B-	P04217	R.RGEKELLVPR.S	0.71
glycoprotein	(A1BG_HUMAN)
alpha-1B-	P04217	K.ELLVPR.S	0.61
glycoprotein	(A1BG_HUMAN)
alpha-1B-	P04217	K.NGVAQEPVHLDSPAIK.H	0.64
glycoprotein	(A1BG_HUMAN)
alpha-2-antiplasmin	P08697	R.NKFDPSLTQR.D	0.60
	(A2AP_HUMAN)
alpha-2-antiplasmin	P08697	R.QLTSGPNQEQVSPLTLLK.	0.67
	(A2AP_HUMAN)	L
alpha-2-antiplasmin	P08697	K.HQM*DLVATLSQLGLQELFQAPDLR.G	0.67
	(A2AP_HUMAN)
angiotensinogen	P01019	R.FM*QAVTGWK.T	0.60
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	K.PKDPTFIPAPIQAK.T	0.83
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	R.SLDFTELDVAAEK.I	0.60
preproprotein	(ANGT_HUMAN)
ankyrin repeat and	Q8NFD2	R.KNLVPR.D	1.00
protein kinase	(ANKK1_HUMAN)
domain-containing
protein 1
antithrombin-III	P01008	R.RVWELSK.A	0.68
	(ANT3_HUMAN)
apolipoprotein A-IV	P06727	K.VKIDQTVEELRR.S	0.62
	(APOA4_HUMAN)
apolipoprotein A-IV	P06727	K.DLRDKVNSFFSTFK.E	0.92
	(APOA4_HUMAN)
apolipoprotein A-IV	P06727	K.LVPFATELHER.L	0.71
	(APOA4_HUMAN)
apolipoprotein A-IV	P06727	R.RVEPYGENFNK.A	0.86
	(APOA4_HUMAN)
apolipoprotein A-IV	P06727	K.VNSFFSTFK.E	0.87
	(APOA4_HUMAN)
apolipoprotein B-	P04114	K.AVSM*PSFSILGSDVR.V	0.70
100	(APOB_HUMAN)
apolipoprotein B-	P04114	K.AVSNIPSFSILGSDVR.V	0.66
100	(APOB_HUMAN)
apolipoprotein B-	P04114	K.AVSNIPSFSILGSDVR.V	0.66
100	(APOB_HUMAN)
apolipoprotein B-	P04114	K.AVSM*PSFSILGSDVR.V	0.70
100	(APOB_HUMAN)
apolipoprotein B-	P04114	K.VNWEEEAASGLLTSLKDNVPK.A	0.60
100	(APOB_HUMAN)
apolipoprotein B-	P04114	R.DLKVEDIPLAR.I	0.70
100	(APOB_HUMAN)
apolipoprotein C-I	P02654	K.MREWFSETFQK.V	0.73
	(APOC1_HUMAN)
apolipoprotein C-II	P02655	K.STAAMSTYTGIFTDQVLSVLKGEE.-	0.68
	(APOC2_HUMAN)
apolipoprotein E	P02649	R.AKLEEQAQQIR.L	0.67
	(APOE_HUMAN)
apolipoprotein E	P02649	R.FWDYLR.W	0.67
	(APOE_HUMAN)
apolipoprotein E	P02649	R.LKSWFEPLVEDMQR.Q	0.65
	(APOE_HUMAN)
beta-2-glycoprotein	P02749	K.VSFFCK.N	0.67
1	(APOH_HUMAN)
beta-2-glycoprotein	P02749	R.VCPFAGILENGAVR.Y	0.63
1	(APOH_HUMAN)
beta-2-	P61769	K.SNFLNCYVSGFHPSDIEVDLLK.N	0.60
microglobulin	(B2MG_HUMAN)
biotinidase	P43251	R.LSSGLVTAALYGR.L	1.00
	(BTD_HUMAN)
carboxypeptidase	Q96IY4	K.IAWHVIR.N	0.90
B2 preproprotein	(CBPB2_HUMAN)
carboxypeptidase N	P22792	K.LSNNALSGLPQGVFGK.L	0.62
subunit 2	(CPN2_HUMAN)
carboxypeptidase N	P15169	R.DHLGFQVTWPDESK.A	0.93
subunit 2	(CBPN_HUMAN)
ceruloplasmin	P00450	K.VYVHLK.N	0.67
	(CERU_HUMAN)
ceruloplasmin	P00450	K.LISVDTEHSNIYLQNGPDR.I	0.62
	(CERU_HUMAN)
ceruloplasmin	P00450	K.M*YYSAVDPTKDIFTGLIGPM*K.I	0.76
	(CERU_HUMAN)
ceruloplasmin	P00450	K.M*YYSAVDPTKDIFTGLIGPMK.I	0.68
	(CERU_HUMAN)
ceruloplasmin	P00450	R.QKDVDKEFYLFPTVFDENESLLLEDNIR.M	0.66
	(CERU_HUMAN)
ceruloplasmin	P00450	K.DVDKEFYLFPTVFDENESLLLEDNIR.M	0.60
	(CERU_HUMAN)
ceruloplasmin	P00450	K.DIFTGLIGPMK.I	0.62
	(CERU_HUMAN)
ceruloplasmin	P00450	R.SVPPSASHVAPTETFTYEWTVPK.E	0.66
	(CERU_HUMAN)
ceruloplasmin	P00450	R.GVYSSDVFDIFPGTYQTLEM*FPR.T	0.67
	(CERU_HUMAN)
ceruloplasmin	P00450	K.DIFTGLIGPMK.I	0.62
	(CERU_HUMAN)
ceruloplasmin	P00450	K.VNKDDEEFIESNK.M	0.78
	(CERU_HUMAN)
clusterin	P10909	R.KYNELLK.S	0.75
preproprotein	(CLUS_HUMAN)
coagulation factor	P00748	R.TTLSGAPCQPWASEATYR.N	0.64
XII	(FA12_HUMAN)
complement C1q	P02745	K.GHIYQGSEADSVFSGFLIFPSA.-	0.64
subcomponent	(C1QA_HUMAN)
subunit A
complement C1q	P02747	K.FQSVFTVTR.Q	0.65
subcomponent	(C1QC_HUMAN)
subunit C
complement C1r	P00736	R.WILTAAHTLYPK.E	0.68
subcomponent	(C1R_HUMAN)
complement C1r	P00736	K.VLNYVDWIKK.E	0.81
subcomponent	(C1R_HUMAN)
complement C1s	P09871	R.LPVAPLRK.C	0.63
subcomponent	(CIS_HUMAN)
complement C2	P06681	R.PICLPCTMEANLALR.R	0.78
	(CO2_HUMAN)
complement C2	P06681	R.QHLGDVLNFLPL.-	0.70
	(CO2_HUMAN)
complement C4-B-	P0C0L5	K.LGQYASPTAKR.C	0.89
like preproprotein	(CO4B_HUMAN)
complement C4-B-	P0C0L5	K.M*RPSTDTITVMVENSHGLR.V	0.65
like preproprotein	(CO4B_HUMAN)
complement C4-B-	P0C0L5	K.MRPSTDTITVMVENSHGLR.V	0.72
like preproprotein	(CO4B_HUMAN)
complement C5	P01031	K.EFPYRIPLDLVPK.T	0.67
preproprotein	(CO5_HUMAN)
complement C5	P01031	R.VFQFLEK.S	0.60
preproprotein	(CO5_HUMAN)
complement C5	P01031	R.MVETTAYALLTSLNLK.D	0.61
preproprotein	(CO5_HUMAN)
complement C5	P01031	R.ENSLYLTAFTVIGIR.K	0.81
preproprotein	(CO5_HUMAN)
complement	P07357	K.YNPVVIDFEMQPIHEVLR.H	0.62
component C8	(CO8A_HUMAN)
alpha chain
complement	P07358	K.IPGIFELGISSQSDR.G	0.61
component C8 beta	(CO8B_HUMAN)
chain preproprotein
complement	P07360	R.RPASPISTIQPK.A	0.71
component C8	(CO8G_HUMAN)
gamma chain
complement	P07360	R.FLQEQGHR.A	0.87
component C8	(CO8G_HUMAN)
gamma chain
complement factor	P00751	K.VSVGGEKR.D	0.60
B preproprotein	(CFAB_HUMAN)
complement factor	P00751	K.CLVNLIEK.V	0.69
B preproprotein	(CFAB_HUMAN)
complement factor	P00751	K.KDNEQHVFK.V	0.68
B preproprotein	(CFAB_HUMAN)
complement factor	P00751	K.ISVIRPSK.G	0.63
B preproprotein	(CFAB_HUMAN)
complement factor	P00751	K.KCLVNLIEK.V	0.63
B preproprotein	(CFAB_HUMAN)
complement factor	P00751	R.LPPTTTCQQQKEELLPAQDIK.A	0.64
B preproprotein	(CFAB_HUMAN)
complement factor	P00751	K.LQDEDLGFL.-	0.66
B preproprotein	(CFAB_HUMAN)
complement factor	P08603	K.SCDIPVFMNAR.T	0.60
H	(CFAH_HUMAN)
complement factor	P08603	K.HGGLYHENMR.R	0.75
H	(CFAH_HUMAN)
complement factor	P08603	K.IIYKENER.F	0.69
H	(CFAH_HUMAN)
complement factor I	P05156	K.RAQLGDLPWQVAIK.D	0.68
preproprotein	(CFAI_HUMAN)
conserved	Q9Y2V7	K.ISNLLK.F	0.71
oligomeric Golgi	(COG6_HUMAN)
complex subunit 6
isoform
cornulin	Q9UBG3	R.RYARTEGNCTALTR.G	0.81
	(CRNN_HUMAN)
FERM domain-	Q9BZ67	R.VQLGPYQPGRPAACDLR.E	0.63
containing protein 8	(FRMD8_HUMAN)
gelsolin	P06396	R.VPEARPNSMVVEHPEFLK.A	0.61
	(GELS_HUMAN)
gelsolin	P06396	K.AGKEPGLQIWR.V	0.70
	(GELS_HUMAN)
glucose-induced	Q9NWU2	K.VWSEVNQAVLDYENRESTPK.L	0.83
degradation protein	(GID8_HUMAN)
8 homolog
hemK	Q9Y5R4	R.M*LWALLSGPGRRGSTR.G	0.61
methyltransferase	(HEMK1 HUMAN)
family member 1
hemopexin	P02790	R.ELISER.W	0.82
	(HEMO_HUMAN)
hemopexin	P02790	R.DVRDYFM*PCPGR.G	0.70
	(HEMO_HUMAN)
hemopexin	P02790	K.GDKVWVYPPEKK.E	0.71
	(HEMO_HUMAN)
hemopexin	P02790	R.DVRDYFMPCPGR.G	0.60
	(HEMO_HUMAN)
hemopexin	P02790	R.EWFWDLATGTMK.E	0.65
	(HEMO_HUMAN)
hemopexin	P02790	R.YYCFQGNQFLR.F	0.68
	(HEMO_HUMAN)
hemopexin	P02790	R.RLWWLDLK.S	0.65
	(HEMO_HUMAN)
heparin cofactor 2	P05546	R.LNILNAK.F	0.75
	(HEP2 HUMAN)
heparin cofactor 2	P05546	R.NFGYTLR.S	0.66
	(HEP2 HUMAN)
histone deacetylase	Q8TEE9	K.LLPPPPIM*SARVLPR.P	0.63
complex subunit	(SAP25_HUMAN)
SAP25
hyaluronan-binding	Q14520	K.RPGVYTQVTK.F	0.68
protein 2	(HABP2_HUMAN)
hyaluronan-binding	Q14520	K.FLNWIK.A	0.62
protein 2	(HABP2_HUMAN)
immediate early	Q5T953	-.MECALDAQSLISISLRKIHSSR.T	0.93
response gene 5-like	(IER5L_HUMAN)
protein
inactive caspase-12	Q6UXS9	K.AGADTHGRLLQGNICNDAVTK.A	0.60
	(CASPC_HUMAN)
insulin-like growth	P35858	K.ANVFVQLPR.L	0.62
factor-binding	(ALS_HUMAN)
protein complex
acid labile subunit
inter-alpha-trypsin	P19827	K.ELAAQTIKK.S	0.71
inhibitor heavy	(ITIH1_HUMAN)
chain H1
inter-alpha-trypsin	P19827	K.ILGDM*QPGDYFDLVLFGTR.V	0.79
inhibitor heavy	(ITIH1_HUMAN)
chain H1
inter-alpha-trypsin	P19827	K.VTFQLTYEEVLKR.N	0.70
inhibitor heavy	(ITIH1_HUMAN)
chain H1
inter-alpha-trypsin	P19827	R.TMEQFTIHLTVNPQSK.V	0.61
inhibitor heavy	(ITIH1_HUMAN)
chain H1
inter-alpha-trypsin	P19827	R.FAHYVVTSQVVNTANEAR.E	0.63
inhibitor heavy	(ITIH1_HUMAN)
chain H1
inter-alpha-trypsin	P19823	R.SSALDMENFRTEVNVLPGAK.V	0.89
inhibitor heavy	(ITIH2_HUMAN)
chain H2
inter-alpha-trypsin	P19823	K.MKQTVEAMK.T	0.93
inhibitor heavy	(ITIH2_HUMAN)
chain H2
inter-alpha-trypsin	P19823	R.IYLQPGR.L	0.66
inhibitor heavy	(ITIH2_HUMAN)
chain H2
inter-alpha-trypsin	P19823	K.HLEVDVWVIEPQGLR.F	0.61
inhibitor heavy	(ITIH2_HUMAN)
chain H2
inter-alpha-trypsin	P19823	K.FYNQVSTPLLR.N	0.89
inhibitor heavy	(ITIH2_HUMAN)
chain H2
inter-alpha-trypsin	P19823	R.KLGSYEHR.I	0.69
inhibitor heavy	(ITIH2_HUMAN)
chain H2
inter-alpha-trypsin	Q14624	K.GSEMVVAGK.L	1.00
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	R.MNFRPGVLSSR.Q	0.72
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	K.YIFHNFM*ER.L	0.73
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	K.ETLFSVMPGLK.M	0.60
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	R.FKPTLSQQQK.S	0.64
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	K.WKETLFSVMPGLK.M	0.69
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	R.RLGVYELLLK.V	0.65
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	R.DTDRFSSHVGGTLGQFYQEVLWGSPAASDDGRR.T	0.69
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	K.VRPQQLVK.H	0.62
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	R.NVHSAGAAGSR.M	0.69
inhibitor heavy	(ITIH4_HUMAN)
chain H4
kallistatin	P29622	R.LGFTDLFSK.W	0.63
	(KAIN_HUMAN)
kallistatin	P29622	R.VGSALFLSHNLK.F	0.62
	(KAIN_HUMAN)
kininogen-1	P01042	R.VQVVAGKK.Y	0.68
	(KNG1_HUMAN)
leucine-rich alpha-	P02750	R.LHLEGNKLQVLGK.D	0.75
2-glycoprotein	(A2GL_HUMAN)
lumican	P51884	R.FNALQYLR.L	0.77
	(LUM_HUMAN)
m7GpppX	Q96C86	R.IVFENPDPSDGFVLIPDLK.W	0.94
diphosphatase	(DCPS_HUMAN)
MAGUK p55	Q8N3R9	K.ILEIEDLFSSLK.H	0.69
subfamily member	(NIPP5_HUMAN)
5
MBT domain-	Q05BQ5	K.WFDYLR.E	0.63
containing protein 1	(MBTD1_HUMAN)
obscurin	Q5VST9	R.CELQIRGLAVEDTGEYLCVCGQERTSATLTVR.A	0.73
	(OBSCN_HUMAN)
olfactory receptor	Q8NH94	K.DMKQGLAKLM*HR.M	0.89
1L1	(OR1L1_HUMAN)
phosphatidylinositol-	P80108	K.GIVAAFYSGPSLSDKEK.L	0.79
glycan-specific	(PHLD_HUMAN)
phospholipase D
phosphatidylinositol-	P80108	R.TLLLVGSPTWK.N	0.65
glycan-specific	(PHLD_HUMAN)
phospholipase D
phosphatidylinositol-	P80108	R.WYVPVKDLLGIYEK.L	0.92
glycan-specific	(PHLD_HUMAN)
phospholipase D
pigment epithelium-	P36955	R.SSTSPTTNVLLSPLSVATALSALSLGAEQR.T	0.63
derived factor	(PEDF_HUMAN)
plasma protease C1	P05155	K.GVTSVSQIFHSPDLAIR.D	0.60
inhibitor	(IC1_HUMAN)
PREDICTED:	P0C0L4	R.DKGQAGLQR.A	0.67
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	K.SHKPLNIVIGK.V	0.87
complement C4-A	(C04A_HUMAN)
PREDICTED:	P0C0L4	R.KKEVYM*PSSIFQDDFVIPDISEPGTWK.I	0.67
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	R.FGLLDEDGKK.T	0.64
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	R.KKEVYMPSSIFQDDFVIPDISEPGTWK.I	0.69
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	K.GLCVATPVQLR.V	0.78
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	R.YRVFALDQK.M	0.63
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	K.AEFQDALEKLNMGITDLQGLR.L	0.60
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	R.ECVGFEAVQEVPVGLVQPASATLYDYYNPERR.C	0.60
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	K.AEFQDALEKLNMGITDLQGLR.L	0.60
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	R.VTASDPLDTLGSEGALSPGGVASLLR.L	0.61
complement C4-A	(CO4A_HUMAN)
pregnancy zone	P20742	R.NELIPLIYLENPRR.N	0.60
protein	(PZP_HUMAN)
pregnancy zone	P20742	K.AVGYLITGYQR.Q	0.67
protein	(PZP_HUMAN)
protein AMBP	P02760	R.AFIQLWAFDAVK.G	0.70
preproprotein	(AMBP_HUMAN)
protein CBFA2T2	043439	R.LTEREWADEWKHLDHAL	0.61
(MTG8R_HUMAN)	NCIMEMVEK.T
protein NLRC3	Q7RTR2	K.ALM*DLLAGKGSQGSQA	0.83
(NLRC3_HUMAN)	PQALDR.T
prothrombin	P00734	R.TFGSGEADCGLRPLFEK.K	0.69
preproprotein	(THRB_HUMAN)
ras-related GTP-	Q7L523	K.ISNIIK.Q	0.68
binding protein A	(RRAGA_HUMAN)
retinol-binding	P02753	R.FSGTWYAMAK.K	0.64
protein 4	(RET4_HUMAN)
retinol-binding	P02753	R.LLNNWDVCADMVGTFTDTEDPAKFK.M	0.61
protein 4	(RET4_HUMAN)
retinol-binding	P02753	K.YWGVASFLQK.G	0.63
protein 4	(RET4_HUMAN)
serum amyloid P-	P02743	R.GYVIIKPLVWV.-	0.60
component	(SAMP_HUMAN)
sex hormone-	P04278	R.LPLVPALDGCLR.R	0.63
binding globulin	(SHBG_HUMAN)
spectrin beta chain,	Q13813	R.NELIRQEKLEQLAR.R	0.88
non-erythrocytic 1	(SPTN1_HUMAN)
TATA element	P82094	K.EELATRLNSSETADLLK.E	0.71
modulatory factor	(TMF1_HUMAN)
testicular haploid	P0DJG4	R.QCLLNRPFSDNSAR.D	0.67
expressed gene	(THEGL_HUMAN)
protein-like
thyroxine-binding	P05543	K.NALALFVLPK.E	0.61
globulin	(THBG_HUMAN)
thyroxine-binding	P05543	R.SFMLLILER.S	0.64
globulin	(THBG_HUMAN)
titin	Q8WZ42	K.TEPKAPEPISSK.P	0.89
	(TITIN_HUMAN)
transthyretin	P02766	R.GSPAINVAVHVFR.K	0.61
	(TTHY_HUMAN)
tripartite motif-	Q9C035	R.ELISDLEHRLQGSVM*ELLQGVDGVIK.R	0.92
containing protein 5	(TRIM5_HUMAN)
vitamin D-binding	P02774	K.TAMDVFVCTYFMPAAQLPELPDVELPTNKDVCDPGNTK.V	0.88
protein	(VTDB_HUMAN)
vitamin D-binding	P02774	K.VM*DKYTFELSR.R	0.70
protein	(VTDB_HUMAN)
vitamin D-binding	P02774	K.LAQKVPTADLEDVLPLAEDITNILSK.C	0.61
protein	(VTDB_HUMAN)
vitamin D-binding	P02774	K.SCESNSPFPVHPGTAECCTK.E	0.68
protein	(VTDB_HUMAN)
vitamin D-binding	P02774	R.KLCMAALK.H	0.71
protein	(VTDB_HUMAN)
vitamin D-binding	P02774	K.LCDNLSTK.N	0.60
protein	(VTDB_HUMAN)
vitamin D-binding	P02774	K.VM*DKYTFELSR.R	0.70
protein	(VTDB HUMAN)
vitronectin	P04004	R.IYISGM*APR.P	0.75
	(VTNC_HUMAN)
vitronectin	P04004	R.ERVYFFK.G	0.67
	(VTNC_HUMAN)
vitronectin	P04004	R.IYISGMAPR.P	0.81
	(VTNC_HUMAN)
vitronectin	P04004	K.AVRPGYPK.L	0.63
	(VTNC_HUMAN)
zinc finger protein	P52746	K.TRFLLR.T	0.67
142	(ZN142_HUMAN)
*= Oxidation of methionine

TABLE 10
Preeclampsia: Additional peptides significant with AUC > 0.6 by X!Tandem only
Protein description	Uniprot ID (name)	Peptide	XT_AUC
afamin	P43652	K.TYVPPPFSQDLFTFHADMCQSQNEELQR.K	0.76
	(AFAM_HUMAN)
afamin	P43652	K.KSDVGFLPPFPTLDPEEK.C	0.62
	(AFAM_HUMAN)
alpha-1-	P01011	R.GTHVDLGLASANVDFAFSLYK.Q	0.69
antichymotrypsin	(AACT_HUMAN)
alpha-1B-	P04217	K.SLPAPWLSM*APVSWITPGLK.T	0.67
glycoprotein	(A1BG_HUMAN)
alpha-1B-	P04217	K.SLPAPWLSM*APVSWITPGLK.T	0.67
glycoprotein	(A1BG_HUMAN)
alpha-1B-	P04217	R.C{circumflex over ( )}LAPLEGAR.F	0.62
glycoprotein	(A1BG_HUMAN)
alpha-2-antiplasmin	P08697	R.WFLLEQPEIQVAHFPFK.N	0.60
	(A2AP_HUMAN)
alpha-2-antiplasmin	P08697	R.LCQDLGPGAFR.L	0.92
	(A2AP_HUMAN)
alpha-2-antiplasmin	P08697	K.HQMDLVATLSQLGLQELFQAPDLR.G	0.67
	(A2AP_HUMAN)
alpha-2-HS-	P02765	R.QLKEHAVEGDCDFQLLK.L	0.63
glycoprotein	(FETUA_HUMAN)
preproprotein
alpha-2-HS-	P02765	R.Q{circumflex over ( )}LKEHAVEGDCDFQLLK.L	0.65
glycoprotein	(FETUA_HUMAN)
preproprotein
alpha-2-HS-	P02765	K.C{circumflex over ( )}NLLAEK.Q	0.61
glycoprotein	(FETUA_HUMAN)
preproprotein
angiotensinogen	P01019	R.SLDFTELDVAAEKIDR F	0.62
preproprotein	(ANGT_HUMAN)
angiotensinogen	P01019	K.DPTFIPAPIQAK.T	0.78
preproprotein	(ANGT_HUMAN)
apolipoprotein A-II	P02652	K.EPCVESLVSQYFQTVTDYGKDLMEK.V	0.67
preproprotein	(APOA2_HUMAN)
apolipoprotein B-	P04114	K.FSVPAGIVIPSFQALTAR.F	0.66
100	(APOB_HUMAN)
apolipoprotein B-	P04114	K.EQHLFLPFSYK.N	0.90
100	(APOB_HUMAN)
apolipoprotein B-	P04114	R.GIISALLVPPETEEAK.Q	0.70
100	(APOB_HUMAN)
beta-2-glycoprotein	P02749	K.C{circumflex over ( )}FKEHSSLAFWK.T	0.70
1	(APOH_HUMAN)
beta-2-glycoprotein	P02749	K.EHSSLAFWK.T	0.62
1	(APOH_HUMAN)
ceruloplasmin	P00450	R.FNKNNEGTYYSPNYNPQSR.S	0.64
	(CERU_HUMAN)
ceruloplasmin	P00450	K.HYYIGIIETTWDYASDHGEK.K	0.63
	(CERU_HUMAN)
ceruloplasmin	P00450	K.M*YYSAVDPTKDIFTGLIGPM*K.I	0.66
	(CERU_HUMAN)
ceruloplasmin	P00450	K.M*YYSAVDPTKDIFTGLIGPM*K.I	0.66
	(CERU_HUMAN)
ceruloplasmin	P00450	K.M*YYSAVDPTKDIFTGLIGPMK.I	0.67
	(CERU_HUMAN)
ceruloplasmin	P00450	K.M*YYSAVDPTKDIFTGLIGPMK.I	0.67
	(CERU_HUMAN)
ceruloplasmin	P00450	K.MYYSAVDPTKDIFTGLIGPM*K.I	0.67
	(CERU_HUMAN)
ceruloplasmin	P00450	K.MYYSAVDPTKDIFTGLIGPM*K.I	0.67
	(CERU_HUMAN)
ceruloplasmin	P00450	R.GVYSSDVFDIFPGTYQTLEM*FPR.T	0.67
	(CERU_HUMAN)
coagulation factor	P00748	R.VVGGLVALR.G	0.64
XII	(FA12_HUMAN)
complement C1q	P02745	K.KGHIYQGSEADSVFSGFLIFPSA.—	0.81
subcomponent	(C1QA_HUMAN)
subunit A
complement C1q	P02747	R.Q{circumflex over ( )}THQPPAPNSLIR.F	0.64
subcomponent	(C1QC_HUMAN)
subunit C
complement C1s	P09871	R.Q{circumflex over ( )}FGPYCGHGFPGPLNIETK.S	0.71
subcomponent	(C1S_HUMAN)
complement C2	P06681	R.QPYSYDFPEDVAPALGTSFSHMLGATNPTQK.T	0.63
	(CO2_HUMAN)
complement C2	P06681	R.LLGMETMAWQEIR.H	0.70
	(CO2_HUMAN)
complement C4-B-	P0C0L5	R.AVGSGATFSHYYYM*ILSR.G	0.67
like preproprotein	(CO4B_HUMAN)
complement C4-B-	P0C0L5	R.FGLLDEDGKKTFFR.G	0.61
like preproprotein	(CO4B_HUMAN)
complement C4-B-	P0C0L5	K.ITQVLHFTK.D	0.67
like preproprotein	(CO4B_HUMAN)
complement C4-B-	P0C0L5	K.M*RPSTDTITVM*VENSHGLR.V	0.65
like preproprotein	(CO4B_HUMAN)
complement C4-B-	P0C0L5	K.M*RPSTDTITVM*VENSHGLR.V	0.75
like preproprotein	(CO4B_HUMAN)
complement C5	P01031	R.IVACASYKPSR.E	0.67
preproprotein	(CO5_HUMAN)
complement C5	P01031	R.SYFPESWLWEVHLVPR.R	0.60
preproprotein	(CO5_HUMAN)
complement C5	P01031	K.Q{circumflex over ( )}LPGGQNPVSYVYLEVVSK.H	0.74
preproprotein	(CO5_HUMAN)
complement C5	P01031	K.TLLPVSKPEIR.S	0.78
preproprotein	(CO5_HUMAN)
complement	P07358	R.GGASEHITTLAYQELPTADLMQEWGDAVQYNPAIIK.V	0.60
component C8 beta	(CO8B_HUMAN)
chain preproprotein
complement factor	P00751	K.GTDYHKQPWQAK.I	0.89
B preproprotein	(CFAB_HUMAN)
complement factor	P00751	K.VKDISEVVTPR.F	0.64
B preproprotein	(CFAB_HUMAN)
complement factor	P00751	K.QVPAHAR.D	0.63
B preproprotein	(CFAB_HUMAN)
complement factor	P00751	R.GDSGGPLIVHKR.S	0.79
B preproprotein	(CFAB_HUMAN)
complement factor	P00751	R.FLCTGGVSPYADPNTCR.G	0.71
B preproprotein	(CFAB_HUMAN)
complement factor	P00751	K.KEAGIPEFYDYDVALIK.L	0.74
B preproprotein	(CFAB_HUMAN)
complement factor	P00751	R.YGLVTYATYPK.I	0.88
B preproprotein	(CFAB_HUMAN)
complement factor	P08603	K.EFDHNSNIR.Y	1.00
H	(CFAH_HUMAN)
complement factor	P08603	K.WSSPPQCEGLPCK.S	0.71
H	(CFAH_HUMAN)
complement factor	P08603	R.KGEWVALNPLR.K	0.67
H	(CFAH_HUMAN)
complement factor I	P05156	K.SLECLHPGTK.F	0.60
preproprotein	(CFAI_HUMAN)
corticosteroid-	P08185	R.GLASANVDFAFSLYK.H	0.62
binding globulin	(CBG_HUMAN)
fetuin-B	Q9UGM5	K.LVVLPFPK.E	0.74
	(FETUB_HUMAN)
fetuin-B	Q9UGM5	R.ASSQWVVGPSYFVEYLIK.E	0.61
	(FETUB_HUMAN)
ficolin-3	O75636	R.LLGEVDHYQLALGK.F	0.61
	(FCN3_HUMAN)
gelsolin	P06396	K.QTQVSVLPEGGETPLFK.Q	0.69
	(GELS_HUMAN)
hemopexin	P02790	K.VDGALCMEK.S	0.60
	(HEMO_HUMAN)
hemopexin	P02790	K.SGAQATWTELPWPHEKVDGALCM*EK.S	0.66
	(HEMO_HUMAN)
hemopexin	P02790	K.SGAQATWTELPWPHEKVDGALCM*EK.S	0.66
	(HEMO_HUMAN)
hemopexin	P02790	R.EWFWDLATGTMK.E	0.68
	(HEMO_HUMAN)
hemopexin	P02790	R.Q{circumflex over ( )}GHNSVFLIK.G	0.67
	(HEMO_HUMAN)
heparin cofactor 2	P05546	K.TLEAQLTPR.V	0.67
	(HEP2_HUMAN)
histidine-rich	P04196	K.DSPVLIDFFEDTER.Y	0.60
glycoprotein	(HRG_HUMAN)
insulin-like growth	P35858	K.ALRDFALQNPSAVPR.F	0.89
factor-binding	(ALS_HUMAN)
protein complex
acid labile subunit
insulin-like growth	P35858	R.LWLEGNPWDCGCPLK.A	0.60
factor-binding	(ALS_HUMAN)
protein complex
acid labile subunit
inter-alpha-trypsin	P19827	K.ILGDM*QPGDYFDLVLFGTR.V	0.85
inhibitor heavy	(ITIH1_HUMAN)
chain H1
inter-alpha-trypsin	P19823	R.SSALDMENFR.T	0.63
inhibitor heavy	(ITIH2_HUMAN)
chain H2
inter-alpha-trypsin	P19823	R.SLAPTAAAK.R	0.83
inhibitor heavy	(ITIH2_HUMAN)
chain H2
inter-alpha-trypsin	P19823	R.LSNENHGIAQR.I	0.76
inhibitor heavy	(ITIH2_HUMAN)
chain H2
inter-alpha-trypsin	P19823	R.IYGNQDTSSQLKK.F	0.63
inhibitor heavy	(ITIH2_HUMAN)
chain H2
inter-alpha-trypsin	Q14624	K.TGLLLLSDPDKVTIGLLFWDGR.G	0.60
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	K.YIFHNFM*ER.L	0.70
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	K.IPKPEASFSPR.R	0.65
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	R.QGPVNLLSDPEQGVEVTGQYER.E	0.64
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	R.ANTVQEATFQMELPK.K	0.61
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	K.WKETLFSVMPGLK.M	0.66
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	R.RLDYQEGPPGVEISCWSVEL.—	0.69
inhibitor heavy	(ITIH4_HUMAN)
chain H4
inter-alpha-trypsin	Q14624	K.SPEQQETVLDGNLIIR.Y	0.66
inhibitor heavy	(ITIH4_HUMAN)
chain H4
kallistatin	P29622	K.ALWEKPFISSR.T	0.65
	(KAIN_HUMAN)
kininogen-1	P01042	R.Q{circumflex over ( )}VVAGLNFR.I	0.67
	(KNG1_HUMAN)
kininogen-1	P01042	R.QVVAGLNFR.I	0.71
	(KNG1_HUMAN)
kininogen-1	P01042	K.LGQSLDCNAEVYVVPWEK.K	0.62
	(KNG1_HUMAN)
kininogen-1	P01042	R.IASFSQNCDIYPGKDFVQPPTK.I	0.64
	(KNG1_HUMAN)
leucine-rich alpha-	P02750	R.C{circumflex over ( )}AGPEAVKGQTLLAVAK.S	0.70
2-glycoprotein	(A2GL_HUMAN)
leucine-rich alpha-	P02750	K.GQTLLAVAK.S	0.67
2-glycoprotein	(A2GL_HUMAN)
leucine-rich alpha-	P02750	K.DLLLPQPDLR.Y	0.71
2-glycoprotein	(A2GL_HUMAN)
lumican	P51884	K.ILGPLSYSK.I	0.83
	(LUM_HUMAN)
PREDICTED:	P0C0L4	R.QGSFQGGFR.S	0.83
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	K.YVLPNFEVK.I	0.69
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	R.LLATLCSAEVCQCAEGK.C	0.60
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	R.VGDTLNLNLR.A	0.66
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	R.EPFLSCCQFAESLR.K	0.62
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	R.EELVYELNPLDHR.G	0.60
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	R.GSFEFPVGDAVSK.V	0.62
complement C4-A	(CO4A_HUMAN)
PREDICTED:	P0C0L4	R.GCGEQTMIYLAPTLAASR.Y	0.71
complement C4-A	(CO4A_HUMAN)
pregnancy zone	P20742	K.GSFALSFPVESDVAPIAR.M	0.63
protein	(PZP_HUMAN)
protein AMBP	P02760	R.VVAQGVGIPEDSIFTMADRGECVPGEQEPEPILIPR.V	0.62
preproprotein	(AMBP_HUMAN)
prothrombin	P00734	R.SGIECQLWR.S	0.65
preproprotein	(THRB_HUMAN)
thyroxine-binding	P05543	K.MSSINADFAFNLYR.R	0.63
globulin	(THBG_HUMAN)
vitronectin	P04004	R.MDWLVPATCEPIQSVFFFSGDKYYR.V	1.00
	(VTNC_HUMAN)
vitronectin	P04004	R.IYISGM*APRPSLAK.K	0.64
	(VTNC_HUMAN)
vitronectin	P04004	R.IYISGMAPRPSLAK.K	0.63
	(VTNC_HUMAN)
vitronectin	P04004	R.DVWGIEGPIDAAFTR.I	0.61
	(VTNC_HUMAN)
zinc finger CCHC	Q8N567	R.SCPDNPK.G	0.68
domain-containing	(ZCHC9_HUMAN)
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
		m/z,	fragment ion, m/z, charge,
Protein	Peptide	charge	rank	area
inter-alpha-trypsin	K.AAISGENAGLVR.A	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.GSLVQASEANLQAAQDFVR.G	668.6763+++	A [y7] - 806.4155 + [1]	304374
inhibitor heavy chain H1			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.TAFISDFAVTADGNAFIGDIK.D	1087.0442++	G [y4] - 432.2453 + [1]	22362
inhibitor heavy chain H1			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	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	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	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	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.HLEVDVWVIEPQGLR.F	597.3247+++	P [y5] - 570.3358 + [1]	303693
inhibitor heavy chain H2			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	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	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	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	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	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.DLYHYITSYVVDGEIIIYGPAYSGR.E	955.4762+++	G [y7] - 707.3471 + [1]	66891
1-glycoprotein 1			V [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] - 763.8617 ++ [10]	10407
pregnancy-specific beta-	TLFIFGVTK	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-	NYTYIWWLNGQSLPVSPR	1097.5576++	W [b6] - 841.3879 + [1]	25756
1-glycoprotein 4			G [y9] - 940.5211 + [2]	25018
PSG4_HUMAN			Y [b4] - 542.2245 + [3]	19778
PSG8_HUMAN	LQLSETNR	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	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	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	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	621.2984++	E [b2] - 231.0975 + [1]	37113
globulin			D [y2] - 262.1397 + [2]	14495
THBG_HUMAN
thyroxine-binding	K.AVLHIGEK.G	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	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	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	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	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	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	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	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	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	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	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.TLDLGENQLETLPPDLLR.G	1019.0468++	P [y6] - 710.4196 + [1]	232459
glycoprotein			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	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	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	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	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	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	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	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	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.NALTGLPPGLFQASATLDTLVLK.E	780.7773+++	T [y8] - 902.5557 + [1]	44285
glycoprotein			P [y17] - 886.0036 ++ [2]	39557
A2GL_HUMAN			D [y6] - 688.4240 + [3]	19464
alpha-1B-glycoprotein	K.NGVAQEPVHLDSPAIK.H	837.9441++	P [y10] - 1076.6099 + [1]	130137
A1BG_HUMAN			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	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] - 1235.6379 + [11]	117366
			Q [y11] - 618.3226 ++ [12]	109274
			D [b8] - 912.4574 + [13]	53233
			T [b6] - 740.4090 + [14]	49104
			D [y5] - 576.2736 + [15]	35232
alpha-1B-glycoprotein	R.SGLSTGWTQLSK.L	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] - 560.8035 ++ [10]	103666
			W [b7] - 689.3253 + [11]	48587
			Q [b9] - 918.4316 + [12]	27677
			T [b8] - 790.3730 + [13]	26742
			L [b10] - 1031.5156 + [14]	23936
alpha-1B-glycoprotein	K.LLELTGPK.S	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	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	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	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.A	862.4837++	D [b6] - 707.3723 + [1]	49322
A1BG_HUMAN			G [y9] - 1017.5952 + [2]	32049
			G [y9] - 509.3012 ++ [3]	27715
alpha-1B-glycoprotein	R.LELHVDGPPPRPQLR.A	575.3249+++	V [y11] - 616.3489 ++ [1]	841163
A1BG_HUMAN			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	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.TPGAAANLELIFVGPQHAGNYR.C	1148.5953++	G [y9] - 999.4755 + [1]	39339
A1BG_HUMAN			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.TPGAAANLELIFVGPQHAGNYR.C	766.0659+++	G [y9] - 999.4755 + [1]	426098
A1BG_HUMAN			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] - 469.7507 ++ [13]	68959
			E [y14] - 800.9152 ++ [14]	67711
			I [y12] - 679.8518 ++ [15]	65740
			N [b7] - 583.2835 + [16]	58648
			A [y17] - 949.9972 ++ [17]	55561
			G [y20] - 1049.5451 ++ [18]	51555
			I [b11] - 1051.5782 + [19]	51489
			L [y13] - 736.3939 ++ [20]	49190
			L [y15] - 857.4572 ++ [21]	48534
			A [y18] - 985.5158 ++ [22]	48337
			L [b8] - 696.3675 + [23]	47352
			N [y16] - 914.4787 ++ [24]	43280
			A [b6] - 469.2405 + [25]	38091
			Q [y7] - 845.4013 + [26]	32443
insulin-like growth factor-	R.SLALGTFAHTPALASLGLSNNR.L	737.7342+++	G [y6] - 660.3424 + [1]	37287
binding protein complex			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	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.E	881.4985++	P [y11] - 1173.6626 + [1]	47285
binding protein complex			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 factor-		588.0014+++	S [y7] - 745.4203 + [1]	29519
binding protein complex			A [y4] - 488.2827 + [2]	23305
acid labile subunit			G [y6] - 658.3883 + [3]	22089
ALS_HUMAN			F [y8] - 892.4887 + [4]	16888
			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	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	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	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.VAGLLEDTFPGLLGLR.V	835.9774++	P [y7] - 725.4668 + [1]	22005
binding protein complex			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.SFEGLGQLEVLTLDHNQLQEVK.A	833.1026+++	Q [y4] - 503.2824 + [1]	328959
binding protein complex			T [y11] - 662.8464 ++ [2]	54479
acid labile subunit			G [b4] - 421.1718 + [3]	24263
ALS_HUMAN
insulin-like growth factor-	R.NLPEQVFR.G	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	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	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	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	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	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	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	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] - 1088.5597 + [13]	37148
			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	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	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	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] - 622.3433 ++ [11]	28163
beta-2-glycoprotein 1	K.CTEEGK.W	362.1525++	E [y3] - 333.1769 + [1]	59464
APOH _HUMAN			E [b3] - 391.1282 + [2]	21675
beta-2-glycoprotein 1	K.WSPELPVCAPIICPPPSIPTFATLR.V	940.4923+++	P [y12] - 648.8692 ++ [1]	294510
APOH_HUMAN			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] - 551.8164 ++ [10]	22284
			C [y13] - 728.8845 ++ [11]	20918
			E [b4] - 500.2140 + [12]	17114
beta-2-glycoprotein 1	K.ATFGCHDGYSLDGPEEIECTK.L	796.0036+++	P [y8] - 503.2315 ++ [1]	67031
APOH_HUMAN			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] - 1005.4144 ++ [9]	35774
			P [y8] - 1005.4557 + [10]	33991
			D [y10] - 1177.5041 + [11]	30366
			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] - 1096.4153 + [16]	17352
beta-2-glycoprotein 1	K.ATWYQGER.V	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	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.CSYTEDAQCIDGTIEVPK.C	1043.4588++	P [y2] - 244.1656 + [1]	34574
APOH_HUMAN			V [y3] - 343.2340 + [2]	9173
			E [y4] - 472.2766 + [3]	7291
			Y [b3] - 411.1333 + [4]	6233
beta-2-glycoprotein 1	K.CSYTEDAQCIDGTIEVPK.C	695.9750+++	D [b11] - 672.2476 ++ [1]	37044
APOH_HUMAN			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	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.—	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	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] - 620.8435 ++ [14]	94640
			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	409.2369++	T [y5] - 605.3141 + [1]	937957
factor-beta-induced			L [b2] - 213.1598 + [2]	298671
protein ig-h3			L [y6] - 718.3981 + [3]	244116
BGH3_HUMAN			L [y2] - 260.1969 + [4]	135739
			D [y4] - 504.2664 + [5]	52472
			E [y3] - 389.2395 + [6]	50839
transforming growth	K.VISTITNNIQQIIEIEDTFETLR.A	897.4798+++	E [y8] - 1010.4789 + [1]	282865
factor-beta-induced			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] - 606.8328 ++ [9]	106647
			I [b5] - 514.3235 + [10]	82030
			N [b8] - 843.4571 + [11]	75125
			T [b4] - 401.2395 + [12]	71448
			I [b12] - 663.3748 ++ [13]	58314
			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] - 542.8035 ++ [18]	43754
			Q [b11] - 1212.6583 + [19]	37375
			T [b6] - 615.3712 + [20]	33322
			I [b9] - 478.7742 ++ [21]	29570
			Q [b10] - 1084.5997 + [22]	25817
			T [y6] - 383.7083 ++ [23]	17187
			N [b8] - 422.2322 ++ [24]	17111
			I [b13] - 719.9168 ++ [25]	16661
transforming growth	K.IPSETLNR.I	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	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	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	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.DILATNGVIHYIDELLIPDSAK.T	804.1003+++	P [y5] - 517.2617 + [1]	400251
factor-beta-induced			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] - 1100.5834 + [10]	24979
			A [y19] - 1035.0493 ++ [11]	23223
			L [y8] - 856.5138 + [12]	22507
			L [y20] - 1091.5913 ++ [13]	16783
transforming growth	K.TLFELAAESDVSTAIDLFR.Q	1049.5388++	D [y4] - 550.2984 + [1]	64464
factor-beta-induced			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	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	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] - 1202.7143 + [15]	16653
transforming growth	K.DGTPPIDAHTR.N	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	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] - 717.3566 ++ [11]	80707
			T [y8] - 818.4618 + [12]	57888
			Q [b6] - 762.3570 + [13]	54766
			G [y10] - 1003.5419 + [14]	51523
			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] - 603.7931 ++ [19]	26902
			G [b5] - 634.2984 + [20]	21858
			Q [b6] - 381.6821 ++ [21]	17595
			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.A	850.9176++	P [y7] - 834.4104 + [1]	364143
factor-beta-induced			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	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	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.YHIGDEILVSGGIGALVR.L	935.0151++	H [b2] - 301.1295 + [1]	24601
factor-beta-induced			S [y9] - 829.4890 + [2]	15456
protein ig-h3
BGH3_HUMAN
transforming growth	K.YHIGDEILVSGGIGALVR.L	623.6791+++	S [y9] - 829.4890 + [1]	917445
factor-beta-induced			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] - 1041.6415 + [21]	38962
			D [b5] - 586.2620 + [22]	36257
			S [b10] - 564.2902 ++ [23]	32329
			I [y6] - 314.7107 ++ [24]	30526
			A [b15] - 741.8830 ++ [25]	27692
			V [y10] - 464.7824 ++ [26]	26340
			L [y11] - 521.3244 ++ [27]	20415
			G [b12] - 621.3117 ++ [28]	18612
			G [b12] - 1241.6161 + [29]	13073
transforming growth	K.LEVSLK.N	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	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	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] - 1106.6092 + [13]	79325
			D [b6] - 601.2464 + [14]	42625
			A [b8] - 759.3155 + [15]	28647
			S [b7] - 688.2784 + [16]	20709
transforming growth	K.QASAFSR.A	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	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.LISVDTEHSNIYLQNGPDR.I	724.3624+++	I [b2] - 227.1754 + [1]	168111
CERU_HUMAN			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] - 1029.4980 ++ [10]	20714
			N [b10] - 1096.5269 + [11]	18087
			I [y9] - 1075.5531 + [12]	15460
ceruloplasmin	K.ALYLQYTDETFR.T	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.TTIEKPVWLGFLGPIIK.A	956.5690++	E [b4] - 445.2293 + [1]	92012
CERU_HUMAN			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] - 1143.6925 + [10]	17412
			I [y15] - 855.5213 ++ [11]	14785
			V [b7] - 769.4454 + [12]	14710
ceruloplasmin	R.TTIEKPVWLGFLGPIIK.A	638.0484+++	G [y8] - 844.5291 + [1]	1645779
CERU_HUMAN			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] - 478.2660 ++ [11]	105521
			P [y12] - 670.4105 ++ [13]	104020
			P [b6] - 670.3770 + [12]	104020
			G [b10] - 1125.6303 + [14]	93363
			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	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	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	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.HYYIAAEEIIWNYAPSGIDIFTK.E	905.4549+++	P [y9] - 977.5302 + [1]	253794
CERU_HUMAN			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] - 602.3059 ++ [18]	24448
			Y [y11] - 1211.6307 + [19]	24238
			G [y7] - 793.4454 + [20]	21926
			W [b11] - 695.3455 ++ [21]	18704
			S [y8] - 880.4775 + [22]	18633
ceruloplasmin	R.IGGSYK.K	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	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.GPEEEHLGILGPVIWAEVGDTIR.V	829.7675+++	A [y8] - 860.4472 + [1]	259776
CERU_HUMAN			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] - 1159.6106 + [10]	58783
			L [b10] - 1075.5419 + [11]	56702
			I [b9] - 962.4578 + [12]	54101
			L [b7] - 792.3523 + [13]	48509
			P [b12] - 615.3117 ++ [14]	37715
			D [y4] - 504.2776 + [15]	34528
			G [b8] - 849.3737 + [16]	34008
			I [b14] - 721.3879 ++ [17]	23669
			H [b6] - 679.2682 + [18]	22174
			W [b15] - 814.4276 ++ [19]	21979
			E [b3] - 284.1241 + [20]	18272
			G [b11] - 566.7853 ++ [21]	17882
			A [b16] - 849.9461 ++ [22]	15476
ceruloplasmin	R.VTFHNK.G	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	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.NNEGTYYSPNYNPQSR.S	952.4139++	P [y4] - 487.2623 + [1]	37339
CERU_HUMAN			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.SVPPSASHVAPTETFTYEWTVPK.E	844.4199+++	P [y2] - 244.1656 + [1]	579331
CERU_HUMAN			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] - 1124.5497 ++ [10]	37303
			P [b3] - 284.1605 + [11]	21690
			E [b18] - 951.4494 ++ [12]	18652
			P [b4] - 381.2132 + [13]	16956
			T [b14] - 681.3384 ++ [14]	15543
ceruloplasmin	K.GSLHANGR.Q	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	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] - 1216.5481 + [10]	35055
			E [b3] - 345.1405 + [11]	20778
			E [y2] - 304.1615 + [12]	19153
ceruloplasmin	R.TYYIAAVEVEWDYSPQR.E	1045.4969++	P [y3] - 400.2303 + [1]	64887
CERU_HUMAN			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
			V [y5] - 650.3257 + [11]	19965
			A [b5] - 612.3028 + [12]	18520
ceruloplasmin	K.ELHHLQEQNVSNAFLDK.G	674.6728+++	N [y6] - 707.3723 + [1]	22715
CERU_HUMAN			L [y3] - 188.1155 ++ [2]	21336
			S [y7] - 794.4043 + [3]	10176
ceruloplasmin	K.GEFYIGSK.Y	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	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.AEEEHLGILGPQLHADVGDK.V	710.0272+++	E [b2] - 201.0870 + [1]	60743
CERU_HUMAN			V [y4] - 418.2296 + [2]	23296
			E [y17] - 899.9759 ++ [3]	14619
ceruloplasmin	K.LEFALLFLVFDENESWYLDDNIK.T	945.1372+++	L [y6] - 359.1925 ++ [1]	19544
CERU_HUMAN			L [b5] - 574.3235 + [2]	17902
ceruloplasmin	K.TYSDHPEK.V	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	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	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.VQLSPDLLATLPEPASPGR.Q	981.0387++	P [y8] - 810.4104 + [1]	51109
activator			Q [b2] - 228.1343 + [2]	19063
HGFA_HUMAN
hepatocyte growth factor	R.TTDVTQTFGIEK.Y	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	596.3402++	P [y7] - 779.4410 + [1]	57992
activator			L [b3] - 314.1710 + [2]	42740
HGFA_HUMAN
hepatocyte growth factor	R.EALVPLVADHK.C	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	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	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.GTHVDLGLASANVDFAFSLYK.Q	1113.0655++	L [b6] - 623.3148 + [1]	244118
AACT_HUMAN			L [b8] - 793.4203 + [2]	211429
			H [b3] - 296.1353 + [3]	204581
			D [b5] - 510.2307 + [4]	200032
			S [y4] - 510.2922 + [5]	195904
			V [b4] - 395.2037 + [6]	187415
			A [b9] - 864.4574 + [7]	167905
			G [b7] - 680.3362 + [8]	87564
			Y [y2] - 310.1761 + [9]	74385
			F [y7] - 875.4662 + [10]	50794
			F [y5] - 657.3606 + [11]	44462
			S [b10] - 951.4894 + [12]	43899
			D [y8] - 990.4931 + [13]	39866
			A [y6] - 728.3978 + [14]	33300
			A [b11] - 1022.5265 + [15]	32502
			L [y3] - 423.2602 + [16]	29829
			V [y9] - 1089.5615 + [17]	22043
			N [b12] - 1136.5695 + [18]	17353
alpha-1-antichymotrypsin	R.GTHVDLGLASANVDFAFSLYK.Q	742.3794+++	D [y8] - 990.4931 + [1]	830612
AACT_HUMAN			L [b8] - 793.4203 + [2]	635646
			G [b7] - 680.3362 + [3]	582273
			S [y4] - 510.2922 + [4]	548645
			D [b5] - 510.2307 + [5]	471071
			F [y7] - 875.4662 + [6]	420278
			A [b9] - 864.4574 + [7]	411366
			A [y6] - 728.3978 + [8]	391668
			Y [y2] - 310.1761 + [9]	390214
			F [y5] - 657.3606 + [10]	358134
			T [b2] - 159.0764 + [11]	288721
			H [b3] - 296.1353 + [12]	251998
			L [b6] - 623.3148 + [13]	240742
			V [y9] - 1089.5615 + [14]	197218
			V [b4] - 395.2037 + [15]	186055
			L [y3] - 423.2602 + [16]	173673
			S [b10] - 951.4894 + [17]	103651
			N [b12] - 1136.5695 + [18]	97976
			A [b11] - 1022.5265 + [19]	76448
alpha-1-antichymotrypsin	K.FNLTETSEAEIHQSFQHLLR.T	800.7363+++	A [b9] - 993.4524 + [1]	75792
AACT_HUMAN			L [b3] - 375.2027 + [2]	59001
			H [y9] - 1165.6225 + [3]	57829
			L [y2] - 288.2030 + [4]	55343
			T [b4] - 476.2504 + [5]	19323
alpha-1-antichymotrypsin	K.EQLSLLDR.F	487.2693++	S [y5] - 603.3461 + [1]	4247034
AACT_HUMAN			L [y3] - 403.2300 + [2]	2094711
			L [y6] - 716.4301 + [3]	1465135
			L [y4] - 516.3140 + [4]	1365427
			Q [b2] - 258.1084 + [5]	1222196
			D [y2] - 290.1459 + [6]	957403
			L [b3] - 371.1925 + [7]	114810
alpha-1-antichymotrypsin	K.EQLSLLDR.F	325.1819+++	L [y3] - 403.2300 + [1]	57123
AACT_HUMAN			D [y2] - 290.1459 + [2]	52105
alpha-1-antichymotrypsin	K.YTGNASALFILPDQDK.M	876.9438++	L [y9] - 1088.5986 + [1]	39933
AACT_HUMAN			A [b5] - 507.2198 + [2]	20117
			D [y4] - 505.2253 + [3]	19937
alpha-1-antichymotrypsin	R.EIGELYLPK.F	531.2975++	P [y2] - 244.1656 + [1]	8170395
AACT_HUMAN			G [y7] - 819.4611 + [2]	3338199
			L [y5] - 633.3970 + [3]	2616703
			L [y3] - 357.2496 + [4]	1922561
			Y [y4] - 520.3130 + [5]	1527792
			G [b3] - 300.1554 + [6]	1417240
			I [b2] - 243.1339 + [7]	1097654
			E [y6] - 762.4396 + [8]	302412
			E [b4] - 429.1980 + [9]	81633
			Y [b6] - 705.3454 + [10]	36795
			L [b5] - 542.2821 + [11]	31993
alpha-1-antichymotrypsin	R.EIGELYLPK.F	354.5341+++	P [y2] - 244.1656 + [1]	189758
AACT_HUMAN			L [y3] - 357.2496 + [2]	86952
			G [b3] - 300.1554 + [3]	49661
			Y [y4] - 520.3130 + [4]	45518
			E [b4] - 429.1980 + [5]	19576
			I [b2] - 243.1339 + [6]	18375
			L [b5] - 542.2821 + [7]	13091
alpha-1-antichymotrypsin	R.DYNLNDILLQLGIEEAFTSK.A	1148.5890++	G [y9] - 981.4888 + [1]	378153
AACT_HUMAN			F [b17] - 981.4964 ++ [2]	378153
			N [b3] - 393.1405 + [3]	338897
			L [y10] - 1094.5728 + [4]	283255
			E [y7] - 811.3832 + [5]	180253
			I [b7] - 848.3785 + [6]	172510
			T [y3] - 335.1925 + [7]	162966
			D [b6] - 735.2944 + [8]	135235
			L [b4] - 506.2245 + [9]	131573
			A [y5] - 553.2980 + [10]	129232
			F [y4] - 482.2609 + [11]	124490
			Y [b2] - 279.0975 + [12]	115367
			L [b9] - 1074.5466 + [13]	106363
			L [b8] - 961.4625 + [14]	101621
			E [y6] - 682.3406 + [15]	98740
			S [y2] - 234.1448 + [16]	75991
			N [b5] - 620.2675 + [17]	66387
			I [y8] - 924.4673 + [18]	61465
alpha-1-antichymotrypsin	R.DYNLNDILLQLGIEEAFTSK.A	766.0618+++	G [y9] - 981.4888 + [1]	309485
AACT_HUMAN			F [b17] - 981.4964 ++ [2]	309485
			E [y7] - 811.3832 + [3]	262306
			N [b3] - 393.1405 + [4]	212306
			T [y3] - 335.1925 + [5]	199100
			F [y4] - 482.2609 + [6]	164346
			A [y5] - 553.2980 + [7]	161405
			Y [b2] - 279.0975 + [8]	149220
			E [y6] - 682.3406 + [9]	138836
			L [y10] - 1094.5728 + [10]	137336
			S [y2] - 234.1448 + [11]	134094
			I [b7] - 848.3785 + [12]	80072
			I [y8] - 924.4673 + [13]	77791
			L [b4] - 506.2245 + [14]	70889
			D [b6] - 735.2944 + [15]	64706
			L [b8] - 961.4625 + [16]	51201
			N [b5] - 620.2675 + [17]	42677
			L [b9] - 1074.5466 + [18]	21609
alpha-1-antichymotrypsin	K.ADLSGITGAR.N	480.7591++	S [y7] - 661.3628 + [1]	4360743
AACT_HUMAN			G [y6] - 574.3307 + [2]	3966462
			T [y4] - 404.2252 + [3]	1937824
			D [b2] - 187.0713 + [4]	799907
			G [y3] - 303.1775 + [5]	647883
			I [y5] - 517.3093 + [6]	612145
			L [b3] - 300.1554 + [7]	606995
			S [b4] - 387.1874 + [8]	544408
			L [y8] - 774.4468 + [9]	348247
			G [b5] - 444.2089 + [10]	232083
			I [b6] - 557.2930 + [11]	132531
			A [y2] - 246.1561 + [12]	113896
alpha-1-antichymotrypsin	K.ADLSGITGAR.N	320.8418+++	T [y4] - 404.2252 + [1]	218597
AACT_HUMAN			G [y3] - 303.1775 + [2]	159381
			G [b5] - 444.2089 + [3]	46527
			A [y2] - 246.1561 + [4]	26911
			D [b2] - 187.0713 + [5]	22497
			S [b4] - 387.1874 + [6]	14589
alpha-1-antichymotrypsin	R.NLAVSQVVHK.A	547.8195++	L [b2] - 228.1343 + [1]	1872233
AACT_HUMAN			A [y8] - 867.5047 + [2]	1133381
			A [b3] - 299.1714 + [3]	1126331
			V [y7] - 796.4676 + [4]	672341
			S [y6] - 697.3991 + [5]	650028
			H [y2] - 284.1717 + [6]	582720
			V [y3] - 383.2401 + [7]	211547
			V [b4] - 398.2398 + [8]	163917
			Q [y5] - 610.3671 + [9]	100778
			V [y4] - 482.3085 + [10]	88456
			S [b5] - 485.2718 + [11]	64488
			V [b7] - 712.3988 + [12]	36045
alpha-1-antichymotrypsin	R.NLAVSQVVHK.A	365.5487+++	L [b2] - 228.1343 + [1]	1175923
AACT_HUMAN			V [y3] - 383.2401 + [2]	593693
			S [y6] - 697.3991 + [3]	587502
			H [y2] - 284.1717 + [4]	440259
			V [y4] - 482.3085 + [5]	375955
			Q [y5] - 610.3671 + [6]	349044
			A [b3] - 299.1714 + [7]	339236
			V [b4] - 398.2398 + [8]	172805
			S [b5] - 485.2718 + [9]	84594
alpha-1-antichymotrypsin	K.AVLDVFEEGTEASAATAVK.I	954.4835++	D [b4] - 399.2238 + [1]	1225699
AACT_HUMAN			G [y11] - 1005.5211 + [2]	812780
			V [b5] - 498.2922 + [3]	741243
			E [y12] - 1134.5637 + [4]	651070
			V [b2] - 171.1128 + [5]	634335
			A [y8] - 718.4094 + [6]	416106
			S [y7] - 647.3723 + [7]	360507
			F [b6] - 645.3606 + [8]	293935
			T [y4] - 418.2660 + [9]	281736
			E [y9] - 847.4520 + [10]	247592
			A [y3] - 317.2183 + [11]	246550
			E [b7] - 774.4032 + [12]	234044
			T [y10] - 948.4997 + [13]	221478
			A [y6] - 560.3402 + [14]	212344
			A [y5] - 489.3031 + [15]	195364
			E [b8] - 903.4458 + [16]	183901
			L [b3] - 284.1969 + [17]	176116
			V [y2] - 246.1812 + [18]	157419
			T [b10] - 1061.5150 + [19]	52841
			E [b11] - 1190.5576 + [20]	34757
			G [b9] - 960.4673 + [21]	25807
alpha-1-antichymotrypsin	K.AVLDVFEEGTEASAATAVK.I	636.6581+++	V [b2] - 171.1128 + [1]	659591
AACT_HUMAN			S [y7] - 647.3723 + [2]	630596
			A [y8] - 718.4094 + [3]	509467
			D [b4] - 399.2238 + [4]	353335
			A [y6] - 560.3402 + [5]	306747
			A [y5] - 489.3031 + [6]	280878
			E [y9] - 847.4520 + [7]	247347
			T [y4] - 418.2660 + [8]	197203
			A [y3] - 317.2183 + [9]	128853
			V [b5] - 498.2922 + [10]	120271
			V [y2] - 246.1812 + [11]	115428
			L [b3] - 284.1969 + [12]	102984
			G [y11] - 1005.5211 + [13]	91215
			F [b6] - 645.3606 + [14]	79016
			E [y 12] - 1134.5637 + [15]	72947
			E [b7] - 774.4032 + [16]	58358
			T [y10] - 948.4997 + [17]	41071
			E [b8] - 903.4458 + [18]	32918
			G [b9] - 960.4673 + [19]	24275
alpha-1-antichymotrypsin	K.ITLLSALVETR.T	608.3690++	S [y7] - 775.4308 + [1]	7387615
AACT_HUMAN			T [b2] - 215.1390 + [2]	3498457
			L [yS] - 888.5149 + [3]	2684639
			L [b3] - 328.2231 + [4]	2164246
			A [y6] - 688.3988 + [5]	2045853
			L [y5] - 617.3617 + [6]	2027311
			L [y9] - 1001.5990 + [7]	1949318
			V [y4] - 504.2776 + [8]	1598519
			T [y2] - 276.1666 + [9]	1416847
			E [y3] - 405.2092 + [10]	967259
			A [b6] - 599.3763 + [11]	579420
			L [b4] - 441.3071 + [12]	431556
			S [b5] - 528.3392 + [13]	107634
			L [b7] - 712.4604 + [14]	71104
			V [b8] - 811.5288 + [15]	24197
alpha-1-antichymotrypsin	K.ITLLSALVETR.T	405.9151+++	E [y3] - 405.2092 + [1]	738128
AACT_HUMAN			T [y2] - 276.1666 + [2]	368830
			V [y4] - 504.2776 + [3]	328133
			A [b6] - 599.3763 + [4]	132469
			T [b2] - 215.1390 + [5]	126898
			L [y5] - 617.3617 + [6]	124559
			S [y7] - 775.4308 + [7]	54263
			L [b3] - 328.2231 + [8]	37891
			A [y6] - 688.3988 + [9]	29853
			L [b4] - 441.3071 + [10]	25558
			L [b7] - 712.4604 + [11]	13353
			S [b5] - 528.3392 + [12]	12290
Pigment epithelium-	K.LAAAVSNFGYDLYR.V	780.3963++	D [b11] - 1109.5262 + [1]	136227
derived factor			F [b8] - 774.4145 + [2]	61248
PEDF_HUMAN*			N [b7] - 314.1767 ++ [3]	55532
			A [y12] - 1375.6641 + [4]	53268
			V [b5] - 213.6392 ++ [5]	35818
			L [b12] - 1222.6103 + [6]	34918
			G [b9] - 831.4359 + [7]	33934
			Y [b10] - 994.4993 + [8]	32923
			G [b9] - 416.2216 ++ [9]	32650
			V [b5] - 426.2711 + [10]	15646
			A [b2] - 185.1285 + [11]	14964
			D [b11] - 555.2667 ++ [12]	13922
			L [y3] - 226.1368 ++ [13]	13027
			A [b4] - 327.2027 + [14]	12782
			A [y12] - 688.3357 ++ [15]	12446
			V [y10] - 1233.5899 + [16]	12400
			A [y11] - 652.8171 ++ [17]	10793
Pigment epithelium-	K.LAAAVSNFGYDLYR.V	520.5999+++	G [y6] - 786.3781 + [1]	42885
derived factor			D [y4] - 566.2933 + [2]	32080
PEDF_HUMAN*			Y [y5] - 729.3566 + [3]	17494
			L [y3] - 451.2663 + [5]	12304
			Y [y2] - 338.1823 + [6]	7780
Pigment epithelium-	R.ALYYDLISSPDIHGTYK.E	652.6632+++	Y [y15] - 886.4305 ++ [1]	12278
derived factor			L [b2] - 185.1285 + [2]	7601
PEDF_HUMAN*			S [y10] - 1104.5320 + [3]	7345
			Y [y14] - 804.8988 ++ [4]	5976
Pigment epithelium-	K.ELLDTVTAPQK.N	607.8350++	T [y5] - 272.6581 ++ [1]	59670
derived factor			Q [y2] - 275.1714 + [2]	11954
PEDF_HUMAN*
Pigment epithelium-	K.ELLDTVTAPQK.N	405.5591+++	L [b2] - 243.1339 + [1]	16428
derived factor			T [b7] - 386.7080 ++ [2]	7918
PEDF_HUMAN*			Q [y2] - 275.1714 + [3]	7043
			T [y5] - 272.6581 ++ [4]	5237
Pigment epithelium-	K.SSFVAPLEK.S	489.2687++	A [y5] - 557.3293 + [1]	20068
derived factor			A [y5] - 279.1683 ++ [2]	5059
PEDF_HUMAN*			S [b2] - 175.0713 + [3]	4883
Pigment epithelium-	K.SSFVAPLEK.S	326.5149+++	A [y5] - 279.1683 ++ [1]	70240
derived factor			A [y5] - 557.3293 + [2]	63329
PEDF_HUMAN*			S [b2] - 175.0713 + [3]	39662
			L [b7] - 351.6947 ++ [4]	5393
Pigment epithelium-	K.EIPDEISILLLGVAHFK.G	632.0277+++	P [y15] - 826.4745 ++ [1]	37871
derived factor			G [y6] - 658.3671 + [2]	20077
PEDF_HUMAN*			L [y7] - 771.4512 + [3]	8952
Pigment epithelium-	K.TSLEDFYLDEER.T	758.8437++	R [y1] - 175.1190 + [1]	8206
derived factor			D [b9] - 1084.4833 + [2]	4591
PEDF_HUMAN*			F [b6] - 693.3090 + [3]	4498
Pigment epithelium-	K.TSLEDFYLDEER.T	506.2316+++	F [b6] - 693.3090 + [1]	3526
derived factor			D [y4] - 548.2311 + [2]	3208
PEDF_HUMAN*
Pigment epithelium-	K.VTQNLTLIEESLTSEFIHDIDR.E	858.4413+++	T [b13] - 721.8905 ++ [1]	11072
derived factor			T [y17] - 1009.5075 ++ [2]	8442
PEDF_HUMAN*			D [y4] - 518.2569 + [3]	6522
Pigment epithelium-	K.TVQAVLTVPK.L	528.3266++	Q [y8] - 855.5298 + [1]	83536
derived factor			V [b2] - 201.1234 + [2]	64729
PEDF_HUMAN*			A [b4] - 200.6132 ++ [3]	58198
			P [y2] - 244.1656 + [4]	43347
			Q [y8] - 428.2686 ++ [5]	38398
			A [y7] - 727.4713 + [6]	33770
			Q [b3] - 329.1819 + [7]	17809
			L [y5] - 557.3657 + [8]	17518
			V [y6] - 656.4341 + [9]	17029
			V [y6] - 328.7207 ++ [10]	15839
			T [y4] - 444.2817 + [11]	13859
			V [y3] - 343.2340 + [12]	10717
			A [b4] - 400.2191 + [13]	9695
Pigment epithelium-	K.TVQAVLTVPK.L	352.5535+++	P [y2] - 244.1656 + [1]	8295
derived factor			T [y4] - 444.2817 + [2]	2986
PEDF_HUMAN*			A [b4] - 400.2191 + [3]	2848
Pigment epithelium-	K.LSYEGEVTK.S	513.2611++	V [b7] - 389.6845 ++ [1]	60831
derived factor			E [b6] - 679.2933 + [2]	34857
PEDF_HUMAN*			V [y7] - 413.2031 ++ [3]	10075
			V [b7] - 778.3618 + [4]	8920
			Y [b3] - 364.1867 + [5]	8008
Pigment epithelium-	K.LQSLFDSPDFSK.I	692.3432++	S [y2] - 234.1448 + [1]	49594
derived factor			L [y9] - 1055.5044 + [2]	48160
PEDF_HUMAN*			P [b8] - 888.4462 + [3]	23566
			S [b7] - 791.3934 + [4]	13766
			P [y5] - 297.1501 ++ [5]	12305
			P [y5] - 593.2930 + [6]	10702
			F [b5] - 589.3344 + [7]	8929
			D [b9] - 1003.4731 + [8]	8742
Pigment epithelium-	K.LQSLFDSPDFSK.I	461.8979+++	P [y5] - 593.2930 + [1]	9154
derived factor			P [y5] - 297.1501 ++ [2]	5479
PEDF_HUMAN*
Pigment epithelium-	R.DTDTGALLFIGK.I	625.8350++	G [y2] - 204.1343 + [1]	32092
derived factor			G [y8] - 818.5135 + [2]	29707
PEDF_HUMAN*			T [b2] - 217.0819 + [4]	28172
			T [b4] - 217.0819 ++ [3]	28172
			F [y4] - 464.2867 + [5]	22160
			D [y10] - 1034.5881 + [6]	20267
			T [y9] - 919.5611 + [7]	17083
			L [y6] - 690.4549 + [8]	14854
			L [y5] - 577.3708 + [9]	12349
			T [b4] - 433.1565 + [10]	11773
			I [y3] - 317.2183 + [11]	11575
			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 (>1=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
Transition	Protein	AUC
LDFHFSSDR_375.2_611.3	INHBC_HUMAN	0.785
TVQAVLTVPK_528.3_428.3	PEDF_HUMAN	0.763
TVQAVLTVPK_528.3_855.5	PEDF_HUMAN	0.762
ETLLQDFR_511.3_565.3	AMBP_HUMAN	0.756
DTDTGALLFIGK_625.8_818.5	PEDF_HUMAN	0.756
DTDTGALLFIGK_625.8_217.1	PEDF_HUMAN	0.756
IQTHSTTYR_369.5_627.3	F13B_HUMAN	0.755
IQTHSTTYR_369.5_540.3	F13B_HUMAN	0.753
ETLLQDFR_511.3_322.2	AMBP_HUMAN	0.751
LDFHFSSDR_375.2_464.2	INHBC_HUMAN	0.745
HHGPTITAK_321.2_275.1	AMBP_HUMAN	0.743
VNHVTLSQPK_374.9_244.2	B2MG_HUMAN	0.733
VEHSDLSFSK_383.5_468.2	B2MG_HUMAN	0.732
ALALPPLGLAPLLNLWAK-	SHBG_HUMAN	0.728
PQGR_770.5_256.2
HHGPTITAK_321.2_432.3	AMBP_HUMAN	0.728
FLYHK_354.2_447.2	AMBP_HUMAN	0.722
FLYHK_354.2_284.2	AMBP_HUMAN	0.721
IALGGLLFPASNLR_481.3	SHBG_HUMAN	0.719
657.4
GDTYPAELYITGSILR_	F13B_HUMAN	0.716
885.0_274.1
VEHSDLSFSK_383.5_234.1	B2MG_HUMAN	0.714
GPGEDFR_389.2_623.3	PTGDS_HUMAN	0.714
IALGGLLFPASNLR_481.3_	SHBG_HUMAN	0.712
412.3
EVFSKPISWEELLQ_852.9_	FA40A_HUMAN	0.708
260.2
FICPLTGLWPINTLK_887.0_	APOH_HUMAN	0.707
685.4
GFQALGDAADIR_617.3_717.4	TINIP1_HUMAN	0.707
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	0.704
791.8_310.2
VVLSSGSGPGLDLPLVLGLPL-	SHBG_HUMAN	0.704
QLK_791.5_598.4
ATVVYQGER_511.8_652.3	APOH_HUMAN	0.702
ALALPPLGLAPLLNLWAKPQ-	SHBG_HUMAN	0.702
GR_770.5_457.3
VVLSSGSGPGLDLPLVLGLPL-	SHBG_HUMAN	0.702
QLK_791.5_768.5
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	0.702
791.8_883.0
AHYDLR_387.7_566.3	FETUA_HUMAN	0.701
GPGEDFR_389.2_322.2	PTGDS_HUMAN	0.701
FSVVYAK_407.2_579.4	FETUA_HUMAN	0.701
TLAFVR_353.7_274.2	FA7_HUMAN	0.699
IAPQLSTEELVSLGEK_857.5_	AFAM_HUMAN	0.698
533.3
HFQNLGK_422.2_527.2	AFAM_HUMAN	0.696
GDTYPAELYITGSILR_885.0_	F13B_HUMAN	0.694
922.5
FICPLTGLWPINTLK_887.0_756.9	APOH_HUMAN	0.694
EVFSKPISWEELLQ_852.9_376.2	FA40A_HUMAN	0.692
ATVVYQGER_511.8_751.4	APOH_HUMAN	0.690
ELIEELVNITQNQK_557.6_618.3	IL13_HUMAN	0.690
VNHVTLSQPK_374.9_459.3	B2MG_HUMAN	0.687
IAQYYYTFK_598.8_395.2	F13B_HUMAN	0.685
IAPQLSTEELVSLGEK_857.5_333.2	AFAM_HUMAN	0.685
LIENGYFHPVK_439.6_627.4	F13B_HUMAN	0.684
FSVVYAK_407.2_381.2	FETUA_HUMAN	0.684
HFQNLGK_422.2_285.1	AFAM_HUMAN	0.684
AHYDLR_387.7_288.2	FETUA_HUMAN	0.684
ELPQSIVYK_538.8_417.7	FBLN3_HUMAN	0.683
DADPDTFFAK_563.8_825.4	AFAM_HUMAN	0.679
DADPDTFFAK_563.8_302.1	AFAM_HUMAN	0.676
IAQYYYTFK_598.8_884.4	F13B_HUMAN	0.673
VVESLAK_373.2_646.4	IBP1_HUMAN	0.673
YGIEEHGK_311.5_599.3	CXA1_HUMAN	0.673
GFQALGDAADIR_617.3_288.2	TINIP1_HUMAN	0.673
YTTEIIK_434.2_704.4	C1R_HUMAN	0.671
LPDTPQGLLGEAR_683.87427.2	EGLN_HUMAN	0.666
TLAFVR_353.7_492.3	FA7_HUMAN	0.666
LIENGYFHPVK_439.6_343.2	F13B_HUMAN	0.665
ELIEELVNITQNQK_557.6_517.3	IL13_HUMAN	0.665
DPNGLPPEAQK_583.3_669.4	RET4_HUMAN	0.664
TNTNEFLIDVDK_704.85_849.5	TF_HUMAN	0.663
NTVISVNPSTK_580.3_845.5	VCAM1_HUMAN	0.662
YEFLNGR_449.7_293.1	PLMN_HUMAN	0.662
AIGLPEELIQK_605.86_856.5	FABPL_HUMAN	0.662
YTTEIIK_434.2_603.4	C1R_HUMAN	0.661
AEHPTWGDEQLFQTTR_639.3_	PGH1_HUMAN	0.658
765.4
HTLNQIDEVK_598.8_951.5	FETUA_HUMAN	0.658
HTLNQIDEVK_598.8_958.5	FETUA_HUMAN	0.656
LPNNVLQEK_527.8_730.4	AFAM_HUMAN	0.655
DPNGLPPEAQK_583.3_497.2	RET4_HUMAN	0.655
TFLTVYWTPER_706.9_401.2	ICAM1_HUMAN	0.653
TFLTVYWTPER_706.9_502.3	ICAM1_HUMAN	0.653
SEPRPGVLLR_375.2_454.3	FA7_HUMAN	0.652
FTFTLHLETPKPSISSSNLNPR_	PSG1_HUMAN	0.652
829.4_787.4
DAQYAPGYDK_564.3_813.4	CFAB_HUMAN	0.651
ALDLSLK_380.2_185.1	ITIH3_HUMAN	0.651
NCSFSIIYPVVIK_770.4_555.4	CRHBP_HUMAN	0.650
NTVISVNPSTK_580.3_732.4	VCAM1_HUMAN	0.649
IPSNPSHR_303.2_610.3	FBLN3_HUMAN	0.649
DAQYAPGYDK_564.3_315.1	CFAB_HUMAN	0.647
TLPFSR_360.7_506.3	LYAM1_HUMAN	0.647
LPNNVLQEK_527.8_844.5	AFAM_HUMAN	0.644
AALAAFNAQNNGSNFQLEEI-	FETUA_HUMAN	0.644
SR_789.1_746.4
AEHPTWGDEQLFQTTR_639.3_	PGH1_HUMAN	0.644
569.3
NNQLVAGYLQGPNVNLEEK_	LIRA_HUMAN	0.642
700.7_999.5
EHSSLAFWK_552.8_267.1	APOH_HUMAN	0.642
ALNHLPLEYNSALYSR_621.0_	CO6_HUMAN	0.641
696.4
VSEADSSNADWVTK_754.9_347.2	CFAB_HUMAN	0.641
NFPSPVDAAFR_610.8_959.5	HEMO_HUMAN	0.641
WNFAYWAAHQPWSR_607.3_545.3	PRG2_HUMAN	0.638
WNFAYWAAHQPWSR_607.3_673.3	PRG2_HUMAN	0.638
TAVTANLDIR_537.3_802.4	CHL1_HUMAN	0.638
IPSNPSHR_303.2_496.3	FBLN3_HUMAN	0.637
YWGVASFLQK_599.8_849.5	RET4_HUMAN	0.637
ALDLSLK_380.2_575.3	ITIH3_HUMAN	0.636
YNSQLLSFVR_613.8_508.3	TFR1_HUMAN	0.636
EHSSLAFWK_552.8_838.4	APOH_HUMAN	0.635
YWGVASFLQK_599.8_350.2	RET4_HUMAN	0.635
ALNHLPLEYNSALYSR_621.0_	CO6_HUMAN	0.633
538.3
DLYHYITSYVVDGEIIIYGPAY-	PSG1_HUMAN	0.633
SGR_955.5_707.3
FTFTLHLETPKPSISSSNLNPR_	PSG1_HUMAN	0.633
829.4_874.4
YQISVNK_426.2_560.3	FIBB_HUMAN	0.632
YEFLNGR_449.7_606.3	PLMN_HUMAN	0.632
LNIGYIEDLK_589.3_950.5	PAI2_HUMAN	0.631
LLEVPEGR_456.8_356.2	C1S_HUMAN	0.630
ENPAVIDFELAPIVDLVR_	CO6_HUMAN	0.630
670.7_811.5
YYLQGAK_421.7_516.3	ITIH4_HUMAN	0.630
ITGFLKPGK_320.9_301.2	LBP_HUMAN	0.629
DLHLSDVFLK_396.2_260.2	CO6_HUMAN	0.629
HELTDEELQSLFTNFANVVDK_	AFAM_HUMAN	0.629
817.1_854.4
YYLQGAK_421.7_327.1	ITIH4_HUMAN	0.628
NCSFSIIYPVVIK_770.4_831.5	CRHBP_HUMAN	0.627
FLNWIK_410.7_560.3	HABP2_HUMAN	0.627
ITGFLKPGK_320.9_429.3	LBP_HUMAN	0.627
VVESLAK_373.2_547.3	IBP1_HUMAN	0.627
NFPSPVDAAFR_610.8_775.4	HEMO_HUMAN	0.627
AEIEYLEK_497.8_552.3	LYAM1_HUMAN	0.627
ENPAVIDFELAPIVDLVR_670.7_	CO6_HUMAN	0.627
601.4
VQEVLLK_414.8_373.3	HYOU1_HUMAN	0.626
TQIDSPLSGK_523.3_703.4	VCAM1_HUMAN	0.626
VSEADSSNADWVTK_754.9_533.3	CFAB_HUMAN	0.625
DFNQFSSGEK_386.8_189.1	FETA_HUMAN	0.624
LPDTPQGLLGEAR_683.87_940.5	EGLN_HUMAN	0.623
DLYHYITSYVVDGEIIIYGPAY-	PSG1_HUMAN	0.623
SGR_955.5_650.3
FAFNLYR_465.8_712.4	HEP2_HUMAN	0.623
LLELTGPK_435.8_644.4	A1BG_HUMAN	0.623
NEIVFPAGILQAPFYTR_968.5_	ECE1_HUMAN	0.623
357.2
EFDDDTYDNDIALLQLK_	TPA_HUMAN	0.621
1014.48_501.3
FSLVSGWGQLLDR_493.3_403.2	FA7_HUMAN	0.621
LLELTGPK_435.8_227.2	A1BG_HUMAN	0.621
LIQDAVTGLTVNGQITGDK_	ITIH3_HUMAN	0.621
972.0_640.4
QGHNSVFLIK_381.6_520.4	HEMO_HUMAN	0.620
ILPSVPK_377.2_244.2	PGH1_HUMAN	0.620
STLFVPR_410.2_272.2	PEPD_HUMAN	0.620
TLEAQLTPR_514.8_685.4	HEP2_HUMAN	0.619
QGHNSVFLIK_381.6_260.2	HEMO_HUMAN	0.619
LSSPAVITDK_515.8_743.4	PLMN_HUMAN	0.618
LLEVPEGR_456.8_686.4	C1S_HUMAN	0.617
GVTGYFTFNLYLK_508.3_260.2	PSG5_HUMAN	0.617
EALVPLVADHK_397.9_390.2	HGFA_HUMAN	0.616
SFRPFVPR_335.9_272.2	LBP_HUMAN	0.616
DFNQFSSGEK_386.8_333.2	FETA_HUMAN	0.616
GSLVQASEANLQAAQDFVR_	ITIH1_HUMAN	0.616
668.7_735.4
ITLPDFTGDLR_624.3_920.5	LBP_HUMAN	0.615
LIQDAVTGLTVNGQITGDK_	ITIH3_HUMAN	0.615
972.0_798.4
ILPSVPK_377.2_227.2	PGH1_HUMAN	0.614
DIIKPDPPK_511.8_342.2	IL12B_HUMAN	0.613
QGFGNVATNTDGK_654.81_319.2	FIBB_HUMAN	0.613
AVLHIGEK_289.5_348.7	THBG_HUMAN	0.613
YENYTSSFFIR_713.8_756.4	IL12B_HUMAN	0.613
LSSPAVITDK_515.8_830.5	PLMN_HUMAN	0.613
SFRPFVPR_335.9_635.3	LBP_HUMAN	0.613
GLQYAAQEGLLALQSELLR_	LBP_HUMAN	0.612
1037.1_858.5
VELAPLPSWQPVGK_760.9_400.3	ICAM1_HUMAN	0.612
CRPINATLAVEK_457.9_559.3	CGB1_HUMAN	0.610
GIVEECCFR_585.3_771.3	IGF2_HUMAN	0.610
AVLHIGEK_289.5_292.2	THBG_HUMAN	0.610
TLEAQLTPR_514.8_814.4	HEP2_HUMAN	0.610
SILFLGK_389.2_577.4	THBG_HUMAN	0.609
HVVQLR_376.2_614.4	IL6RA_HUMAN	0.609
TQILEWAAER_608.8_761.4	EGLN_HUMAN	0.609
NSDQEIDFK_548.3_409.2	S10A5_HUMAN	0.609
SGAQATWTELPWPHEK_	HEMO_HUMAN	0.607
613.3_510.3
EDTPNSVWEPAK_686.8_630.3	C1S_HUMAN	0.607
ITLPDFTGDLR_624.3_288.2	LBP_HUMAN	0.607
TLPFSR_360.7_409.2	LYAM1_HUMAN	0.607
GIVEECCFR_585.3_900.3	IGF2_HUMAN	0.606
SGAQATWTELPWPHEK_	HEMO_HUMAN	0.606
613.3_793.4
VRPQQLVK_484.3_609.4	ITIH4_HUMAN	0.605
SEYGAALAWEK_612.8_788.4	CO6_HUMAN	0.605
LEEHYELR_363.5_288.2	PAI2_HUMAN	0.605
FQLPGQK_409.2_275.1	PSG1_HUMAN	0.605
IHWESASLLR_606.3_437.2	CO3_HUMAN	0.604
NAVVQGLEQPHGLVVHPLR_	LRP1_HUMAN	0.604
688.4_890.6
VTGLDFIPGLHPILTLSK_	LEP_HUMAN	0.603
641.04_771.5
YNSQLLSFVR_613.8_734.5	TFR1_HUMAN	0.603
ALVLELAK_428.8_672.4	INHBE_HUMAN	0.603
FAFNLYR_465.8_565.3	HEP2_HUMAN	0.603
VRPQQLVK_484.3_722.4	ITIH4_HUMAN	0.602
SLQAFVAVAAR_566.8_487.3	IL23A_HUMAN	0.602
AGFAGDDAPR_488.7_701.3	ACTB_HUMAN	0.601
EDTPNSVWEPAK_686.8_315.2	C1S_HUMAN	0.601
VQEVLLK_414.8_601.4	HYOU1_HUMAN	0.601
SEYGAALAWEK_612.8_845.5	CO6_HUMAN	0.601
TLFIFGVTK_513.3_215.1	PSG4_HUMAN	0.601
YNQLLR_403.7_288.2	ENOA_HUMAN	0.600
TQIDSPLSGK_523.3_816.5	VCAM1_HUMAN	0.600

TABLE 13
Transition	Protein	AUC
LDFHFSSDR_375.2_611.3	INHBC_HUMAN	0.858
LDFHFSSDR_375.2_464.2	INHBC_HUMAN	0.838
ELPQSIVYK_538.8_417.7	FBLN3_HUMAN	0.815
VNHVTLSQPK_374.9_244.2	B2MG_HUMAN	0.789
GFQALGDAADIR_617.3_717.4	TIMP1_HUMAN	0.778
VEHSDLSFSK_383.5_234.1	B2MG_HUMAN	0.778
TVQAVLTVPK_528.3_428.3	PEDF_HUMAN	0.775
TVQAVLTVPK_528.3_855.5	PEDF_HUMAN	0.775
DTDTGALLFIGK_625.8_217.1	PEDF_HUMAN	0.772
ETLLQDFR_511.3_565.3	AMBP_HUMAN	0.772
DTDTGALLFIGK_625.8_818.5	PEDF_HUMAN	0.769
VVESLAK_373.2_646.4	IBP1_HUMAN	0.766
FSVVYAK_407.2_381.2	FETUA_HUMAN	0.764
HHGPTITAK_321.2_275.1	AMBP_HUMAN	0.764
ETLLQDFR_511.3_322.2	AMBP_HUMAN	0.761
FLYHK_354.2_447.2	AMBP_HUMAN	0.758
GPGEDFR_389.2_623.3	PTGDS_HUMAN	0.755
HHGPTITAK_321.2_432.3	AMBP_HUMAN	0.755
VEHSDLSFSK_383.5_468.2	B2MG_HUMAN	0.752
FLYHK_354.2_284.2	AMBP_HUMAN	0.749
FSVVYAK_407.2_579.4	FETUA_HUMAN	0.749
VNHVTLSQPK_374.9_459.3	B2MG_HUMAN	0.749
IPSNPSHR_303.2_610.3	FBLN3_HUMAN	0.746
VVESLAK_373.2_547.3	IBP1_HUMAN	0.746
IPSNPSHR_303.2_496.3	FBLN3_HUMAN	0.746
NCSFSIIYPVVIK_770.4_555.4	CRHBP_HUMAN	0.746
GFQALGDAADIR_617.3_288.2	TIMP1_HUMAN	0.744
IQTHSTTYR_369.5_627.3	F13B_HUMAN	0.744
AALAAFNAQNNGSNFQLEE-	FETUA_HUMAN	0.738
ISR_789.1_746.4
AHYDLR_387.7_566.3	FETUA_HUMAN	0.738
IQTHSTTYR_369.5_540.3	F13B_HUMAN	0.738
AIGLPEELIQK_605.86_856.5	FABPL_HUMAN	0.735
ATVVYQGER_511.8_751.4	APOH_HUMAN	0.735
FICPLTGLWPINTLK_887.0_685.4	APOH_HUMAN	0.735
FICPLTGLWPINTLK_887.0_756.9	APOH_HUMAN	0.735
HTLNQIDEVK_598.8_958.5	FETUA_HUMAN	0.735
AQETSGEEISK_589.8_979.5	IBM_HUMAN	0.732
DSPSVWAAVPGK_607.31_301.2	PROF1_HUMAN	0.732
GPGEDFR_389.2_322.2	PTGDS_HUMAN	0.732
ATVVYQGER_511.8_652.3	APOH_HUMAN	0.729
NFPSPVDAAFR_610.8_959.5	HEMO_HUMAN	0.729
LIENGYFHPVK_439.6_627.4	F13B_HUMAN	0.726
AHYDLR_387.7_288.2	FETUA_HUMAN	0.726
ELIEELVNITQNQK_557.6_618.3	IL13_HUMAN	0.724
ETPEGAEAKPWYEPIYLGGVFQ-	TNFA_HUMAN	0.724
LEK_951.14_877.5
ALDLSLK_380.2_185.1	ITIH3_HUMAN	0.721
IHWESASLLR_606.3_437.2	CO3_HUMAN	0.721
DAQYAPGYDK_564.3_813.4	CFAB_HUMAN	0.718
NFPSPVDAAFR_610.8_775.4	HEMO_HUMAN	0.718
AVGYLITGYQR_620.8_523.3	PZP_HUMAN	0.715
AVGYLITGYQR_620.8_737.4	PZP_HUMAN	0.712
DIPHWLNPTR_416.9_600.3	PAPP1_HUMAN	0.712
ALDLSLK_380.2_575.3	ITIH3_HUMAN	0.709
IEGNLIFDPNNYLPK_874.0_845.5	APOB_HUMAN	0.709
LIENGYFHPVK_439.6_343.2	F13B_HUMAN	0.709
QTLSWTVTPK_580.8_818.4	PZP_HUMAN	0.709
DAQYAPGYDK_564.3_315.1	CFAB_HUMAN	0.707
GLQYAAQEGLLALQSELLR_	LBP_HUMAN	0.707
1037.1_858.5
IEGNLIFDPNNYLPK_874.0_414.2	APOB_HUMAN	0.707
IQHPFTVEEFVLPK_562.0_861.5	PZP_HUMAN	0.707
QTLSWTVTPK_580.8_545.3	PZP_HUMAN	0.707
VSEADSSNADWVTK_754.9_347.2	CFAB_HUMAN	0.707
ILPSVPK_377.2_244.2	PGH1_HUMAN	0.704
IQHPFTVEEFVLPK_562.0_603.4	PZP_HUMAN	0.704
NCSFSIIYPVVIK_770.4_831.5	CRHBP_HUMAN	0.704
YNSQLLSFVR_613.8_508.3	TFR1_HUMAN	0.704
HTLNQIDEVK_598.8_951.5	FETUA_HUMAN	0.701
NEIWYR_440.7_637.4	FA12_HUMAN	0.701
QGHNSVFLIK_381.6_260.2	HEMO_HUMAN	0.701
YTTEIIK_434.2_603.4	C1R_HUMAN	0.701
STLFVPR_410.2_272.2	PEPD_HUMAN	0.699
EVFSKPISWEELLQ_852.9_260.2	FA40A_HUMAN	0.698
TGISPLALIK_506.8_741.5	APOB_HUMAN	0.698
TSESGELHGLTTEEEFVEGI-	TTHY_HUMAN	0.698
YK_819.06_310.2
AEHPTWGDEQLFQTTR_639.3_	PGH1_HUMAN	0.695
569.3
AEHPTWGDEQLFQTTR_639.3_	PGH1_HUMAN	0.695
765.4
HFQNLGK_422.2_527.2	AFAM_HUMAN	0.695
SVSLPSLDPASAK_636.4_473.3	APOB_HUMAN	0.695
ILPSVPK_377.2_227.2	PGH1_HUMAN	0.692
LIQDAVTGLTVNGQITGDK_	ITIH3_HUMAN	0.692
972.0_640.4
QGHNSVFLIK_381.6_520.4	HEMO_HUMAN	0.692
TGISPLALIK_506.8_654.5	APOB_HUMAN	0.692
Transition	Protein	AUC
YGIEEHGK_311.5_599.3	CXA1_HUMAN	0.692
ELIEELVNITQNQK_557.6_517.3	IL13_HUMAN	0.689
IHWESASLLR_606.3_251.2	CO3_HUMAN	0.689
LIQDAVTGLTVNGQITGDK_	ITIH3_HUMAN	0.689
972.0_798.4
ALALPPLGLAPLLNLWAKP-	SHBG_HUMAN	0.687
QGR_770.5_256.2
ALNFGGIGVVVGHELTHAFDD-	ECE1_HUMAN	0.687
QGR_837.1_299.2
AQETSGEEISK_589.8_850.4	IBM_HUMAN	0.687
GVTGYFTFNLYLK_508.3_683.9	PSG5_HUMAN	0.687
ITLPDFTGDLR_624.3_288.2	LBP_HUMAN	0.687
LPDTPQGLLGEAR_683.87_427.2	EGLN_HUMAN	0.687
SVSLPSLDPASAK_636.4_885.5	APOB_HUMAN	0.687
TLAFVR_353.7_274.2	FA7_HUMAN	0.687
YTTEIIK_434.2_704.4	C1R_HUMAN	0.687
EFDDDTYDNDIALLQLK_	TPA_HUMAN	0.684
1014.4_388.3
IALGGLLFPASNLR_481.3_657.4	SHBG_HUMAN	0.684
DFNQFSSGEK_386.8_189.1	FETA_HUMAN	0.681
EHSSLAFWK_552.8_838.4	APOH_HUMAN	0.681
ELPQSIVYK_538.8_409.2	FBLN3_HUMAN	0.681
ITGFLKPGK_320.9_301.2	LBP_HUMAN	0.681
ITGFLKPGK_320.9_429.3	LBP_HUMAN	0.681
AFQVWSDVTPLR_709.88_385.3	MMP2_HUMAN	0.678
GLQYAAQEGLLALQSELLR_	LBP_HUMAN	0.678
1037.1_929.5
HYINLITR_515.3_301.1	NPY_HUMAN	0.678
NAVVQGLEQPHGLVVHPLR_	LRP1_HUMAN	0.675
688.4_890.6
WWGGQPLWITATK_772.4_	ENPP2_HUMAN	0.675
929.5
YNQLLR_403.7_288.2	ENOA_HUMAN	0.675
LDGSTHLNIFFAK_488.3_852.5	PAPP1_HUMAN	0.672
VVGGLVALR_442.3_784.5	FA12_HUMAN	0.672
WNFAYWAAHQPWSR_607.3_	PRG2_HUMAN	0.672
673.3
NHYTESISVAK_624.8_252.1	NEUR1_HUMAN	0.670
NSDQEIDFK_548.3_409.2	S10A5_HUMAN	0.670
SGAQATWTELPWPHEK_613.3_	HEMO_HUMAN	0.670
510.3
WNFAYWAAHQPWSR_607.3_	PRG2_HUMAN	0.670
545.3
SFRPFVPR_335.9_272.2	LBP_HUMAN	0.670
AFQVWSDVTPLR_709.88_347.2	MMP2_HUMAN	0.667
DADPDTFFAK_563.8_825.4	AFAM_HUMAN	0.667
EHSSLAFWK_552.8_267.1	APOH_HUMAN	0.667
ITENDIQIALDDAK_779.9_632.3	APOB_HUMAN	0.667
ITLPDFTGDLR_624.3_920.5	LBP_HUMAN	0.667
VQEVLLK_414.8_373.3	HYOU1_HUMAN	0.667
VSFSSPLVAISGVALR_802.0_715.4	PAPP1_HUMAN	0.667
HFQNLGK_422.2_285.1	AFAM_HUMAN	0.664
ITENDIQIALDDAK_779.9_873.5	APOB_HUMAN	0.664
ALQDQLVLVAAK_634.9_289.2	ANGT_HUMAN	0.661
Transition	Protein	AUC
DLHLSDVFLK_396.2_260.2	CO6_HUMAN	0.661
DLHLSDVFLK_396.2_366.2	CO6_HUMAN	0.661
TAVTANLDIR_537.3_802.4	CHL1_HUMAN	0.661
DADPDTFFAK_563.8_302.1	AFAM_HUMAN	0.658
DPTFIPAPIQAK433.2_461.2	ANGT_HUMAN	0.658
FAFNLYR_465.8_712.4	HEP2_HUMAN	0.658
IALGGLLFPASNLR_481.3_412.3	SHBG_HUMAN	0.658
IAQYYYTFK_598.8_395.2	F13B_HUMAN	0.658
LPNNVLQEK_527.8_730.4	AFAM_HUMAN	0.658
SLDFTELDVAAEK_719.4_874.5	ANGT_HUMAN	0.658
VELAPLPSWQPVGK_760.9_400.3	ICAM1_HUMAN	0.658
DIIKPDPPK_511.8_342.2	IL12B_HUMAN	0.655
EVFSKPISWEELLQ_852.9_376.2	FA40A_HUMAN	0.655
LSETNR_360.2_330.2	PSG1_HUMAN	0.655
NEIWYR_440.7_357.2	FA12_HUMAN	0.655
SFRPFVPR_335.9_635.3	LBP_HUMAN	0.655
SGAQATWTELPWPHEK_613.3_	HEMO_HUMAN	0.655
793.4
TGAQELLR_444.3_530.3	GELS_HUMAN	0.655
VSEADSSNADWVTK_754.9_	CFAB_HUMAN	0.655
533.3
VVGGLVALR_442.3_685.4	FA12_HUMAN	0.655
DISEVVTPR_508.3_787.4	CFAB_HUMAN	0.652
IHPSYTNYR_575.8_598.3	PSG2_HUMAN	0.652
VSFSSPLVAISGVALR_802.0_	PAPP1_HUMAN	0.652
602.4
YNQLLR_403.7_529.3	ENOA_HUMAN	0.652
ALQDQLVLVAAK_634.9_956.6	ANGT_HUMAN	0.650
IHPSYTNYR_575.8_813.4	PSG2_HUMAN	0.650
TFLTVYWTPER_706.9_401.2	ICAM1_HUMAN	0.650
VQEVLLK_414.8_601.4	HYOU1_HUMAN	0.650
GDTYPAELYITGSILR_885.0_274.1	F13B_HUMAN	0.647
GVTGYFTFNLYLK_508.3_260.2	PSG5_HUMAN	0.647
SLDFTELDVAAEK_719.4_316.2	ANGT_HUMAN	0.647
VVLSSGSGPGLDLPLVLGLPL-	SHBG_HUMAN	0.647
QLK_791.5_598.4
YEFLNGR_449.7_293.1	PLMN_HUMAN	0.647
AQPVQVAEGSEPDGFWEALGG-	GELS_HUMAN	0.644
K_758.0_623.4
FLNWIK_410.7_561.3	HABP2_HUMAN	0.644
IAPQLSTEELVSLGEK_857.5_	AFAM_HUMAN	0.644
533.3
NTVISVNPSTK_580.3_732.4	VCAM1_HUMAN	0.644
SFEGLGQLEVLTLDHNQLQEV-	ALS_HUMAN	0.644
K_833.1_503.3
TFLTVYWTPER_706.9_502.3	ICAM1_HUMAN	0.644
AGFAGDDAPR_488.7_701.3	ACTB_HUMAN	0.641
AIGLPEELIQK_605.86_355.2	FABPL_HUMAN	0.641
DISEVVTPR_508.3_472.3	CFAB_HUMAN	0.641
DPTFIPAPIQAK_433.2_556.3	ANGT_HUMAN	0.641
ENPAVIDFELAPIVDLVR_670.7_	CO6_HUMAN	0.641
811.5
FAFNLYR_465.8_565.3	HEP2_HUMAN	0.641
IAPQLSTEELVSLGEK_857.5_333.2	AFAM_HUMAN	0.641
TNTNEFLIDVDK_704.85_849.5	TF_HUMAN	0.639
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	0.638
791.8_883.0
LDGSTHLNIFFAK_488.3_739.4	PAPP1_HUMAN	0.638
LPDTPQGLLGEAR_683.87_940.5	EGLN_HUMAN	0.638
VVLSSGSGPGLDLPLVLGLPL-	SHBG_HUMAN	0.638
QLK_791.5_768.5
ALALPPLGLAPLLNLWAKPQ-	SHBG_HUMAN	0.635
GR_770.5_457.3
LPNNVLQEK_527.8_844.5	AFAM_HUMAN	0.635
QINSYVK_426.2_496.3	CBG_HUMAN	0.635
QINSYVK_426.2_610.3	CBG_HUMAN	0.635
TGAQELLR_444.3_658.4	GELS_HUMAN	0.635
TLEAQLTPR_514.8_685.4	HEP2_HUMAN	0.635
WILTAAHTLYPK_471.9_621.4	C1R_HUMAN	0.635
SEPRPGVLLR_375.2_454.3	FA7_HUMAN	0.632
AGFAGDDAPR_488.7_630.3	ACTB_HUMAN	0.632
DFNQFSSGEK_386.8_333.2	FETA_HUMAN	0.632
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	0.632
791.8_310.2
NKPGVYTDVAYYLAWIR_	FA12_HUMAN	0.632
677.0_545.3
SEYGAALAWEK_612.8_788.4	CO6_HUMAN	0.632
YNSQLLSFVR_613.8_734.5	TFR1_HUMAN	0.632
ALVLELAK_428.8_672.4	INHBE_HUMAN	0.630
ENPAVIDFELAPIVDLVR_	CO6_HUMAN	0.630
670.7_601.4
NNQLVAGYLQGPNVNLEE-	IL1RA_HUMAN	0.630
K_700.7_999.5
WGAAPYR_410.7_577.3	PGRP2_HUMAN	0.630
HELTDEELQSLFTNFANV-	AFAM_HUMAN	0.627
VDK_817.1_854.4
AKPALEDLR_506.8_288.2	AP0A1_HUMAN	0.624
AVLHIGEK_289.5_348.7	THBG_HUMAN	0.624
EDTPNSVWEPAK_686.8_630.3	C1S_HUMAN	0.624
SPELQAEAK_486.8_788.4	APOA2_HUMAN	0.624
YENYTSSFFIR_713.8_756.4	IL12B_HUMAN	0.624
NEIVFPAGILQAPFYTR_968.5_	ECE1_HUMAN	0.621
456.2
TAVTANLDIR_537.3_288.2	CHL1_HUMAN	0.621
WWGGQPLWITATK_772.4_373.2	ENPP2_HUMAN	0.621
AVDIPGLEAATPYR_736.9_399.2	TENA_HUMAN	0.618
ALNFGGIGVVVGHELTHAFDD-	ECE1_HUMAN	0.618
QGR_837.1_360.2
ALNHLPLEYNSALYSR_621.0_	CO6_HUMAN	0.618
696.4
FNAVLTNPQGDYDTSTGK_	C1QC_HUMAN	0.618
964.5_262.1
GDTYPAELYITGSILR_885.0_922.5	F13B_HUMAN	0.618
IAQYYYTFK_598.8_884.4	F13B_HUMAN	0.618
LEQGENVFLQATDK_796.4_822.4	C1QB_HUMAN	0.618
LSITGTYDLK_555.8_696.4	A1AT_HUMAN	0.618
NTVISVNPSTK_580.3_845.5	VCAM1_HUMAN	0.618
TLAFVR_353.7_492.3	FA7_HUMAN	0.618
TLEAQLTPR_514.8_814.4	HEP2_HUMAN	0.618
TQIDSPLSGK_523.3_703.4	VCAM1_HUMAN	0.618
AVLHIGEK_289.5_292.2	THBG_HUMAN	0.615
FLIPNASQAESK_652.8_931.4	1433Z_HUMAN	0.615
FNAVLTNPQGDYDTSTGK_964.5_	C1QC_HUMAN	0.615
333.2
FQSVFTVTR_542.8_722.4	C1QC_HUMAN	0.615
INPASLDK_429.2_630.4	C163A_HUMAN	0.615
IPKPEASFSPR_410.2_506.3	ITIH4_HUMAN	0.615
ITQDAQLK_458.8_803.4	CBG_HUMAN	0.615
TSYQVYSK_488.2_397.2	C163A_HUMAN	0.615
WGAAPYR_410.7_634.3	PGRP2_HUMAN	0.615
AVDIPGLEAATPYR_736.9_286.1	TENA_HUMAN	0.613
DVLLLVHNLPQNLPGYFWYK_	PSG9_HUMAN	0.613
810.4_328.2
SFEGLGQLEVLTLDHNQLQE-	ALS_HUMAN	0.613
VK_833.1_662.8
TASDFITK_441.7_710.4	GELS_HUMAN	0.613
AGPLQAR_356.7_584.4	DEF4_HUMAN	0.610
DYWSTVK_449.7_347.2	APOC3_HUMAN	0.610
FQSVFTVTR_542.79_623.4	C1QC_HUMAN	0.610
FQSVFTVTR_542.79_722.4	C1QC_HUMAN	0.610
SYTITGLQPGTDYK_772.4_352.2	FINC_HUMAN	0.610
FQLSETNR_497.8_476.3	PSG2_HUMAN	0.607
IPKPEASFSPR_410.2_359.2	ITIH4_HUMAN	0.607
LIEIANHVDK_384.6_498.3	ADA12_HUMAN	0.607
SILFLGK_389.2_201.1	THBG_HUMAN	0.607
SLLQPNK_400.2_358.2	CO8A_HUMAN	0.607
VFQFLEK_455.8_811.4	COS_HUMAN	0.607
VPGLYYFTYHASSR_554.3_720.3	C1QB_HUMAN	0.607
VSAPSGTGHLPGLNPL_506.3_860.5	PSG3_HUMAN	0.607
AGITIPR_364.2_486.3	IL17_HUMAN	0.604
FLIPNASQAESK_652.8_261.2	1433Z_HUMAN	0.604
FQSVFTVTR_542.8_623.4	C1QC_HUMAN	0.604
IRPFFPQQ_516.79_661.4	FIBB_HUMAN	0.604
LLELTGPK_435.8_644.4	A1BG_HUMAN	0.604
SETEIHQGFQHLHQLFAK_717.4_	CBG_HUMAN	0.604
318.1
SILFLGK_389.2_577.4	THBG_HUMAN	0.604
STLFVPR_410.2_518.3	PEPD_HUMAN	0.604
TEQAAVAR_423.2_487.3	FA12_HUMAN	0.604
EDTPNSVWEPAK_686.8_315.2	C1S_HUMAN	0.601
FLNWIK_410.7_560.3	HABP2_HUMAN	0.601
ITQDAQLK_458.8_702.4	CBG_HUMAN	0.601
SPELQAEAK_486.8_659.4	APOA2_HUMAN	0.601
TLLPVSKPEIR_418.3_288.2	COS_HUMAN	0.601
VFQFLEK_455.8_276.2	COS_HUMAN	0.601
YGLVTYATYPK_638.3_843.4	CFAB_HUMAN	0.601

TABLE 14
Univariate AUC values early-middle combined windows
Transition	Protein	AUC
LDFHFSSDR_375.2_611.3	INHBC_HUMAN	0.809
ETLLQDFR_511.3_565.3	AMBP_HUMAN	0.802
HHGPTITAK_321.2_275.1	AMBP_HUMAN	0.801
ATVVYQGER_511.8_652.3	APOH_HUMAN	0.799
ETLLQDFR_511.3_322.2	AMBP_HUMAN	0.796
ATVVYQGER_511.8_751.4	APOH_HUMAN	0.795
HHGPTITAK_321.2_432.3	AMBP_HUMAN	0.794
TVQAVLTVPK_528.3_855.5	PEDF_HUMAN	0.791
AHYDLR_387.7_566.3	FETUA_HUMAN	0.789
TVQAVLTVPK_528.3_428.3	PEDF_HUMAN	0.787
FICPLTGLWPINTLK_887.0_685.4	APOH_HUMAN	0.785
VNHVTLSQPK_374.9_244.2	B2MG_HUMAN	0.783
AHYDLR_387.7_288.2	FETUA_HUMAN	0.781
ELIEELVNITQNQK_557.6_618.3	IL13_HUMAN	0.780
FSVVYAK_407.2_381.2	FETUA_HUMAN	0.777
IQTHSTTYR_369.5_627.3	F13B_HUMAN	0.777
DTDTGALLFIGK_625.8_818.5	PEDF_HUMAN	0.774
FICPLTGLWPINTLK_887.0_756.9	APOH_HUMAN	0.773
DTDTGALLFIGK_625.8_217.1	PEDF_HUMAN	0.771
FSVVYAK_407.2_579.4	FETUA_HUMAN	0.770
IQTHSTTYR_369.5_540.3	F13B_HUMAN	0.769
LDFHFSSDR_375.2_464.2	INHBC_HUMAN	0.769
TLAFVR_353.7_274.2	FA7_HUMAN	0.769
FLYHK_354.2_447.2	AMBP_HUMAN	0.766
VNHVTLSQPK_374.9_459.3	B2MG_HUMAN	0.762
AIGLPEELIQK_605.86_856.5	FABPL_HUMAN	0.752
FLYHK_354.2_284.2	AMBP_HUMAN	0.752
ELIEELVNITQNQK_557.6_517.3	IL13_HUMAN	0.751
ETPEGAEAKPWYEPIYLGGVFQ-	TNFA_HUMAN	0.751
LEK_951.14_877.5
HFQNLGK_422.2_527.2	AFAM_HUMAN	0.749
LIQDAVTGLTVNGQITGDK_	ITIH3_HUMAN	0.749
972.0_640.4
LIQDAVTGLTVNGQITGDK_	ITIH3_HUMAN	0.747
972.0_798.4
IAPQLSTEELVSLGEK_857.5_	AFAM_HUMAN	0.745
533.3
HFQNLGK_422.2_285.1	AFAM_HUMAN	0.740
NNQLVAGYLQGPNVNLEEK_	IL1RA_HUMAN	0.738
700.7_999.5
VVESLAK_373.2_646.4	IBM_HUMAN	0.738
IAPQLSTEELVSLGEK_857.5_	AFAM_HUMAN	0.737
333.2
IALGGLLFPASNLR_481.3_	SHBG_HUMAN	0.734
657.4
ALALPPLGLAPLLNLWAKPQGR_	SHBG_HUMAN	0.731
770.5_256.2
ELPQSIVYK_538.8_417.7	FBLN3_HUMAN	0.724
TFLTVYWTPER_706.9_401.2	ICAM1_HUMAN	0.723
GVTGYFTFNLYLK_508.3_260.2	PSG5_HUMAN	0.717
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	0.716
791.8_310.2
WNFAYWAAHQPWSR_607.3_545.3	PRG2_HUMAN	0.716
YTTEIIK_434.2_603.4	C1R_HUMAN	0.716
YTTEIIK_434.2_704.4	C1R_HUMAN	0.716
DIPHWLNPTR_416.9_600.3	PAPP1_HUMAN	0.715
WNFAYWAAHQPWSR_607.3_673.3	PRG2_HUMAN	0.715
IALGGLLFPASNLR_481.3_412.3	SHBG_HUMAN	0.713
VVLSSGSGPGLDLPLVLGLPLQ-	SHBG_HUMAN	0.713
LK_791.5_598.4
GFQALGDAADIR_617.3_717.4	TIMP1_HUMAN	0.711
VVLSSGSGPGLDLPLVLGLPL-	SHBG_HUMAN	0.711
QLK_791.5_768.5
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	0.708
791.8_883.0
YGIEEHGK_311.5_599.3	CXA1_HUMAN	0.706
AEHPTWGDEQLFQTTR_639.3_765.4	PGH1_HUMAN	0.705
VVESLAK_373.2_547.3	IBM_HUMAN	0.705
DADPDTFFAK_563.8_825.4	AFAM_HUMAN	0.704
DAQYAPGYDK_564.3_813.4	CFAB_HUMAN	0.704
GFQALGDAADIR_617.3_288.2	TIMP1_HUMAN	0.704
AEHPTWGDEQLFQTTR_639.3_569.3	PGH1_HUMAN	0.702
NFPSPVDAAFR_610.8_959.5	HEMO_HUMAN	0.702
ALALPPLGLAPLLNLWAKPQ-	SHBG_HUMAN	0.701
GR_770.5_457.3
GVTGYFTFNLYLK_508.3_683.9	PSG5_HUMAN	0.701
DFNQFSSGEK_386.8_189.1	FETA_HUMAN	0.699
GDTYPAELYITGSILR_885.0_274.1	F13B_HUMAN	0.699
TLEAQLTPR_514.8_685.4	HEP2_HUMAN	0.699
VEHSDLSFSK_383.5_468.2	B2MG_HUMAN	0.699
DAQYAPGYDK_564.3_315.1	CFAB_HUMAN	0.698
VSEADSSNADWVTK_754.9_347.2	CFAB_HUMAN	0.698
ILPSVPK_377.2_244.2	PGH1_HUMAN	0.695
DADPDTFFAK_563.8_302.1	AFAM_HUMAN	0.694
EVFSKPISWEELLQ_852.9_260.2	FA40A_HUMAN	0.694
HTLNQIDEVK_598.8_958.5	FETUA_HUMAN	0.694
NFPSPVDAAFR_610.8_775.4	HEMO_HUMAN	0.694
VSFSSPLVAISGVALR_802.0_715.4	PAPP1_HUMAN	0.694
TLAFVR_353.7_492.3	FA7_HUMAN	0.693
ILPSVPK_377.2_227.2	PGH1_HUMAN	0.691
LLEVPEGR_456.8_356.2	C1S_HUMAN	0.691
TLEAQLTPR_514.8_814.4	HEP2_HUMAN	0.691
IPSNPSHR_303.2_610.3	FBLN3_HUMAN	0.690
LPNNVLQEK_527.8_730.4	AFAM_HUMAN	0.690
NCSFSIIYPVVIK_770.4_555.4	CRHBP_HUMAN	0.690
NCSFSIIYPVVIK_770.4_831.5	CRHBP_HUMAN	0.690
VEHSDLSFSK_383.5_234.1	B2MG_HUMAN	0.690
ALDLSLK_380.2_185.1	ITIH3_HUMAN	0.688
IHWESASLLR_606.3_437.2	CO3_HUMAN	0.688
IPSNPSHR_303.2_496.3	FBLN3_HUMAN	0.688
LDGSTHLNIFFAK_488.3_852.5	PAPP1_HUMAN	0.687
QGHNSVFLIK_381.6_260.2	HEMO_HUMAN	0.687
AVLHIGEK_289.5_348.7	THBG_HUMAN	0.686
VSEADSSNADWVTK_754.9_533.3	CFAB_HUMAN	0.686
TNTNEFLIDVDK_704.85_849.5	TF_HUMAN	0.685
AVLHIGEK_289.5_292.2	THBG_HUMAN	0.683
HTLNQIDEVK_598.8_951.5	FETUA_HUMAN	0.683
VSFSSPLVAISGVALR_802.0_602.4	PAPP1_HUMAN	0.683
IAQYYYTFK_598.8_395.2	F13B_HUMAN	0.681
ALDLSLK_380.2_575.3	ITIH3_HUMAN	0.680
LLEVPEGR_456.8_686.4	C1S_HUMAN	0.680
QGHNSVFLIK_381.6_520.4	HEMO_HUMAN	0.680
SEPRPGVLLR_375.2_454.3	FA7_HUMAN	0.680
SFRPFVPR_335.9_272.2	LBP_HUMAN	0.680
AFQVWSDVTPLR_709.88_385.3	MMP2_HUMAN	0.679
FAFNLYR_465.8_712.4	HEP2_HUMAN	0.679
IAQYYYTFK_598.8_884.4	F13B_HUMAN	0.679
ITGFLKPGK_320.9_429.3	LBP_HUMAN	0.679
EHSSLAFWK_552.8_838.4	APOH_HUMAN	0.677
GLQYAAQEGLLALQSELLR_	LBP_HUMAN	0.676
1037.1_858.5
YYLQGAK_421.7_327.1	ITIH4_HUMAN	0.676
LIENGYFHPVK_439.6_627.4	F13B_HUMAN	0.675
SFRPFVPR_335.9_635.3	LBP_HUMAN	0.675
AALAAFNAQNNGSNFQLEEISR_	FETUA_HUMAN	0.674
789.1_746.4
ITGFLKPGK_320.9_301.2	LBP_HUMAN	0.673
VQEVLLK_414.8_373.3	HYOU1_HUMAN	0.673
YNSQLLSFVR_613.8_508.3	TFR1_HUMAN	0.673
EHSSLAFWK_552.8_267.1	APOH_HUMAN	0.672
FAFNLYR_465.8_565.3	HEP2_HUMAN	0.672
GDTYPAELYITGSILR_885.0_922.5	F13B_HUMAN	0.672
ITLPDFTGDLR_624.3_920.5	LBP_HUMAN	0.672
NSDQEIDFK_548.3_409.2	S10A5_HUMAN	0.672
TAVTANLDIR_537.3_802.4	CHL1_HUMAN	0.672
YYLQGAK_421.7_516.3	ITIH4_HUMAN	0.672
ITLPDFTGDLR_624.3_288.2	LBP_HUMAN	0.670
AIGLPEELIQK_605.86_355.2	FABPL_HUMAN	0.669
ALNFGGIGVVVGHELTHAFDD-	ECE1_HUMAN	0.668
QGR_837.1_299.2
AQETSGEEISK_589.8_979.5	IBP1_HUMAN	0.668
LPNNVLQEK_527.8_844.5	AFAM_HUMAN	0.668
TGISPLALIK_506.8_654.5	APOB_HUMAN	0.666
DFHINLFQVLPWLK_885.5_543.3	CFAB_HUMAN	0.665
VQEVLLK_414.8_601.4	HYOU1_HUMAN	0.665
YENYTSSFFIR_713.8_756.4	IL12B_HUMAN	0.665
CRPINATLAVEK_457.9_559.3	CGB1_HUMAN	0.663
LDGSTHLNIFFAK_488.3_739.4	PAPP1_HUMAN	0.663
TGISPLALIK_506.8_741.5	APOB_HUMAN	0.663
EVFSKPISWEELLQ_852.9_376.2	FA40A_HUMAN	0.662
SLDFTELDVAAEK_719.4_874.5	ANGT_HUMAN	0.662
TFLTVYWTPER_706.9_502.3	ICAM1_HUMAN	0.662
VRPQQLVK_484.3_609.4	ITIH4_HUMAN	0.662
GLQYAAQEGLLALQSELLR_	LBP_HUMAN	0.661
1037.1_929.5
NAVVQGLEQPHGLVVHPLR_	LRP1_HUMAN	0.661
688.4_890.6
SILFLGK_389.2_201.1	THBG_HUMAN	0.661
DFNQFSSGEK_386.8_333.2	FETA_HUMAN	0.659
IHWESASLLR_606.3_251.2	CO3_HUMAN	0.659
SILFLGK_389.2_577.4	THBG_HUMAN	0.658
SVSLPSLDPASAK_636.4_473.3	APOB_HUMAN	0.658
WWGGQPLWITATK_772.4_929.5	ENPP2_HUMAN	0.658
LNIGYIEDLK_589.3_950.5	PAI2_HUMAN	0.657
DFHINLFQVLPWLK_885.5_400.2	CFAB_HUMAN	0.657
YSHYNER_323.48_418.2	HABP2_HUMAN	0.657
STLFVPR_410.2_272.2	PEPD_HUMAN	0.656
AFQVWSDVTPLR_709.88_347.2	MMP2_HUMAN	0.655
FQSVFTVTR_542.8_722.4	C1QC_HUMAN	0.655
GPGEDFR_389.2_623.3	PTGDS_HUMAN	0.655
LEEHYELR_363.5_288.2	PAI2_HUMAN	0.655
LPDTPQGLLGEAR_683.87_427.2	EGLN_HUMAN	0.655
FQSVFTVTR_542.79_722.4	C1QC_HUMAN	0.654
FTFTLHLETPKPSISSSNLNPR_	PSG1_HUMAN	0.654
829.4_787.4
NHYTESISVAK_624.8_252.1	NEUR1_HUMAN	0.654
YSHYNER_323.48_581.3	HABP2_HUMAN	0.654
FQSVFTVTR_542.79_623.4	C1QC_HUMAN	0.652
IEGNLIFDPNNYLPK_874.0_845.5	APOB_HUMAN	0.652
VRPQQLVK_484.3_722.4	ITIH4_HUMAN	0.652
WILTAAHTLYPK_471.9_621.4	C1R_HUMAN	0.652
ITQDAQLK_458.8_803.4	CBG_HUMAN	0.651
SVSLPSLDPASAK_636.4_885.5	APOB_HUMAN	0.651
ESDTSYVSLK_564.8_347.2	CRP_HUMAN	0.650
ESDTSYVSLK_564.8_696.4	CRP_HUMAN	0.650
FQSVFTVTR_542.8_623.4	C1QC_HUMAN	0.650
HELTDEELQSLFTNFANVVDK_	AFAM_HUMAN	0.650
817.1_854.4
IEGNLIFDPNNYLPK_874.0_414.2	APOB_HUMAN	0.650
DIIKPDPPK_511.8_342.2	IL12B_HUMAN	0.648
SPELQAEAK_486.8_788.4	APOA2_HUMAN	0.648
VELAPLPSWQPVGK_760.9_400.3	ICAM1_HUMAN	0.648
AQETSGEEISK_589.8_850.4	IBM_HUMAN	0.647
QTLSWTVTPK_580.8_545.3	PZP_HUMAN	0.647
DISEVVTPR_508.3_787.4	CFAB_HUMAN	0.645
DVLLLVHNLPQNLPGYFWYK_	PSG9_HUMAN	0.645
810.4_328.2
QTLSWTVTPK_580.8_818.4	PZP_HUMAN	0.645
SGAQATWTELPWPHEK_613.3_510.3	HEMO_HUMAN	0.645
SLDFTELDVAAEK_719.4_316.2	ANGT_HUMAN	0.645
AVGYLITGYQR_620.8_523.3	PZP_HUMAN	0.644
DISEVVTPR_508.3_472.3	CFAB_HUMAN	0.644
FLNWIK_410.7_560.3	HABP2_HUMAN	0.644
IQHPFTVEEFVLPK_562.0_861.5	PZP_HUMAN	0.644
ALQDQLVLVAAK_634.9_289.2	ANGT_HUMAN	0.643
AVGYLITGYQR_620.8_737.4	PZP_HUMAN	0.643
FLNWIK_410.7_561.3	HABP2_HUMAN	0.643
LEQGENVFLQATDK_796.4_822.4	C1QB_HUMAN	0.643
LSITGTYDLK_555.8_797.4	A1AT_HUMAN	0.641
SEPRPGVLLR_375.2_654.4	FA7_HUMAN	0.641
VPGLYYFTYHASSR_554.3_720.3	C1QB_HUMAN	0.641
APLTKPLK_289.9_357.2	CRP_HUMAN	0.639
FNAVLTNPQGDYDTSTGK_	C1QC_HUMAN	0.639
964.5_333.2
IQHPFTVEEFVLPK_562.0_603.4	PZP_HUMAN	0.639
LSSPAVITDK_515.8_743.4	PLMN_HUMAN	0.639
ALNFGGIGVVVGHELTHAFDD-	ECE1_HUMAN	0.637
QGR_837.1_360.2
FNAVLTNPQGDYDTSTGK_	C1QC_HUMAN	0.637
964.5_262.1
LLELTGPK_435.8_227.2	A1BG_HUMAN	0.637
YNSQLLSFVR_613.8_734.5	TFR1_HUMAN	0.636
DLYHYITSYVVDGEIIIYGPAYSGR_	PSG1_HUMAN	0.634
955.5_707.3
GPGEDFR_389.2_322.2	PTGDS_HUMAN	0.634
IHPSYTNYR_575.8_813.4	PSG2_HUMAN	0.634
SGAQATWTELPWPHEK_613.3_793.4	HEMO_HUMAN	0.634
SPELQAEAK_486.8_659.4	APOA2_HUMAN	0.634
ALQDQLVLVAAK_634.9_956.6	ANGT_HUMAN	0.633
ITENDIQIALDDAK_779.9_632.3	APOB_HUMAN	0.632
ITQDAQLK_458.8_702.4	CBG_HUMAN	0.632
LSSPAVITDK_515.8_830.5	PLMN_HUMAN	0.632
SLLQPNK_400.2_358.2	CO8A_HUMAN	0.632
VPGLYYFTYHASSR_554.3_420.2	C1QB_HUMAN	0.632
YGLVTYATYPK_638.3_843.4	CFAB_HUMAN	0.632
AGITIPR_364.2_486.3	IL17_HUMAN	0.630
IHPSYTNYR_575.8_598.3	PSG2_HUMAN	0.630
QINSYVK_426.2_610.3	CBG_HUMAN	0.630
SSNNPHSPIVEEFQVPYNK_	C1S_HUMAN	0.630
729.4_261.2
ANDQYLTAAALHNLDEAVK_	IL1A_HUMAN	0.629
686.3_317.2
ATWSGAVLAGR_544.8_730.4	A1BG_HUMAN	0.629
TLPFSR_360.7_506.3	LYAM1_HUMAN	0.629
TYLHTYESEI_628.3_515.3	ENPP2_HUMAN	0.629
EFDDDTYDNDIALLQLK_	TPA_HUMAN	0.627
1014.48_388.3
EFDDDTYDNDIALLQLK_	TPA_HUMAN	0.627
1014.48_501.3
VTGLDFIPGLHPILTLSK_641.04_771.5	LEP_HUMAN	0.627
HVVQLR_376.2_614.4	IL6RA_HUMAN	0.626
LIENGYFHPVK_439.6_343.2	F13B_HUMAN	0.626
LLELTGPK_435.8_644.4	A1BG_HUMAN	0.626
YEVQGEVFTKPQLWP_911.0_392.2	CRP_HUMAN	0.626
DPNGLPPEAQK_583.3_497.2	RET4_HUMAN	0.625
FTFTLHLETPKPSISSSNLNPR_	PSG1_HUMAN	0.625
829.4_874.4
YGLVTYATYPK_638.3_334.2	CFAB_HUMAN	0.625
APLTKPLK_289.9_398.8	CRP_HUMAN	0.623
DSPSVWAAVPGK_607.31_301.2	PROF1_HUMAN	0.623
ENPAVIDFELAPIVDLVR_670.7_811.5	CO6_HUMAN	0.623
ILILPSVTR_506.3_559.3	PSGx_HUMAN	0.623
SFEGLGQLEVLTLDHNQLQEVK_	ALS_HUMAN	0.623
833.1_503.3
TSESGELHGLTTEEEFVEGIYK_	TTHY_HUMAN	0.623
819.06_310.2
AGITIPR_364.2_272.2	IL17_HUMAN	0.622
DPDQTDGLGLSYLSSHIANVER_	GELS_HUMAN	0.622
796.4_328.1
ATWSGAVLAGR_544.8_643.4	A1BG_HUMAN	0.620
HVVQLR_376.2_515.3	IL6RA_HUMAN	0.620
QINSYVK_426.2_496.3	CBG_HUMAN	0.620
TLFIFGVTK_513.3_215.1	PSG4_HUMAN	0.620
YEVQGEVFTKPQLWP_911.0_293.1	CRP_HUMAN	0.620
YYGYTGAFR_549.3_771.4	TRFL_HUMAN	0.620
AALAAFNAQNNGSNFQLEEISR_	FETUA_HUMAN	0.619
789.1_633.3
ALNHLPLEYNSALYSR_621.0_696.4	CO6_HUMAN	0.619
EDTPNSVWEPAK_686.8_630.3	C1S_HUMAN	0.619
NNQLVAGYLQGPNVNLEEK_	LIRA_HUMAN	0.619
700.7_357.2
ELANTIK_394.7_475.3	S10AC_HUMAN	0.618
ENPAVIDFELAPIVDLVR_670.7_601.4	CO6_HUMAN	0.618
GEVTYTTSQVSK_650.3_913.5	EGLN_HUMAN	0.616
NEIWYR_440.7_637.4	FA12_HUMAN	0.616
TLFIFGVTK_513.3_811.5	PSG4_HUMAN	0.616
DLYHYITSYVVDGEIIIYGPAYS-	PSG1_HUMAN	0.615
GR_955.5_650.3
DPTFIPAPIQAK_433.2_556.3	ANGT_HUMAN	0.615
VELAPLPSWQPVGK_760.9_342.2	ICAM1_HUMAN	0.615
DPNGLPPEAQK_583.3_669.4	RET4_HUMAN	0.614
GIVEECCFR_585.3_900.3	IGF2_HUMAN	0.614
ITENDIQIALDDAK_779.9_873.5	APOB_HUMAN	0.614
LSETNR_360.2_330.2	PSG1_HUMAN	0.614
LSNENHGIAQR_413.5_519.8	ITIH2_HUMAN	0.614
Transition	Protein	AUC
YEFLNGR_449.7_293.1	PLMN_HUMAN	0.614
AEIEYLEK_497.8_552.3	LYAM1_HUMAN	0.612
GIVEECCFR_585.3_771.3	IGF2_HUMAN	0.612
ILDDLSPR_464.8_587.3	ITIH4_HUMAN	0.611
IRPHTFTGLSGLR_485.6_545.3	ALS_HUMAN	0.611
VVGGLVALR_442.3_784.5	FA12_HUMAN	0.609
LEEHYELR_363.5_417.2	PAI2_HUMAN	0.609
LSNENHGIAQR_413.5_544.3	ITIH2_HUMAN	0.609
TYLHTYESEI_628.3_908.4	ENPP2_HUMAN	0.609
VLEPTLK_400.3_587.3	VTDB_HUMAN	0.609
ILILPSVTR_506.3_785.5	PSGx_HUMAN	0.608
TAVTANLDIR_537.3_288.2	CHL1_HUMAN	0.608
WWGGQPLWITATK_772.4_373.2	ENPP2_HUMAN	0.607
ALVLELAK_428.8_672.4	INHBE_HUMAN	0.605
EAQLPVIENK_570.8_329.2	PLMN_HUMAN	0.605
QRPPDLDTSSNAVDLLFFTDES-	C1R_HUMAN	0.605
GDSR_961.5_866.3
TDAPDLPEENQAR_728.3_613.3	COS_HUMAN	0.605
TLPFSR_360.7_409.2	LYAM1_HUMAN	0.605
VQTAHFK_277.5_502.3	CO8A_HUMAN	0.605
ANLINNIFELAGLGK_793.9_299.2	LCAP_HUMAN	0.604
FQLPGQK_409.2_275.1	PSG1_HUMAN	0.604
NTVISVNPSTK_580.3_845.5	VCAM1_HUMAN	0.604
VLEPTLK_400.3_458.3	VTDB_HUMAN	0.604
YWGVASFLQK_599.8_849.5	RET4_HUMAN	0.604
AGPLQAR_356.7_584.4	DEF4_HUMAN	0.602
AHQLAIDTYQEFEETYIPK_	CSH_HUMAN	0.602
766.0_521.3
DLHLSDVFLK_396.2_366.2	CO6_HUMAN	0.602
SSNNPHSPIVEEFQVPYNK_	C1S_HUMAN	0.602
729.4_521.3
YWGVASFLQK_599.8_350.2	RET4_HUMAN	0.602
AGPLQAR_356.7_487.3	DEF4_HUMAN	0.601
ALNHLPLEYNSALYSR_621.0_538.3	CO6_HUMAN	0.601
EAQLPVIENK_570.8_699.4	PLMN_HUMAN	0.601
EDTPNSVWEPAK_686.8_315.2	C1S_HUMAN	0.601
NTVISVNPSTK_580.3_732.4	VCAM1_HUMAN	0.601

TABLE 15
Univariate AUC values middle-late combined windows
Transition	Protein	AUC
GDTYPAELYITGSILR_885.0_	F13B_HUMAN	0.7750
274.1
TVQAVLTVPK_528.3_428.3	PEDF_HUMAN	0.7667
IQTHSTTYR_369.5_627.3	F13B_HUMAN	0.7667
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	0.7667
791.8_310.2
IQTHSTTYR_369.5_540.3	F13B_HUMAN	0.7646
ALALPPLGLAPLLNLWAKPQGR_	SHBG_HUMAN	0.7646
770.5_256.2
VVLSSGSGPGLDLPLVLGLPLQLK_	SHBG_HUMAN	0.7625
791.5_768.5
VVLSSGSGPGLDLPLVLGLPLQLK_	SHBG_HUMAN	0.7625
791.5_598.4
TVQAVLTVPK_528.3_855.5	PEDF_HUMAN	0.7604
GDTYPAELYITGSILR_885.0_922.5	F13B_HUMAN	0.7604
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	0.7604
791.8_883.0
TLPFSR_360.7_506.3	LYAM1_HUMAN	0.7563
ALALPPLGLAPLLNLWAKPQGR_	SHBG_HUMAN	0.7563
770.5_457.3
IALGGLLFPASNLR_481.3_657.4	SHBG_HUMAN	0.7542
IALGGLLFPASNLR_481.3_412.3	SHBG_HUMAN	0.7542
DTDTGALLFIGK_625.8_217.1	PEDF_HUMAN	0.7500
QGFGNVATNTDGK_654.81_	FIBB_HUMAN	0.7438
706.3
ETLLQDFR_511.3_565.3	AMBP_HUMAN	0.7438
ETLLQDFR_511.3_322.2	AMBP_HUMAN	0.7417
IAQYYYTFK_598.8_884.4	F13B_HUMAN	0.7396
DTDTGALLFIGK_625.8_818.5	PEDF_HUMAN	0.7396
AEIEYLEK_497.8_552.3	LYAM1_HUMAN	0.7396
LDFHFSSDR_375.2_611.3	INHBC_HUMAN	0.7354
YQISVNK_426.2_560.3	FIBB_HUMAN	0.7333
IAPQLSTEELVSLGEK_857.5_	AFAM_HUMAN	0.7313
533.3
EVFSKPISWEELLQ_852.9_	FA40A_HUMAN	0.7292
376.2
TLAFVR_353.7_274.2	FA7_HUMAN	0.7229
HHGPTITAK_321.2_275.1	AMBP_HUMAN	0.7229
SLQAFVAVAAR_566.8_487.3	IL23A_HUMAN	0.7208
IAQYYYTFK_598.8_395.2	F13B_HUMAN	0.7208
EVFSKPISWEELLQ_852.9_260.2	FA40A_HUMAN	0.7208
DPNGLPPEAQK_583.3_669.4	RET4_HUMAN	0.7208
DPNGLPPEAQK_583.3_497.2	RET4_HUMAN	0.7167
VEHSDLSFSK_383.5_468.2	B2MG_HUMAN	0.7146
YQISVNK_426.2_292.1	FIBB_HUMAN	0.7125
TLAFVR_353.7_492.3	FA7_HUMAN	0.7125
IAPQLSTEELVSLGEK_857.5_	AFAM_HUMAN	0.7125
333.2
AEIEYLEK_497.8_389.2	LYAM1_HUMAN	0.7125
YWGVASFLQK_599.8_849.5	RET4_HUMAN	0.7104
TLPFSR_360.7_409.2	LYAM1_HUMAN	0.7104
HFQNLGK_422.2_527.2	AFAM_HUMAN	0.7104
TQILEWAAER_608.8_761.4	EGLN_HUMAN	0.7083
HFQNLGK_422.2_285.1	AFAM_HUMAN	0.7063
FTFTLHLETPKPSISSSNLNPR_	PSG1_HUMAN	0.7063
829.4_787.4
DPDQTDGLGLSYLSSHIANVER_	GELS_HUMAN	0.7063
796.4_456.2
DADPDTFFAK_563.8_825.4	AFAM_HUMAN	0.7042
YWGVASFLQK_599.8_350.2	RET4_HUMAN	0.7021
DADPDTFFAK_563.8_302.1	AFAM_HUMAN	0.7021
HHGPTITAK_321.2_432.3	AMBP_HUMAN	0.6979
NTVISVNPSTK_580.3_845.5	VCAM1_HUMAN	0.6958
FLYHK_354.2_447.2	AMBP_HUMAN	0.6958
FICPLTGLWPINTLK_887.0_	APOH_HUMAN	0.6958
685.4
FTFTLHLETPKPSISSSNLNPR_	PSG1_HUMAN	0.6938
829.4_874.4
FLYHK_354.2_284.2	AMBP_HUMAN	0.6938
EALVPLVADHK_397.9_390.2	HGFA_HUMAN	0.6938
LNIGYIEDLK_589.3_837.4	PAI2_HUMAN	0.6917
QGFGNVATNTDGK_654.81_319.2	FIBB_HUMAN	0.6896
EALVPLVADHK_397.9_439.8	HGFA_HUMAN	0.6896
TNTNEFLIDVDK_704.85_849.5	TF_HUMAN	0.6875
DTYVSSFPR_357.8_272.2	TCEA1_HUMAN	0.6813
VNHVTLSQPK_374.9_244.2	B2MG_HUMAN	0.6771
GPGEDFR_389.2_623.3	PTGDS_HUMAN	0.6771
GEVTYTTSQVSK_650.3_913.5	EGLN_HUMAN	0.6771
GEVTYTTSQVSK_650.3_750.4	EGLN_HUMAN	0.6771
FICPLTGLWPINTLK_887.0_756.9	APOH_HUMAN	0.6771
YEFLNGR_449.7_606.3	PLMN_HUMAN	0.6750
YEFLNGR_449.7_293.1	PLMN_HUMAN	0.6750
TLFIFGVTK_513.3_215.1	PSG4_HUMAN	0.6750
LNIGYIEDLK_589.3_950.5	PAI2_HUMAN	0.6750
LLELTGPK_435.8_227.2	A1BG_HUMAN	0.6750
TPSAAYLWVGTGASEAEK_	GELS_HUMAN	0.6729
919.5_849.4
FQLPGQK_409.2_275.1	PSG1_HUMAN	0.6729
ELIEELVNITQNQK_557.6_	IL13_HUMAN	0.6729
618.3
DLYHYITSYVVDGEIIIYGPA-	PSG1_HUMAN	0.6729
YSGR_955.5_707.3
AHYDLR_387.7_566.3	FETUA_HUMAN	0.6729
LLEVPEGR_456.8_356.2	C1S_HUMAN	0.6708
TLFIFGVTK_513.3_811.5	PSG4_HUMAN	0.6688
FQLPGQK_409.2_429.2	PSG1_HUMAN	0.6667
DLYHYITSYVVDGEIIIYGPA-	PSG1_HUMAN	0.6667
YSGR_955.5_650.3
YYLQGAK_421.7_516.3	ITIH4_HUMAN	0.6646
FSVVYAK_407.2_579.4	FETUA_HUMAN	0.6646
EQLGEFYEALDCLR_871.9_	A1AG1_HUMAN	0.6646
747.4
LDFHFSSDR_375.2_464.2	INHBC_HUMAN	0.6625
ALNHLPLEYNSALYSR_621.0_	CO6_HUMAN	0.6625
696.4
YYLQGAK_421.7_327.1	ITIH4_HUMAN	0.6604
YTTEIIK_434.2_704.4	C1R_HUMAN	0.6604
VEHSDLSFSK_383.5_234.1	B2MG_HUMAN	0.6604
SNPVTLNVLYGPDLPR_585.7_	PSG6_HUMAN	0.6604
654.4
LWAYLTIQELLAK_781.5_300.2	ITIH1_HUMAN	0.6604
FSLVSGWGQLLDR_493.3_403.2	FA7_HUMAN	0.6604
ATVVYQGER_511.8_652.3	APOH_HUMAN	0.6604
TPSAAYLWVGTGASEAEK_919.5_	GELS_HUMAN	0.6583
428.2
SEPRPGVLLR_375.2_454.3	FA7_HUMAN	0.6583
LSSPAVITDK_515.8_830.5	PLMN_HUMAN	0.6583
GPGEDFR_389.2_322.2	PTGDS_HUMAN	0.6583
EFDDDTYDNDIALLQLK_1014.48_	TPA_HUMAN	0.6583
501.3
TFLTVYWTPER_706.9_502.3	ICAM1_HUMAN	0.6563
NTVISVNPSTK_580.3_732.4	VCAM1_HUMAN	0.6563
LPNNVLQEK_527.8_730.4	AFAM_HUMAN	0.6563
LPDTPQGLLGEAR_683.8_7427.2	EGLN_HUMAN	0.6563
VANYVDWINDR_682.8_818.4	HGFA_HUMAN	0.6542
LSSPAVITDK_515.8_743.4	PLMN_HUMAN	0.6542
LPNNVLQEK_527.8_844.5	AFAM_HUMAN	0.6542
IPGIFELGISSQSDR_809.9_849.4	CO8B_HUMAN	0.6542
GAVHVVVAETDYQSFAVLYLER_	CO8G_HUMAN	0.6542
822.8_580.3
FLNWIK_410.7_560.3	HABP2_HUMAN	0.6542
TFLTVYWTPER_706.9_401.2	ICAM1_HUMAN	0.6521
NKPGVYTDVAYYLAWIR_677.0_	FA12_HUMAN	0.6521
821.5
AHYDLR_387.7_288.2	FETUA_HUMAN	0.6521
LLEVPEGR_456.8_686.4	C1S_HUMAN	0.6500
LIENGYFHPVK_439.6_627.4	F13B_HUMAN	0.6500
GFQALGDAADIR_617.3_717.4	TIMP1_HUMAN	0.6500
ELIEELVNITQNQK_557.6_517.3	IL13_HUMAN	0.6500
EAQLPVIENK_570.8_329.2	PLMN_HUMAN	0.6479
CRPINATLAVEK_457.9_559.3	CGB1_HUMAN	0.6479
ATVVYQGER_511.8_751.4	APOH_HUMAN	0.6479
ALNHLPLEYNSALYSR_621.0_538.3	CO6_HUMAN	0.6479
AHQLAIDTYQEFEETYIPK_766.0_	CSH_HUMAN	0.6479
634.4
VTGLDFIPGLHPILTLSK_641.04_	LEP_HUMAN	0.6458
771.5
VANYVDWINDR_682.8_917.4	HGFA_HUMAN	0.6458
SSNNPHSPIVEEFQVPYNK_	C1S_HUMAN	0.6458
729.4_261.2
NKPGVYTDVAYYLAWIR_677.0_	FA12_HUMAN	0.6458
545.3
GSLVQASEANLQAAQDFVR_	ITIH1_HUMAN	0.6458
668.7_735.4
YTTEIIK_434.2_603.4	C1R_HUMAN	0.6438
NEIVFPAGILQAPFYTR_968.5_357.2	ECE1_HUMAN	0.6438
IPGIFELGISSQSDR_809.9_679.3	CO8B_HUMAN	0.6438
SNPVTLNVLYGPDLPR_585.7_817.4	PSG6_HUMAN	0.6417
LLELTGPK_435.8_644.4	A1BG_HUMAN	0.6417
EAQLPVIENK_570.8_699.4	PLMN_HUMAN	0.6417
AEHPTWGDEQLFQTTR_639.3_	PGH1_HUMAN	0.6417
765.4
YGIEEHGK_311.5_599.3	CXA1_HUMAN	0.6396
TQIDSPLSGK_523.3_703.4	VCAM1_HUMAN	0.6396
YHFEALADTGISSEFYDNANDL-	CO8A_HUMAN	0.6375
LSK_940.8_301.1
SCDLALLETYCATPAK_906.9_	IGF2_HUMAN	0.6375
315.2
NAVVQGLEQPHGLVVHPLR_	LRP1_HUMAN	0.6375
688.4_285.2
HVVQLR_376.2_614.4	IL6RA_HUMAN	0.6375
NNQLVAGYLQGPNVNLEEK_	IL1RA_HUMAN	0.6354
700.7_999.5
GIVEECCFR_585.3_771.3	IGF2_HUMAN	0.6354
DGSPDVTTADIGANTPDATK_	PGRP2_HUMAN	0.6354
973.5_531.3
AEHPTWGDEQLFQTTR_639.3_	PGH1_HUMAN	0.6354
569.3
YVVISQGLDKPR_458.9_400.3	LRP1_HUMAN	0.6333
WGAAPYR_410.7_577.3	PGRP2_HUMAN	0.6333
VRPQQLVK_484.3_609.4	ITIH4_HUMAN	0.6333
AVYEAVLR_460.8_750.4	PEPD_HUMAN	0.6333
TQIDSPLSGK_523.3_816.5	VCAM1_HUMAN	0.6313
IPKPEASFSPR_410.2_359.2	ITIH4_HUMAN	0.6313
HELTDEELQSLFTNFANVVDK_	AFAM_HUMAN	0.6313
817.1_854.4
GSLVQASEANLQAAQDFVR_	ITIH1_HUMAN	0.6313
668.7_806.4
GAVHVVVAETDYQSFAVLYLER_	CO8G_HUMAN	0.6313
822.8_863.5
ENPAVIDFELAPIVDLVR_	CO6_HUMAN	0.6313
670.7_811.5
VRPQQLVK_484.3_722.4	ITIH4_HUMAN	0.6292
IRPFFPQQ_516.79_372.2	FIBB_HUMAN	0.6292
LWAYLTIQELLAK_781.5_371.2	ITIH1_HUMAN	0.6271
EQLGEFYEALDCLR_871.9_563.3	A1AG1_HUMAN	0.6271
LLDFEFSSGR_585.8_553.3	G6PE_HUMAN	0.6250
LIENGYFHPVK_439.6_343.2	F13B_HUMAN	0.6250
ENPAVIDFELAPIVDLVR_	CO6_HUMAN	0.6250
670.7_601.4
WNFAYWAAHQPWSR_	PRG2_HUMAN	0.6229
607.3_545.3
TAVTANLDIR_537.3_802.4	CHL1_HUMAN	0.6229
WNFAYWAAHQPWSR_607.3_	PRG2_HUMAN	0.6208
673.3
HTLNQIDEVK_598.8_951.5	FETUA_HUMAN	0.6208
DPDQTDGLGLSYLSSHIANV-	GELS_HUMAN	0.6208
ER_796.4_328.1
WGAAPYR_410.7_634.3	PGRP2_HUMAN	0.6188
TEQAAVAR_423.2_487.3	FA12_HUMAN	0.6188
LEEHYELR_363.5_288.2	PAI2_HUMAN	0.6188
GIVEECCFR_585.3_900.3	IGF2_HUMAN	0.6188
YHFEALADTGISSEFYDNAND-	CO8A_HUMAN	0.6167
LLSK_940.8_874.5
TQILEWAAER_608.8_632.3	EGLN_HUMAN	0.6167
DSPSVWAAVPGK_607.31_301.2	PROF1_HUMAN	0.6167
DLHLSDVFLK_396.2_260.2	CO6_HUMAN	0.6167
AQPVQVAEGSEPDGFWEALGGK_	GELS_HUMAN	0.6167
758.0_574.3
YSHYNER_323.48_581.3	HABP2_HUMAN	0.6146
YSHYNER_323.48_418.2	HABP2_HUMAN	0.6146
VNHVTLSQPK_374.9_459.3	B2MG_HUMAN	0.6146
EHSSLAFWK_552.8_267.1	APOH_HUMAN	0.6146
TATSEYQTFFNPR_781.4_386.2	THRB_HUMAN	0.6104
SGFSFGFK_438.7_732.4	CO8B_HUMAN	0.6104
GFQALGDAADIR_617.3_288.2	TIMP1_HUMAN	0.6104
FSVVYAK_407.2_381.2	FETUA_HUMAN	0.6104
QTLSWTVTPK_580.8545.3	PZP_HUMAN	0.6083
QLGLPGPPDVPDHAAYHPF_	ITIH4_HUMAN	0.6083
676.7_263.1
LSITGTYDLK_555.8_797.4	A1AT_HUMAN	0.6083
LPDTPQGLLGEAR_683.87_	EGLN_HUMAN	0.6083
940.5
VVESLAK_373.2_646.4	IBP1_HUMAN	0.6063
VSEADSSNADWVTK_754.9_	CFAB_HUMAN	0.6063
347.2
TEQAAVAR_423.2_615.4	FA12_HUMAN	0.6063
SEPRPGVLLR_375.2_654.4	FA7_HUMAN	0.6063
QTLSWTVTPK_580.8_818.4	PZP_HUMAN	0.6063
HYINLITR_515.3_301.1	NPY_HUMAN	0.6063
DPTFIPAPIQAK_433.2_461.2	ANGT_HUMAN	0.6063
VSEADSSNADWVTK_754.9_	CFAB_HUMAN	0.6042
533.3
VQEVLLK_414.8_373.3	HYOU1_HUMAN	0.6042
SILFLGK_389.2_577.4	THBG_HUMAN	0.6042
IQHPFTVEEFVLPK_562.0_603.4	PZP_HUMAN	0.6042
ELPQSIVYK_538.8_417.7	FBLN3_HUMAN	0.6042
AVGYLITGYQR_620.8_737.4	PZP_HUMAN	0.6042
ATWSGAVLAGR_544.8_643.4	A1BG_HUMAN	0.6042
AKPALEDLR_506.8_288.2	APOA1_HUMAN	0.6042
SEYGAALAWEK_612.8_845.5	CO6_HUMAN	0.6021
NVNQSLLELHK_432.2_656.3	FRIH_HUMAN	0.6021
IQHPFTVEEFVLPK_562.0_861.5	PZP_HUMAN	0.6021
IPKPEASFSPR_410.2_506.3	ITIH4_HUMAN	0.6021
GVTGYFTFNLYLK_508.3_260.2	PSG5_HUMAN	0.6021
DGSPDVTTADIGANTPDATK_	PGRP2_HUMAN	0.6021
973.5_844.4
AVGYLITGYQR_620.8_523.3	PZP_HUMAN	0.6021
ANDQYLTAAALHNLDEAVK_	IL1A_HUMAN	0.6021
686.3_317.2
TLYSSSPR_455.7_696.3	IC1_HUMAN	0.6000
LHKPGVYTR_357.5_479.3	HGFA_HUMAN	0.6000
IIGGSDADIK_494.8_260.2	C1S_HUMAN	0.6000
HELTDEELQSLFTNFANVVDK_	AFAM_HUMAN	0.6000
817.1_906.5
GGEGTGYFVDFSVR_745.9_869.5	HRG_HUMAN	0.6000
AVLHIGEK_289.5_348.7	THBG_HUMAN	0.6000
ALVLELAK_428.8_672.4	INHBE_HUMAN	0.6000

TABLE 16
Lasso Summed Coefficients All Windows
		SumBest-
Transition	Protein	Coefs_All
TQILEWAAER_608.8_761.4	EGLN_HUMAN	26.4563
GFQALGDAADIR_617.3_717.4	TIMP1_HUMAN	17.6447
AVDIPGLEAATPYR_736.9_399.2	TENA_HUMAN	16.2270
TVQAVLTVPK_528.3_428.3	PEDF_HUMAN	15.1166
LDFHFSSDR_375.2_611.3	INHBC_HUMAN	15.0029
ATVVYQGER_511.8_652.3	APOH_HUMAN	13.2314
ETLLQDFR_511.3_565.3	AMBP_HUMAN	13.1219
GFQALGDAADIR_617.3_288.2	TIMP1_HUMAN	12.1693
IQTHSTTYR_369.5_627.3	F13B_HUMAN	9.4737
GDTYPAELYITGSILR_885.0_274.1	F13B_HUMAN	6.1820
ELPQSIVYK_538.8_417.7	FBLN3_HUMAN	6.1607
NEIVFPAGILQAPFYTR_968.5_357.2	ECE1_HUMAN	5.5493
AHYDLR_387.7_566.3	FETUA_HUMAN	5.4415
HHGPTITAK_321.2_275.1	AMBP_HUMAN	5.0751
SERPPIFEIR_415.2_564.3	LRP1_HUMAN	4.5620
ALDLSLK_380.2_185.1	ITIH3_HUMAN	4.4275
DTDTGALLFIGK_625.8_217.1	PEDF_HUMAN	4.3562
ALNHLPLEYNSALYSR_621.0_696.4	CO6_HUMAN	3.9022
ETLLQDFR_511.3_322.2	AMBP_HUMAN	3.3017
YGIEEHGK_311.5_599.3	CXA1_HUMAN	2.8410
IHWESASLLR_606.3_437.2	CO3_HUMAN	2.6618
GEVTYTTSQVSK_650.3_750.4	EGLN_HUMAN	2.5328
ELIEELVNITQNQK_557.6_618.3	IL13_HUMAN	2.5088
DLHLSDVFLK_396.2_260.2	CO6_HUMAN	2.4010
SYTITGLQPGTDYK_772.4_352.2	FINC_HUMAN	2.3304
SPELQAEAK_486.8_788.4	APOA2_HUMAN	2.2657
VNHVTLSQPK_374.9_459.3	B2MG_HUMAN	2.1480
DTDTGALLFIGK_625.8_818.5	PEDF_HUMAN	2.0051
LLDFEFSSGR_585.8_944.4	G6PE_HUMAN	1.7763
GPGEDFR_389.2_623.3	PTGDS_HUMAN	1.6782
DPNGLPPEAQK_583.3_669.4	RET4_HUMAN	1.6581
IQTHSTTYR_369.5_540.3	F13B_HUMAN	1.6107
VNHVTLSQPK_374.9_244.2	B2MG_HUMAN	1.4779
STLFVPR_410.2_518.3	PEPD_HUMAN	1.3961
GEVTYTTSQVSK_650.3_913.5	EGLN_HUMAN	1.3306
ALVLELAK_428.8_672.4	INHBE_HUMAN	1.2973
ANDQYLTAAALHNLDEAVK_	IL1A_HUMAN	1.1850
686.3_317.2
STLFVPR_410.2_272.2	PEPD_HUMAN	1.1842
GPGEDFR_389.2_322.2	PTGDS_HUMAN	1.1742
IPSNPSHR_303.2_610.3	FBLN3_HUMAN	1.0868
HHGPTITAK_321.2_432.3	AMBP_HUMAN	1.0813
TLAFVR_353.7_274.2	FA7_HUMAN	1.0674
DLHLSDVFLK_396.2_366.2	CO6_HUMAN	0.9887
EFDDDTYDNDIALLQLK_	TPA_HUMAN	0.9468
1014.48_501.3
AIGLPEELIQK_605.86_856.5	FABPL_HUMAN	0.7740
LIENGYFHPVK_439.6_343.2	F13B_HUMAN	0.7740
LPDTPQGLLGEAR_683.87_427.2	EGLN_HUMAN	0.6748
EHSSLAFWK_552.8_267.1	APOH_HUMAN	0.6035
NCSFSIIYPVVIK_770.4_831.5	CRHBP_HUMAN	0.6014
ALNSIIDVYHK_424.9_661.3	S10A8_HUMAN	0.5987
WGAAPYR_410.7_577.3	PGRP2_HUMAN	0.5699
TQILEWAAER_608.8_632.3	EGLN_HUMAN	0.5395
IPSNPSHR_303.2_496.3	FBLN3_HUMAN	0.4845
VEHSDLSFSK_383.5_234.1	B2MG_HUMAN	0.4398
VEHSDLSFSK_383.5_468.2	B2MG_HUMAN	0.3883
FLYHK_354.2_284.2	AMBP_HUMAN	0.3410
LPDTPQGLLGEAR_683.87_940.5	EGLN_HUMAN	0.3282
EALVPLVADHK_397.9_390.2	HGFA_HUMAN	0.3091
IEGNLIFDPNNYLPK_874.0_845.5	APOB_HUMAN	0.2933
LIENGYFHPVK_439.6_627.4	F13B_HUMAN	0.2896
VPLALFALNR_557.3_620.4	PEPD_HUMAN	0.2875
FICPLTGLWPINTLK_887.0_685.4	APOH_HUMAN	0.2823
NAVVQGLEQPHGLVVHPLR_	LRP1_HUMAN	0.2763
688.4_890.6
ALNFGGIGVVVGHELTHAFDD-	ECE1_HUMAN	0.2385
QGR_837.1_299.2
SPELQAEAK_486.8_659.4	APOA2_HUMAN	0.2232
EVFSKPISWEELLQ_852.9_260.2	FA40A_HUMAN	0.1608
VANYVDWINDR_682.8_917.4	HGFA_HUMAN	0.1507
EVFSKPISWEELLQ_852.9_376.2	FA40A_HUMAN	0.1487
HVVQLR_376.2_614.4	IL6RA_HUMAN	0.1256
TVQAVLTVPK_528.3_855.5	PEDF_HUMAN	0.1170
ELIEELVNITQNQK_557.6_517.3	IL13_HUMAN	0.1159
EALVPLVADHK_397.9_439.8	HGFA_HUMAN	0.0979
AITPPHPASQANIIFDITEGNL-	FBLN1_HUMAN	0.0797
R_825.8_917.5
FLYHK_354.2_447.2	AMBP_HUMAN	0.0778
SLLQPNK_400.2_358.2	CO8A_HUMAN	0.0698
TGISPLALIK_506.8_654.5	APOB_HUMAN	0.0687
ALNFGGIGVVVGHELTHAFDDQGR_	ECE1_HUMAN	0.0571
837.1_360.2
DYWSTVK_449.7_347.2	APOC3_HUMAN	0.0357
AITPPHPASQANIIFDITEGNLR_	FBLN1_HUMAN	0.0313
825.8_459.3
AALAAFNAQNNGSNFQLEEISR_	FETUA_HUMAN	0.0279
789.1_633.3
DPNGLPPEAQK_583.3_497.2	RET4_HUMAN	0.0189
TLAFVR_353.7_492.3	FA7_HUMAN	0.0087

TABLE 17
Lasso Summed Coefficients Early Window
		SumBest-
Transition	Protein	Coefs_Early
LDFHFSSDR_375.2_611.3	INHBC_HUMAN	40.2030
ELPQSIVYK_538.8_417.7	FBLN3_HUMAN	22.6926
GFQALGDAADIR_617.3_288.2	TIMP1_HUMAN	17.4169
GFQALGDAADIR_617.3_717.4	TIMP1_HUMAN	3.4083
VNHVTLSQPK_374.9_459.3	B2MG_HUMAN	3.2559
EFDDDTYDNDIALLQLK_	TPA_HUMAN	2.4073
1014.48_388.3
STLFVPR_410.2_272.2	PEPD_HUMAN	2.3984
WGAAPYR_410.7_634.3	PGRP2_HUMAN	2.3564
LDFHFSSDR_375.2_464.2	INHBC_HUMAN	1.9038
VNHVTLSQPK_374.9_244.2	B2MG_HUMAN	1.7999
DTDTGALLFIGK_625.8_217.1	PEDF_HUMAN	1.5802
GPGEDFR_389.2_623.3	PTGDS_HUMAN	1.4223
IHWESASLLR_606.3_437.2	CO3_HUMAN	1.2735
ELIEELVNITQNQK_557.6_618.3	IL13_HUMAN	1.2652
AQPVQVAEGSEPDGFWEALGGK_	GELS_HUMAN	1.2361
758.0_623.4
FAFNLYR_465.8_565.3	HEP2_HUMAN	1.0876
SGFSFGFK_438.7_732.4	CO8B_HUMAN	1.0459
VVGGLVALR_442.3_784.5	FA12_HUMAN	0.9572
IEGNLIFDPNNYLPK_874.0_845.5	APOB_HUMAN	0.9571
ETLLQDFR_511.3_565.3	AMBP_HUMAN	0.7851
LSIPQITTK_500.8_687.4	PSG5_HUMAN	0.7508
TASDFITK_441.7_710.4	GELS_HUMAN	0.6549
YGIEEHGK_311.5_599.3	CXA1_HUMAN	0.6179
AFQVWSDVTPLR_709.88_347.2	MMP2_HUMAN	0.6077
TVQAVLTVPK_528.3_855.5	PEDF_HUMAN	0.5889
LSITGTYDLK_555.8_696.4	A1AT_HUMAN	0.5857
ELIEELVNITQNQK_557.6_517.3	IL13_HUMAN	0.5334
LIENGYFHPVK_439.6_627.4	F13B_HUMAN	0.5257
NEIVFPAGILQAPFYTR_968.5_357.2	ECE1_HUMAN	0.4601
SLLQPNK_400.2_358.2	CO8A_HUMAN	0.4347
LSIPQITTK_500.8_800.5	PSG5_HUMAN	0.4329
GVTGYFTFNLYLK_508.3_683.9	PSG5_HUMAN	0.4302
IQTHSTTYR_369.5_627.3	F13B_HUMAN	0.4001
ATVVYQGER_511.8_652.3	APOH_HUMAN	0.3909
LPDTPQGLLGEAR_683.87_427.2	EGLN_HUMAN	0.3275
NNQLVAGYLQGPNVNLEEK_	IL1RA_HUMAN	0.3178
700.7_999.5
SERPPIFEIR_415.2_564.3	LRP1_HUMAN	0.3112
AHYDLR_387.7_566.3	FETUA_HUMAN	0.2900
NEIWYR_440.7_637.4	FA12_HUMAN	0.2881
ALDLSLK_380.2_575.3	ITIH3_HUMAN	0.2631
NKPGVYTDVAYYLAWIR_	FA12_HUMAN	0.2568
677.0_545.3
SYTITGLQPGTDYK_772.4_352.2	FINC_HUMAN	0.2277
LFIPQITPK_528.8_683.4	PSG11_HUMAN	0.2202
IIGGSDADIK_494.8_260.2	C1S_HUMAN	0.2182
AVDIPGLEAATPYR_736.9_399.2	TENA_HUMAN	0.2113
DTDTGALLFIGK_625.8_818.5	PEDF_HUMAN	0.2071
AEIEYLEK_497.8_389.2	LYAM1_HUMAN	0.1925
EHSSLAFWK_552.8_838.4	APOH_HUMAN	0.1899
LPDTPQGLLGEAR_683.87_940.5	EGLN_HUMAN	0.1826
WGAAPYR_410.7_577.3	PGRP2_HUMAN	0.1669
LFIPQITPK_528.8_261.2	PSG11_HUMAN	0.1509
WWGGQPLWITATK_772.4_929.5	ENPP2_HUMAN	0.1446
DSPSVWAAVPGK_607.31_301.2	PROF1_HUMAN	0.1425
LIQDAVTGLTVNGQITGDK_	ITIH3_HUMAN	0.1356
972.0_798.4
ALDLSLK_380.2_185.1	ITIH3_HUMAN	0.1305
TVQAVLTVPK_528.3_428.3	PEDF_HUMAN	0.1249
NAVVQGLEQPHGLVVHPLR_	LRP1_HUMAN	0.1092
688.4_890.6
NSDQEIDFK_548.3_409.2	S10A5_HUMAN	0.0937
YNSQLLSFVR_613.8_508.3	TFR1_HUMAN	0.0905
LLDFEFSSGR_585.8_553.3	G6PE_HUMAN	0.0904
ALNFGGIGVVVGHELTHAFDDQGR_	ECE1_HUMAN	0.0766
837.1_299.2
STLFVPR_410.2_518.3	PEPD_HUMAN	0.0659
DLHLSDVFLK_396.2_260.2	CO6_HUMAN	0.0506
EHSSLAFWK_552.8_267.1	APOH_HUMAN	0.0452
TQIDSPLSGK_523.3_703.4	VCAM1_HUMAN	0.0447
HHGPTITAK_321.2_432.3	AMBP_HUMAN	0.0421
AFQVWSDVTPLR_709.88_385.3	MMP2_HUMAN	0.0417
TGISPLALIK_506.8_741.5	APOB_HUMAN	0.0361
DLHLSDVFLK_396.2_366.2	CO6_HUMAN	0.0336
NTVISVNPSTK_580.3_845.5	VCAM1_HUMAN	0.0293
DIIKPDPPK_511.8_342.2	IL12B_HUMAN	0.0219
TGISPLALIK_506.8_654.5	APOB_HUMAN	0.0170
GAVHVVVAETDYQSFAVLYLER_	CO8G_HUMAN	0.0151
822.8_580.3
LNIGYIEDLK_589.3_837.4	PAI2_HUMAN	0.0048
GPGEDFR_389.2_322.2	PTGDS_HUMAN	0.0008

TABLE 18
Lasso Summed Coefficients Early Middle Combined Windows
		SumBest-
Transition	Protein	Coefs_EM
ELPQSIVYK_538.8_417.7	FBLN3_HUMAN	24.8794
AHYDLR_387.7_566.3	FETUA_HUMAN	20.8397
LDFHFSSDR_375.2_611.3	INHBC_HUMAN	18.6630
GFQALGDAADIR_617.3_288.2	TIMP1_HUMAN	14.7270
HHGPTITAK_321.2_432.3	AMBP_HUMAN	11.1473
VNHVTLSQPK_374.9_459.3	B2MG_HUMAN	10.9421
NNQLVAGYLQGPNVNLEEK_	IL1RA_HUMAN	10.4646
700.7_999.5
HHGPTITAK_321.2_275.1	AMBP_HUMAN	7.7034
ETLLQDFR_511.3_565.3	AMBP_HUMAN	6.7435
TVQAVLTVPK_528.3_428.3	PEDF_HUMAN	5.7356
SLQAFVAVAAR_566.8_487.3	IL23A_HUMAN	4.8684
YGIEEHGK_311.5_599.3	CXA1_HUMAN	4.4936
ATVVYQGER_511.8_652.3	APOH_HUMAN	3.9524
VNHVTLSQPK_374.9_244.2	B2MG_HUMAN	3.8937
ELIEELVNITQNQK_557.6_618.3	IL13_HUMAN	3.8022
ALNFGGIGVVVGHELTHAFDDQGR_	ECE1_HUMAN	3.7603
837.1_299.2
ETLLQDFR_511.3_322.2	AMBP_HUMAN	3.1792
TVQAVLTVPK_528.3_855.5	PEDF_HUMAN	3.1046
AALAAFNAQNNGSNFQLEEISR_	FETUA_HUMAN	3.0021
789.1_633.3
AVDIPGLEAATPYR_736.9_399.2	TENA_HUMAN	2.6899
DLHLSDVFLK_396.2_366.2	CO6_HUMAN	2.5525
DTDTGALLFIGK_625.8_818.5	PEDF_HUMAN	2.4794
SYTITGLQPGTDYK_772.4_352.2	FINC_HUMAN	2.4535
IQTHSTTYR_369.5_627.3	F13B_HUMAN	2.3395
AHYDLR_387.7_288.2	FETUA_HUMAN	2.1058
NCSFSIIYPVVIK_770.4_831.5	CRHBP_HUMAN	2.0427
AIGLPEELIQK_605.86_856.5	FABPL_HUMAN	1.5354
GFQALGDAADIR_617.3_717.4	TIMP1_HUMAN	1.4175
TGISPLALIK_506.8_654.5	APOB_HUMAN	1.3562
YTTEIIK_434.2_603.4	C1R_HUMAN	1.2855
ETPEGAEAKPWYEPIYLGGVFQLEK_	TNFA_HUMAN	1.1198
951.14_877.5
ANDQYLTAAALHNLDEAVK_	IL1A_HUMAN	1.0574
686.3_317.2
ILPSVPK_377.2_244.2	PGH1_HUMAN	1.0282
ALDLSLK_380.2_185.1	ITIH3_HUMAN	1.0057
NAVVQGLEQPHGLVVHPLR_	LRP1_HUMAN	0.9884
688.4_890.6
IEGNLIFDPNNYLPK_874.0_845.5	APOB_HUMAN	0.9846
ALDLSLK_380.2_575.3	ITIH3_HUMAN	0.9327
LDFHFSSDR_375.2_464.2	INHBC_HUMAN	0.8852
LSIPQITTK_500.8_800.5	PSG5_HUMAN	0.7740
SERPPIFEIR_415.2_564.3	LRP1_HUMAN	0.7013
AEAQAQYSAAVAK_654.3_709.4	ITIH4_HUMAN	0.6752
IHWESASLLR_606.3_437.2	CO3_HUMAN	0.6176
LFIPQITPK_528.8_261.2	PSG11_HUMAN	0.5345
FICPLTGLWPINTLK_887.0_685.4	APOH_HUMAN	0.5022
DFNQFSSGEK_386.8_189.1	FETA_HUMAN	0.4932
TATSEYQTFFNPR_781.4_272.2	THRB_HUMAN	0.4725
SPELQAEAK_486.8_788.4	APOA2_HUMAN	0.4153
FIVGFTR_420.2_261.2	CCL20_HUMAN	0.4111
TLLPVSKPEIR_418.3_288.2	CO5_HUMAN	0.3409
DIIKPDPPK_511.8_342.2	IL12B_HUMAN	0.3403
DTDTGALLFIGK_625.8_217.1	PEDF_HUMAN	0.3073
YTTEIIK_434.2_704.4	C1R_HUMAN	0.3050
SPELQAEAK_486.8_659.4	APOA2_HUMAN	0.3047
TGISPLALIK_506.8_741.5	APOB_HUMAN	0.3031
VVGGLVALR_442.3_784.5	FA12_HUMAN	0.2960
WWGGQPLWITATK_772.4_373.2	ENPP2_HUMAN	0.2498
TQILEWAAER_608.8_632.3	EGLN_HUMAN	0.2342
STLFVPR_410.2_272.2	PEPD_HUMAN	0.2035
DYWSTVK_449.7_347.2	APOC3_HUMAN	0.2018
WWGGQPLWITATK_772.4_929.5	ENPP2_HUMAN	0.1614
SILFLGK_389.2_201.1	THBG_HUMAN	0.1593
AFQVWSDVTPLR_709.88_385.3	MIVIP2_HUMAN	0.1551
IQTHSTTYR_369.5_540.3	F13B_HUMAN	0.1434
AFQVWSDVTPLR_709.88_347.2	MIVIP2_HUMAN	0.1420
LSITGTYDLK_555.8_797.4	A1AT_HUMAN	0.1395
LSITGTYDLK_555.8_696.4	A1AT_HUMAN	0.1294
WGAAPYR_410.7_634.3	PGRP2_HUMAN	0.1259
IAPQLSTEELVSLGEK_857.5_533.3	AFAM_HUMAN	0.1222
FICPLTGLWPINTLK_887.0_756.9	APOH_HUMAN	0.1153
QINSYVK_426.2_496.3	CBG_HUMAN	0.1055
TATSEYQTFFNPR_781.4_386.2	THRB_HUMAN	0.0921
AFLEVNEEGSEAAASTAVVIAGR_	ANT3_HUMAN	0.0800
764.4_685.4
AKPALEDLR_506.8_288.2	APOA1_HUMAN	0.0734
GPGEDFR_389.2_623.3	PTGDS_HUMAN	0.0616
SLLQPNK_400.2_358.2	CO8A_HUMAN	0.0565
ESDTSYVSLK_564.8_347.2	CRP_HUMAN	0.0497
FFQYDTWK_567.8_712.3	IGF2_HUMAN	0.0475
FSVVYAK_407.2_579.4	FETUA_HUMAN	0.0437
TQIDSPLSGK_523.3_703.4	VCAM1_HUMAN	0.0401
LNIGYIEDLK_589.3_837.4	PAI2_HUMAN	0.0307
IPSNPSHR_303.2_496.3	FBLN3_HUMAN	0.0281
NEIVFPAGILQAPFYTR_968.5_456.2	ECE1_HUMAN	0.0276
TLAFVR_353.7_274.2	FA7_HUMAN	0.0220
AEAQAQYSAAVAK_654.3_908.5	ITIH4_HUMAN	0.0105
AQPVQVAEGSEPDGFWEALGGK_	GELS_HUMAN	0.0103
758.0_623.4
QINSYVK_426.2_610.3	CBG_HUMAN	0.0080
NSDQEIDFK_548.3_409.2	S10A5_HUMAN	0.0017

TABLE 19
Lasso Summed Coefficients Middle-Late Combined Windows
		SumBest-
Transition	Protein	Coefs_ML
TQILEWAAER_608.8_761.4	EGLN_HUMAN	45.0403
GDTYPAELYITGSILR_885.0_274.1	F13B_HUMAN	31.4888
GEVTYTTSQVSK_650.3_750.4	EGLN_HUMAN	22.3322
GEVTYTTSQVSK_650.3_913.5	EGLN_HUMAN	17.0298
AVDIPGLEAATPYR_736.9_286.1	TENA_HUMAN	8.6029
AVDIPGLEAATPYR_736.9_399.2	TENA_HUMAN	7.9874
NEIVFPAGILQAPFYTR_968.5_357.2	ECE1_HUMAN	7.8773
ALNHLPLEYNSALYSR_621.0_696.4	CO6_HUMAN	6.8534
DPNGLPPEAQK_583.3_669.4	RET4_HUMAN	5.0045
GFQALGDAADIR_617.3_717.4	TIMP1_HUMAN	4.6191
ATVVYQGER_511.8_652.3	APOH_HUMAN	4.2522
IAQYYYTFK_598.8_395.2	F13B_HUMAN	3.5721
NAVVQGLEQPHGLVVHPLR_	LRP1_HUMAN	3.2886
688.4_285.2
IAQYYYTFK_598.8_884.4	F13B_HUMAN	2.9205
SERPPIFEIR_415.2_564.3	LRP1_HUMAN	2.4237
TLAFVR_353.7_274.2	FA7_HUMAN	2.1925
EVFSKPISWEELLQ_852.9_260.2	FA40A_HUMAN	2.1591
EVFSKPISWEELLQ_852.9_376.2	FA40A_HUMAN	2.1586
EFDDDTYDNDIALLQLK_	TPA_HUMAN	2.0892
1014.48_501.3
TLAFVR_353.7_492.3	FA7_HUMAN	2.0399
EALVPLVADHK_397.9_439.8	HGFA_HUMAN	1.8856
ETLLQDFR_511.3_565.3	AMBP_HUMAN	1.7809
ALNSIIDVYHK_424.9_661.3	S10A8_HUMAN	1.6114
AITPPHPASQANIIFDITEGNLR_	FBLN1_HUMAN	1.3423
825.8_917.5
EQLGEFYEALDCLR_871.9_747.4	A1AG1_HUMAN	1.2473
TFLTVYWTPER_706.9_502.3	ICAM1_HUMAN	0.9851
NTVISVNPSTK_580.3_845.5	VCAM1_HUMAN	0.9845
FLNWIK_410.7_560.3	HABP2_HUMAN	0.9798
ETPEGAEAKPWYEPIYLGGVFQLEK_	TNFA_HUMAN	0.9679
951.14_990.6
NVNQSLLELHK_432.2_656.3	FRIH_HUMAN	0.8280
VPLALFALNR_557.3_620.4	PEPD_HUMAN	0.7851
IAPQLSTEELVSLGEK_857.5_533.3	AFAM_HUMAN	0.7731
AVYEAVLR_460.8_750.4	PEPD_HUMAN	0.7452
LPDTPQGLLGEAR_683.87_427.2	EGLN_HUMAN	0.7145
TVQAVLTVPK_528.3_428.3	PEDF_HUMAN	0.6584
YSHYNER_323.48_418.2	HABP2_HUMAN	0.5244
LLELTGPK_435.8_644.4	A1BG_HUMAN	0.5072
DTDTGALLFIGK_625.8_818.5	PEDF_HUMAN	0.5010
DPNGLPPEAQK_583.3_497.2	RET4_HUMAN	0.4803
AHYDLR_387.7_566.3	FETUA_HUMAN	0.4693
LPNNVLQEK_527.8_844.5	AFAM_HUMAN	0.4640
VTGLDFIPGLHPILTLSK_	LEP_HUMAN	0.4584
641.04_771.5
LLELTGPK_435.8_227.2	A1BG_HUMAN	0.4515
YTTEIIK_434.2_704.4	C1R_HUMAN	0.4194
SSNNPHSPIVEEFQVPYNK_	C1S_HUMAN	0.3886
729.4_261.2
ALNHLPLEYNSALYSR_	CO6_HUMAN	0.3405
621.0_538.3
HFQNLGK_422.2_527.2	AFAM_HUMAN	0.3368
EQLGEFYEALDCLR_871.9_563.3	A1AG1_HUMAN	0.3348
TQILEWAAER_608.8_632.3	EGLN_HUMAN	0.2943
ALVLELAK_428.8_672.4	INHBE_HUMAN	0.2895
LSNENHGIAQR_413.5_519.8	ITIH2_HUMAN	0.2835
LPNNVLQEK_527.8_730.4	AFAM_HUMAN	0.2764
DTDTGALLFIGK_625.8_217.1	PEDF_HUMAN	0.2694
GDTYPAELYITGSILR_885.0_922.5	F13B_HUMAN	0.2594
GPITSAAELNDPQSILLR_632.3_601.4	EGLN_HUMAN	0.2388
ANLINNIFELAGLGK_793.9_834.5	LCAP_HUMAN	0.2158
SEPRPGVLLR_375.2_454.3	FA7_HUMAN	0.1921
EQSLNVSQDLDTIR_539.9_557.8	SYNE2_HUMAN	0.1836
FICPLTGLWPINTLK_887.0_685.4	APOH_HUMAN	0.1806
ALNFGGIGVVVGHELTHAFDDQGR_	ECE1_HUMAN	0.1608
837.1_360.2
ANDQYLTAAALHNLDEAVK_	ILIA_HUMAN	0.1607
686.3_317.2
AQETSGEEISK_589.8_979.5	IBP1_HUMAN	0.1598
QINSYVK_426.2_610.3	CBG_HUMAN	0.1592
SILFLGK_389.2_577.4	THBG_HUMAN	0.1412
DAVVYPILVEFTR_761.4_286.1	HYOU1_HUMAN	0.1298
LIEIANHVDK_384.6_683.3	ADA12_HUMAN	0.1297
LSSPAVITDK_515.8_830.5	PLMN_HUMAN	0.1272
LIENGYFHPVK_439.6_343.2	F13B_HUMAN	0.1176
AALAAFNAQNNGSNFQLEEISR_	FETUA_HUMAN	0.1160
789.1_633.3
IQTHSTTYR_369.5_540.3	F13B_HUMAN	0.1146
IPKPEASFSPR_410.2_506.3	ITIH4_HUMAN	0.1001
LLDFEFSSGR_585.8_944.4	G6PE_HUMAN	0.0800
YYLQGAK_421.7_516.3	ITIH4_HUMAN	0.0793
VRPQQLVK_484.3_722.4	ITIH4_HUMAN	0.0744
GPGEDFR_389.2_322.2	PTGDS_HUMAN	0.0610
ITQDAQLK_458.8_803.4	CBG_HUMAN	0.0541
TATSEYQTFFNPR_781.4_272.2	THRB_HUMAN	0.0511
ETLLQDFR_511.3_322.2	AMBP_HUMAN	0.0472
YEFLNGR_449.7_293.1	PLMN_HUMAN	0.0345
TLYSSSPR_455.7_696.3	IC1_HUMAN	0.0316
SLLQPNK_400.2_599.4	CO8A_HUMAN	0.0242
LLEVPEGR_456.8_686.4	C1S_HUMAN	0.0168
GGEGTGYFVDFSVR_745.9_722.4	HRG_HUMAN	0.0110
IQTHSTTYR_369._627.3	F13B_HUMAN	0.0046

TABLE 20
Random Forest SummedGini All Windows
Transition	Protein	SumBestGini	Probability
TVQAVLTVPK_528.3_428.3	PEDF_HUMAN	12.6521	1.0000
DTDTGALLFIGK_625.8_818.5	PEDF_HUMAN	11.9585	0.9985
ALALPPLGLAPLLNLWAKPQGR_770.5_256.2	SHBG_HUMAN	10.5229	0.9971
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	10.2666	0.9956
791.8_883.0
ETLLQDFR_511.3_565.3	AMBP_HUMAN	8.9862	0.9941
ALALPPLGLAPLLNLWAKPQGR_	SHBG_HUMAN	8.6349	0.9927
770.5_457.3
IALGGLLFPASNLR_481.3_657.4	SHBG_HUMAN	8.5838	0.9912
DTDTGALLFIGK_625.8_217.1	PEDF_HUMAN	8.2463	0.9897
IQTHSTTYR_369.5_627.3	F13B_HUMAN	8.1199	0.9883
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	7.7393	0.9868
791.8_310.2
IALGGLLFPASNLR_481.3_412.3	SHBG_HUMAN	7.5601	0.9853
HHGPTITAK_321.2_432.3	AMBP_HUMAN	7.5181	0.9838
ETLLQDFR_511.3_322.2	AMBP_HUMAN	7.4043	0.9824
FICPLTGLWPINTLK_887.0_685.4	APOH_HUMAN	7.2072	0.9809
GPGEDFR_389.2_623.3	PTGDS_HUMAN	7.1422	0.9794
IQTHSTTYR_369.5_540.3	F13B_HUMAN	6.9809	0.9780
TVQAVLTVPK_528.3_855.5	PEDF_HUMAN	6.6191	0.9765
ATVVYQGER_511.8_652.3	APOH_HUMAN	6.5813	0.9750
VVLSSGSGPGLDLPLVLGL-	SHBG_HUMAN	6.3244	0.9736
PLQLK_791.5_598.4
HHGPTITAK_321.2_275.1	AMBP_HUMAN	6.3081	0.9721
VVLSSGSGPGLDLPLVLGL-	SHBG_HUMAN	6.0654	0.9706
PLQLK_791.5_768.5
GDTYPAELYITGSILR_	F13B_HUMAN	5.9580	0.9692
885.0_274.1
ATVVYQGER_511.8_751.4	APOH_HUMAN	5.9313	0.9677
LDFHFSSDR_375.2_611.3	INHBC_HUMAN	5.8533	0.9662
LDFHFSSDR_375.2_464.2	INHBC_HUMAN	5.8010	0.9648
EVFSKPISWEELLQ_852.9_260.2	FA40A_HUMAN	5.6648	0.9633
DTYVSSFPR_357.8_272.2	TCEA1_HUMAN	5.6549	0.9618
LPDTPQGLLGEAR_683.87_427.2	EGLN_HUMAN	5.3806	0.9604
FLYHK_354.2_447.2	AMBP_HUMAN	5.3764	0.9589
SPELQAEAK_486.8_659.4	APOA2_HUMAN	5.1896	0.9574
GPGEDFR_389.2_322.2	PTGDS_HUMAN	5.1876	0.9559
SGVDLADSNQK_567.3_662.3	VGFR3_HUMAN	5.1159	0.9545
TNTNEFLIDVDK_704.85_849.5	TF_HUMAN	4.7216	0.9530
FICPLTGLWPINTLK_887.0_756.9	APOH_HUMAN	4.6421	0.9515
LNIGYIEDLK_589.3_950.5	PAI2_HUMAN	4.6250	0.9501
EVFSKPISWEELLQ_852.9_376.2	FA40A_HUMAN	4.4215	0.9486
SYTITGLQPGTDYK_772.4_680.3	FINC_HUMAN	4.4103	0.9471
TLPFSR_360.7_409.2	LYAM1_HUMAN	4.2148	0.9457
SPELQAEAK_486.8_788.4	APOA2_HUMAN	4.2081	0.9442
GDTYPAELYITGSILR_	F13B_HUMAN	4.0672	0.9427
885.0_922.5
AEIEYLEK_497.8_552.3	LYAM1_HUMAN	3.9248	0.9413
FSLVSGWGQLLDR_493.3_403.2	FA7_HUMAN	3.9034	0.9398
FLYHK_354.2_284.2	AMBP_HUMAN	3.8982	0.9383
SGVDLADSNQK_567.3_591.3	VGFR3_HUMAN	3.8820	0.9369
LDGSTHLNIFFAK_488.3_739.4	PAPP1_HUMAN	3.8770	0.9354
HFQNLGK_422.2_527.2	AFAM_HUMAN	3.7628	0.9339
IAQYYYTFK_598.8_884.4	F13B_HUMAN	3.7040	0.9325
GFQALGDAADIR_617.3_717.4	TIMP1_HUMAN	3.6538	0.9310
ELPQSIVYK_538.8_417.7	FBLN3_HUMAN	3.6148	0.9295
IAQYYYTFK_598.8_395.2	F13B_HUMAN	3.5820	0.9280
GSLVQASEANLQAAQDFVR_	ITIH1_HUMAN	3.5283	0.9266
668.7_735.4
TLPFSR_360.7_506.3	LYAM1_HUMAN	3.5064	0.9251
VNHVTLSQPK_374.9_244.2	B2MG_HUMAN	3.5045	0.9236
IAPQLSTEELVSLGEK_	AFAM_HUMAN	3.4990	0.9222
857.5_533.3
VEHSDLSFSK_383.5_468.2	B2MG_HUMAN	3.4514	0.9207
TQILEWAAER_608.8_761.4	EGLN_HUMAN	3.4250	0.9192
AHQLAIDTYQEFEETYIPK_	CSH_HUMAN	3.3634	0.9178
766.0_521.3
TEFLSNYLTNVDDITLVP-	ENPP2_HUMAN	3.3512	0.9163
GTLGR_846.8_600.3
HFQNLGK_422.2_285.1	AFAM_HUMAN	3.3375	0.9148
VEHSDLSFSK_383.5_234.1	B2MG_HUMAN	3.3371	0.9134
TELRPGETLNVNFLLR_624.68_	CO3_HUMAN	3.1889	0.9119
875.5
YQISVNK_426.2_292.1	FIBB_HUMAN	3.1668	0.9104
YGFYTHVFR_397.2_659.4	THRB_HUMAN	3.1188	0.9075
SEPRPGVLLR_375.2_454.3	FA7_HUMAN	3.1068	0.9060
IAPQLSTEELVSLGEK_857.5_	AFAM_HUMAN	3.0917	0.9046
333.2
ILILPSVTR_506.3_785.5	PSGx_HUMAN	3.0346	0.9031
TLAFVR_353.7_492.3	FA7_HUMAN	3.0237	0.9016
AKPALEDLR_506.8_288.2	APOA1_HUMAN	3.0189	0.9001

TABLE 21
Random Forest SummedGini Early Window
Transition	Protein	SumBestGini	Probability
LSETNR_360.2_330.2	PSG1_HUMAN	26.3610	1.0000
ALNFGGIGVVVGHELTHAFDDQGR_837.1_	ECE1_HUMAN	24.8946	0.9985
299.2
ELPQSIVYK_538.8_417.7	FBLN3_HUMAN	24.8817	0.9971
LDFHFSSDR_375.2_464.2	INHBC_HUMAN	24.3229	0.9956
LDFHFSSDR_375.2_611.3	INHBC_HUMAN	22.2162	0.9941
FSLVSGWGQLLDR_493.3_403.2	FA7_HUMAN	19.6528	0.9927
TSESGELHGLTTEEEFVEGIYK_	TTHY_HUMAN	19.2430	0.9912
819.06_310.2
ATVVYQGER_511.8_751.4	APOH_HUMAN	19.1321	0.9897
IQTHSTTYR_369.5_627.3	F13B_HUMAN	17.1528	0.9883
ATVVYQGER_511.8_652.3	APOH_HUMAN	17.0214	0.9868
HYINLITR_515.3_301.1	NPY_HUMAN	16.6713	0.9853
FICPLTGLWPINTLK_887.0_685.4	APOH_HUMAN	15.0826	0.9838
AFLEVNEEGSEAAASTAVVIAGR_	ANT3_HUMAN	14.6110	0.9824
764.4_614.4
IQTHSTTYR_369.5_540.3	F13B_HUMAN	14.5473	0.9809
AHQLAIDTYQEFEETYIPK_	CSH_HUMAN	14.0287	0.9794
766.0_521.3
TGAQELLR_444.3_530.3	GELS_HUMAN	13.1389	0.9780
DSPSVWAAVPGK_607.31_301.2	PROF1_HUMAN	12.9571	0.9765
NCSFSIIYPVVIK_770.4_555.4	CRHBP_HUMAN	12.5867	0.9750
ALALPPLGLAPLLNLWAKPQGR_	SHBG_HUMAN	12.1138	0.9721
770.5_256.2
DTDTGALLFIGK_625.8_818.5	PEDF_HUMAN	11.7054	0.9706
TSDQIHFFFAK_447.6_512.3	ANT3_HUMAN	11.4261	0.9692
IALGGLLFPASNLR_481.3_657.4	SHBG_HUMAN	11.0968	0.9677
DTDTGALLFIGK_625.8_217.1	PEDF_HUMAN	10.9040	0.9662
EQSLNVSQDLDTIR_539.9_758.4	SYNE2_HUMAN	10.6572	0.9648
IALGGLLFPASNLR_481.3_412.3	SHBG_HUMAN	10.0629	0.9633
FGFGGSTDSGPIR_649.3_745.4	ADA12_HUMAN	10.0449	0.9618
ETPEGAEAKPWYEPIYLGGVFQLEK_	TNFA_HUMAN	10.0286	0.9604
951.14_877.5
LPDTPQGLLGEAR_683.87_427.2	EGLN_HUMAN	9.8980	0.9589
FSVVYAK_407.2_381.2	FETUA_HUMAN	9.7971	0.9574
YGIEEHGK_311.5_599.3	CXA1_HUMAN	9.7850	0.9559
GFQALGDAADIR_617.3_717.4	TIMP1_HUMAN	9.7587	0.9545
VVLSSGSGPGLDLPLVLGLPLQLK_	SHBG_HUMAN	9.3421	0.9530
791.5_598.4
HHGPTITAK_321.2_275.1	AMBP_HUMAN	9.2728	0.9515
ALALPPLGLAPLLNLWAKPQGR_	SHBG_HUMAN	9.2431	0.9501
770.54_57.3
LIEIANHVDK_384.6_498.3	ADA12_HUMAN	9.1368	0.9486
AFQVWSDVTPLR_709.88_347.2	MMP2_HUMAN	8.6789	0.9471
AFQVWSDVTPLR_709.88_385.3	MMP2_HUMAN	8.6339	0.9457
ETLLQDFR_511.3_322.2	AMBP_HUMAN	8.6252	0.9442
ETLLQDFR_511.3_565.3	AMBP_HUMAN	8.3957	0.9427
VNHVTLSQPK_374.9_459.3	B2MG_HUMAN	8.3179	0.9413
HHGPTITAK_321.2_432.3	AMBP_HUMAN	8.2567	0.9398
DTYVSSFPR_357.8_272.2	TCEA1_HUMAN	8.2028	0.9383
GGEGTGYFVDFSVR_745.9_722.4	HRG_HUMAN	8.0751	0.9369
DFNQFSSGEK_386.8_189.1	FETA_HUMAN	8.0401	0.9354
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	7.9924	0.9339
791.8_883.0
VSEADSSNADWVTK_754.9_347.2	CFAB_HUMAN	7.8630	0.9325
QGHNSVFLIK_381.6_260.2	HEMO_HUMAN	7.8588	0.9310
AQETSGEEISK_589.8_979.5	IBP1_HUMAN	7.7787	0.9295
DIPHWLNPTR_416.9_600.3	PAPP1_HUMAN	7.6393	0.9280
SPELQAEAK_486.8_788.4	APOA2_HUMAN	7.6248	0.9266
QGHNSVFLIK_381.6_520.4	HEMO_HUMAN	7.6042	0.9251
LIENGYFHPVK_439.6_343.2	F13B_HUMAN	7.5771	0.9236
DIIKPDPPK_511.8_342.2	IL12B_HUMAN	7.5523	0.9222
VNHVTLSQPK_374.9_244.2	B2MG_HUMAN	7.5296	0.9207
TELRPGETLNVNFLLR_624.68_	CO3_HUMAN	7.4484	0.9178
875.5
QINSYVK_426.2_496.3	CBG_HUMAN	7.3266	0.9163
YNSQLLSFVR_613.8_734.5	TFR1_HUMAN	7.3262	0.9148
TVQAVLTVPK_528.3_855.5	PEDF_HUMAN	7.1408	0.9134
QTLSWTVTPK_580.8_818.4	PZP_HUMAN	6.9764	0.9119
DVLLLVHNLPQNLPGYFWYK_	PSG9_HUMAN	6.9663	0.9104
810.4_328.2
FICPLTGLWPINTLK_887.0_756.9	APOH_HUMAN	6.8924	0.9090
TSYQVYSK_488.2_397.2	C163A_HUMAN	6.5617	0.9075
VVLSSGSGPGLDLPLVLGLPLQLK_	SHBG_HUMAN	6.4615	0.9060
791.5_768.5
QINSYVK_426.2_610.3	CBG_HUMAN	6.4595	0.9046
LHKPGVYTR_357.5_479.3	HGFA_HUMAN	6.4062	0.9031
ALVLELAK_428.8_672.4	INHBE_HUMAN	6.3684	0.9016
YNSQLLSFVR_613.8_508.3	TFR1_HUMAN	6.3628	0.9001

TABLE 22
Random Forest SummedGini Early-Middle Combined Windows
Transition	Protein	SumBestGini	Probability
ATVVYQGER_511.8_652.3	APOH_HUMAN	120.6132	1.0000
ATVVYQGER_511.8_751.4	APOH_HUMAN	99.7548	0.9985
IQTHSTTYR_369.5_627.3	F13B_HUMAN	57.5339	0.9971
IQTHSTTYR_369.5_540.3	F13B_HUMAN	55.0267	0.9956
FICPLTGLWPINTLK_887.0_685.4	APOH_HUMAN	49.9116	0.9941
AHQLAIDTYQEFEETYIPK_766.0_521.3	CSH_HUMAN	48.9796	0.9927
HHGPTITAK_321.2_432.3	AMBP_HUMAN	45.7432	0.9912
SPELQAEAK_486.8_659.4	APOA2_HUMAN	42.1848	0.9897
AHYDLR_387.7_566.3	FETUA_HUMAN	41.4591	0.9883
ETLLQDFR_511.3_565.3	AMBP_HUMAN	39.7301	0.9868
HHGPTITAK_321.2_275.1	AMBP_HUMAN	39.2096	0.9853
ETLLQDFR_511.3_322.2	AMBP_HUMAN	36.8033	0.9838
FICPLTGLWPINTLK_	APOH_HUMAN	31.8246	0.9824
887.0_756.9
TVQAVLTVPK_528.3_855.5	PEDF_HUMAN	31.1356	0.9809
IALGGLLFPASNLR_481.3_657.4	SHBG_HUMAN	30.5805	0.9794
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	29.5729	0.9780
791.8_883.0
AHYDLR_387.7_288.2	FETUA_HUMAN	29.0239	0.9765
SPELQAEAK_486.8_788.4	APOA2_HUMAN	28.6741	0.9750
ETPEGAEAKPWYEPIYLGGVF-	TNFA_HUMAN	26.8117	0.9736
QLEK_951.14_877.5
LDFHFSSDR_375.2_611.3	INHBC_HUMAN	26.0001	0.9721
DFNQFSSGEK_386.8_189.1	FETA_HUMAN	25.9113	0.9706
HFQNLGK_422.2_527.2	AFAM_HUMAN	25.7497	0.9692
DPDQTDGLGLSYLSSHIANVER_	GELS_HUMAN	25.7418	0.9677
796.4_328.1
VVLSSGSGPGLDLPLVLGLPLQLK_7	SHBG_HUMAN	25.6425	0.9662
91.5_598.4
IALGGLLFPASNLR_481.3_412.3	SHBG_HUMAN	25.1737	0.9648
LDFHFSSDR_375.2_464.2	INHBC_HUMAN	25.0674	0.9633
LIQDAVTGLTVNGQITGDK_	ITIH3_HUMAN	24.5613	0.9618
972.0_640.4
VVLSSGSGPGLDLPLVLGLPLQLK_	SHBG_HUMAN	23.2995	0.9604
791.5_768.5
DIPHWLNPTR_416.9_600.3	PAPP1_HUMAN	22.9504	0.9589
VNHVTLSQPK_374.9_459.3	B2MG_HUMAN	22.2821	0.9574
QINSYVK_426.2_496.3	CBG_HUMAN	22.2233	0.9559
ALALPPLGLAPLLNLWAKPQGR_	SHBG_HUMAN	22.1160	0.9545
770.5_256.2
TELRPGETLNVNFLLR_624.68_875.5	CO3_HUMAN	21.9043	0.9530
ITQDAQLK_458.8_803.4	CBG_HUMAN	21.8933	0.9515
IAPQLSTEELVSLGEK_857.5_533.3	AFAM_HUMAN	21.4577	0.9501
QINSYVK_426.2_610.3	CBG_HUMAN	21.3414	0.9486
LIQDAVTGLTVNGQITGDK_	ITIH3_HUMAN	21.2843	0.9471
972.0_798.4
DTDTGALLFIGK_625.8_818.5	PEDF_HUMAN	21.2631	0.9457
DVLLLVHNLPQNLPGYFWYK_	PSG9_HUMAN	21.2547	0.9442
810.4_328.2
HFQNLGK_422.2_285.1	AFAM_HUMAN	20.8051	0.9427
DTDTGALLFIGK_625.8_217.1	PEDF_HUMAN	20.2572	0.9413
FLYHK_354.2_447.2	AMBP_HUMAN	19.6822	0.9398
NNQLVAGYLQGPNVNLEEK_	IL1RA_HUMAN	19.2156	0.9383
700.7_999.5
VSFSSPLVAISGVALR_802.0_715.4	PAPP1_HUMAN	18.9721	0.9369
TVQAVLTVPK_528.3_428.3	PEDF_HUMAN	18.9392	0.9354
TFVNITPAEVGVLVGK_822.47_968.6	PROF1_HUMAN	18.9351	0.9339
LQVLGK_329.2_416.3	A2GL_HUMAN	18.6613	0.9325
TLAFVR_353.7_274.2	FA7_HUMAN	18.5095	0.9310
ITQDAQLK_458.8_702.4	CBG_HUMAN	18.5046	0.9295
DVLLLVHNLPQNLTGHIWYK_	PSG7_HUMAN	18.4015	0.9280
791.8_310.2
VSFSSPLVAISGVALR_802.0_602.4	PAPP1_HUMAN	17.5397	0.9266
IAPQLSTEELVSLGEK_857.5_333.2	AFAM_HUMAN	17.5338	0.9251
TLFIFGVTK_513.3_215.1	PSG4_HUMAN	17.5245	0.9236
ALNFGGIGVVVGHELTHAFDDQGR_	ECE1_HUMAN	17.1108	0.9222
837.1_299.2
FLYHK_354.2_284.2	AMBP_HUMAN	16.9237	0.9207
LDGSTHLNIFFAK_488.3_739.4	PAPP1_HUMAN	16.8260	0.9192
ELIEELVNITQNQK_557.6_618.3	IL13_HUMAN	16.5607	0.9178
YNSQLLSFVR_613.8_734.5	TFR1_HUMAN	16.5425	0.9163
AFQVWSDVTPLR_709.88_385.3	MMP2_HUMAN	16.3293	0.9148
LDGSTHLNIFFAK_488.3_852.5	PAPP1_HUMAN	15.9820	0.9134
TPSAAYLWVGTGASEAEK_	GELS_HUMAN	15.9084	0.9119
919.5_428.2
YTTEIIK_434.2_603.4	C1R_HUMAN	15.7998	0.9104
FSVVYAK_407.2_381.2	FETUA_HUMAN	15.4991	0.9090
VNHVTLSQPK_374.9_244.2	B2MG_HUMAN	15.2938	0.9075
SYTITGLQPGTDYK_772.4_680.3	FINC_HUMAN	14.9898	0.9060
DIPHWLNPTR_416.9_373.2	PAPP1_HUMAN	14.6923	0.9046
AFQVWSDVTPLR_709.88_347.2	MMP2_HUMAN	14.4361	0.9031
IAQYYYTFK_598.8_884.4	F13B_HUMAN	14.4245	0.9016
FSLVSGWGQLLDR_493.3_403.2	FA7_HUMAN	14.3848	0.9001

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.

Claims

1. A panel of isolated biomarkers comprising two or more biomarkers, wherein said two or more biomarkers comprise sex hormone-binding globulin (SHBG) and one or more biomarkers selected from the group consisting of afamin (AFAM), apolipoprotein C III (APOC3), complement C5 preproprotein (CO5) and chorionic somatomammotropin hormone (CSH), or fragments or derivatives thereof.

2.-6. (canceled)

7. A method of determining probability for preeclampsia in a pregnant female, the method comprising detecting a measurable feature of two or more biomarkers in a biological sample obtained from said pregnant female, and analyzing said measurable feature to determine the probability for preeclampsia in said pregnant female, wherein said two or more biomarkers comprise sex hormone-binding globulin (SHBG) and one or more biomarkers selected from the group consisting of afamin (AFAM), apolipoprotein C III (APOC3), complement C5 preproprotein (CO5) and chorionic somatomammotropin hormone (CSH).

8. The method of claim 7, wherein said measurable feature comprises fragments or derivatives of said two or more biomarkers.

9. The method of claim 7, wherein said detecting a measurable feature comprises quantifying an amount of said two or more biomarkers, or fragments or derivatives thereof in said biological sample.

10. The method of claim 9, further comprising calculating the probability for preeclampsia in said pregnant female based on said quantified amount of said two or more biomarkers, and wherein said probability is expressed as a risk score.

11. (canceled)

12. The method of claim 7, further comprising an initial step of providing a biological sample from the pregnant female, wherein the biological sample is selected from the group consisting of whole blood, plasma, and serum.

13. The method of claim 7, further comprising communicating said probability to a health care provider, wherein said communication informs a subsequent treatment decision for said pregnant female.

14.-16. (canceled)

17. The method of claim 7, wherein said analysis comprises use of a predictive model or comparing said two or more biomarkers with a reference biomarker.

18. (canceled)

19. The method of claim 17, wherein said analysis comprises using one or more analyses selected from the group consisting 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, and a combination thereof.

20.-23. (canceled)

24. The method of claim 7, wherein said quantifying comprises mass spectrometry (MS).

25.-27. (canceled)

28. The method of claim 7, wherein said quantifying comprises an assay that utilizes a capture agent, wherein said capture agent is selected from the group consisting of an antibody, antibody fragment, nucleic acid-based protein binding reagent, or small molecule.

29. (canceled)

30. The method of claim 28, wherein said assay is selected from the group consisting of enzyme immunoassay (EIA), enzyme-linked immunosorbent assay (ELISA), and radioimmunoassay (RIA).

31. (canceled)

32. The method of claim 24, wherein said MS comprises co-immunoprecipitation mass spectrometry (co-IP MS).

33. The method of claim 7, further comprising detecting a measurable feature for one or more risk indicia, wherein the one or more risk indicia are selected from the group consisting of history of preeclampsia, first pregnancy, age, obesity, diabetes, gestational diabetes, hypertension, kidney disease, multiple pregnancy, interval between pregnancies, new paternity, migraine headaches, rheumatoid arthritis, and lupus.

34. (canceled)

35. A method of detecting two or more biomarkers, the method comprising: (a) obtaining a biological sample from a pregnant female; and (b) detecting whether said two or more biomarkers are present in said biological sample, wherein said two or more biomarkers comprises sex hormone-binding globulin (SHBG) and one or more features selected from the group consisting of afamin (AFAM), apolipoprotein C III (APOC3), complement C5 preproprotein (CO5) and chorionic somatomammotropin hormone (CSH).

36-44. (canceled)

45. The method of claim 35, wherein said detecting comprises mass spectrometry (MS).

46. The method of claim 45, wherein said MS comprises co-immunoprecipitation mass spectrometry (co-IP MS).

47. The method of claim 35, wherein said detecting comprises an assay that utilizes a capture agent, wherein said capture agent is selected from the group consisting of an antibody, antibody fragment, nucleic acid-based protein binding reagent, or small molecule.

48. The method of claim 47, wherein said assay is selected from the group consisting of enzyme immunoassay (EIA), enzyme-linked immunosorbent assay (ELISA), and radioimmunoassay (RIA).

49. The panel of claim 1, wherein said two or more biomarkers comprise: i. SHBG and AFAM;ii. SHBG, AFAM and CSH;iii. SHBG and APOC3;iv. SHBG, APOC3 and CSH; oriv. SHBG and CO5.

50. The method of claim 7, wherein said two or more biomarkers comprise: i. SHBG and AFAM;ii. SHBG, AFAM and CSH;iii. SHBG and APOC3;iv. SHBG, APOC3 and CSH; oriv. SHBG and CO5.

51. The method of claim 35, wherein said two or more biomarkers comprise: i. SHBG and AFAM;ii. SHBG, AFAM and CSH;iii. SHBG and APOC3;iv. SHBG, APOC3 and CSH; oriv. SHBG and CO5.

52. The panel of claim 1, wherein said panel comprises fragments or derivatives of said two or more biomarkers.

53. The panel of claim 52, wherein said fragments consist of the amino acid sequence: i. IALGGLLFPASNLR for the biomarker SHBG;ii. HFQNLGK for the biomarker AFAM;iii. ISLLLIESWLEPVR for the biomarker CSH;iv. GWVTDGFSSLK for the biomarker APOC3; orv. IEEIAAK for the biomarker CO5.