A NONREDUNDANT LIST DEVELOPED BY COMBINATION OF FOUR SEPARATE SOURCES*□ S

The Human Plasma ProteomeA NONREDUNDANT LIST DEVELOPED BY COMBINATION OF FOUR SEPARATE SOURCES*□S

N. Leigh Anderson‡§, Malu Polanski‡, Rembert Pieper¶�, Tina Gatlin¶�,Radhakrishna S. Tirumalai**, Thomas P. Conrads**, Timothy D. Veenstra**,Joshua N. Adkins‡‡, Joel G. Pounds‡‡, Richard Fagan§§, and Anna Lobley§§

We have merged four different views of the human plasmaproteome, based on different methodologies, into a singlenonredundant list of 1175 distinct gene products. Themethodologies used were 1) literature search for proteinsreported to occur in plasma or serum; 2) multidimensionalchromatography of proteins followed by two-dimensionalelectrophoresis and mass spectroscopy (MS) identifica-tion of resolved proteins; 3) tryptic digestion and multidi-mensional chromatography of peptides followed by MSidentification; and 4) tryptic digestion and multidimen-sional chromatography of peptides from low-molecular-mass plasma components followed by MS identification. Of1,175 nonredundant gene products, 195 were included inmore than one of the four input datasets. Only 46 appearedin all four. Predictions of signal sequence and transmem-brane domain occurrence, as well as Genome Ontologyannotation assignments, allowed characterization of thenonredundant list and comparison of the data sources. The“nonproteomic” literature (468 input proteins) is stronglybiased toward signal sequence-containing extracellularproteins, while the three proteomics methods showed amuch higher representation of cellular proteins, includingnuclear, cytoplasmic, and kinesin complex proteins. Cyto-kines and protein hormones were almost completely absentfrom the proteomics data (presumably due to low abun-dance), while categories like DNA-binding proteins werealmost entirely absent from the literature data (perhapsunexpected and therefore not sought). Most major catego-ries of proteins in the human proteome are represented inplasma, with the distribution at successively deeper layersshifting from mostly extracellular to a distribution more likethe whole (primarily cellular) proteome. The resulting non-redundant list confirms the presence of a number of inter-esting candidate marker proteins in plasma and serum.Molecular & Cellular Proteomics 3:311–326, 2004.

The human plasma proteome is likely to contain most, if notall, human proteins, as well as proteins derived from someviruses, bacteria, and fungi. Many of the human proteins,introduced by low-level tissue leakage, ought to be present atvery low concentrations (��pg/ml), while others, such as al-bumin, are present in very large amounts (��mg/ml). Numer-ous post-translationally modified forms of each protein arelikely to be present, along with literally millions of distinctclonal immunoglobulin (Ig)1 sequences. This complexity andenormous dynamic range make plasma the most difficultspecimen to be dealt with by proteomics (1).

At the same time, plasma is the most generally informativeproteome from a medical viewpoint. Almost all cells in thebody communicate with plasma directly or through extracel-lular or cerebrospinal fluids, and many release at least part oftheir contents into plasma upon damage or death. Somemedical conditions, such as myocardial infarction, are offi-cially defined based on the increase of a specific protein in theplasma (e.g. cardiac troponin-T), and it is difficult to argueconvincingly that there is any disease state that does notproduce some specific pattern of protein change in the body’sworking fluid. This immense diagnostic potential has spurreda rapid acceleration in the search for protein disease markersby a wide variety of proteomics strategies.

Current methods of proteomics are only beginning to cat-alog the contents of plasma. Two-dimensional electrophore-sis was able to resolve 40 distinct plasma proteins in 1976 (2),but, because of the dynamic range problem, this number hadonly grown to 60 in 1992 (3) and is substantially unchangedtoday, a quarter century later. It is now clear that more thantwo dimensions of conventional resolution are required toprogress beyond this point. Recently, several truly multidi-mensional survey efforts have been mounted, with the resultthat the number of distinct proteins detected has increaseddramatically. Additional dimensions of separation can be in-troduced at any of three levels: a) separation of intact pro-teins, either by specific binding (e.g. subtraction of definedhigh-abundance proteins) or continuous resolution (e.g. elec-trophoresis or chromatography); b) separation of peptides

From ‡The Plasma Proteome Institute, Washington DC 20009-3450; ¶Large Scale Biology Corporation, Proteomics Division, Ger-mantown, MD 20876; **Laboratory of Proteomics and AnalyticalTechnologies, SAIC-Frederick Inc., National Cancer Institute, Fred-erick, MD 21702-1201; ‡‡Biological Sciences Department, PacificNorthwest National Laboratory, Richland, WA 99352; and §§Inphar-matica Ltd., London, W1T 2NU, United Kingdom

Received, November 29, 2003, and in revised form, January 9,2004

Published, MCP Papers in Press, January 12, 2004, DOI10.1074/mcp.M300127-MCP200

1 The abbreviations used are: Ig, immunoglobulin; MS, mass spec-trometry; GO, Genome Ontology; 2DE, two-dimensional electro-phoresis; NR, nonredundant; TM, transmembrane; LC, liquid chroma-tography; MS/MS, tandem MS; IT, ion trap.

Research

© 2004 by The American Society for Biochemistry and Molecular Biology, Inc. Molecular & Cellular Proteomics 3.4 311This paper is available on line at http://www.mcponline.org

derived from plasma proteins, either by specific binding (e.g.capture by anti-peptide antibodies) or continuous resolution(e.g. chromatography); and c) separation of peptides, andparticularly their fragments, by mass spectrometry (MS).Many possible combinations of these dimensions can beimplemented, the only limitations being the effort, cost, andtime of analyzing many fractions or runs instead of one.

In this article, we have compared and combined data fromthree different multi-dimensional strategies with data from afourth, classical source (the protein biochemistry and clinicalchemistry literature) to provide a meta-level overview of boththe contents and the rate of discovery of new components inplasma. The three experimental datasets are derived from 1)whole protein separation by a three-dimensional process (im-munosubtraction/ion exchange/size exclusion) followed bytwo-dimensional electrophoresis (2DE) followed by MS iden-tification of resolved spots (4); 2) Ig subtraction followed bytrypsin digestion followed by two-dimensional liquid chroma-tography (LC) (ion exchange/reversed phase) followed by tan-dem MS (MS/MS) (5); and 3) molecular mass fractionation,followed by trypsin digestion followed by two-dimensional LC(cation exchange/reversed phase) followed by MS/MS (6).These three experimental approaches have two features incommon (the removal of most Igs, by specific subtraction orsize, and the use of MS for molecular identification) but oth-erwise they span the gamut of proteomics discovery ap-proaches: separation at the protein level, separation at thetryptic peptide level, and a hybrid.

Combining experimental data with literature search resultson proteins detected in plasma (representing a large body ofaccumulated “nonproteomics” data) should provide a broadperspective on plasma contents. Because the same proteinsdetected by various methods can be referred to by differentnames or accession numbers, we have used a sequence-based approach to eliminate redundancy and cluster all oc-currences of the same protein. The resulting list makes itpossible to examine the overlap between the various ap-proaches and to see whether they are biased toward partic-ular classes of proteins. In addition, a pooled nonredundantlist should provide a relatively unbiased survey of the kinds ofproteins present in plasma, which could have important diag-nostic implications. Finally, a large list of proteins actuallyobserved in plasma paves the way for top-down, targetedproteomics approaches to the discovery of disease markers:the development of accurate high-throughput specific assaysfor selected candidates from this list, as a supplement to the useof single methods for marker discovery in small sample sets. Inthe longer term, proteins with strong, mechanistic disease rela-tionships may be viable therapeutic candidates as well.

DATA SOURCES AND METHODS

Lit: Literature Search

Manual Medline searches were performed searching for titles orabstracts containing human plasma or serum proteins, excluding

articles on membranes, stimulation, drug, and dose. A total of 468entries were collected, of which 458 had a human sequence acces-sion number in one or more of the major databases.

2DEMS: Separation of Serum Proteins(LC3/2-DE) � MS/MS Identification

Intact proteins were fractionated by chromatography and 2DE andidentified by MS, generating the dataset described by Pieper et al. (7).Briefly, human blood sera were obtained in equal volumes from twohealthy male donors (ages 40 and 80). Albumin, haptoglobin, trans-ferrin, transthyretin, �-1-anti trypsin, �-1-acid glycoprotein, he-mopexin, and �-2-macroglobulin were removed by immunoaffinitychromatography. The immunoaffinity-subtracted serum concentratewas fractionated further by sequential anion exchange and size ex-clusion chromatography. The resulting 66 samples were individuallysubjected to 2DE. All visible Coomassie Blue R250 spots were cutout, destained, reduced, alkylated, and digested with trypsin. Allextracted peptides were analyzed by matrix-assisted laser desorp-tion/ionization time-of-flight (MALDI-TOF) on a Bruker Biflex orAutoflex mass spectrometer (Bruker, Billerica, MA) and searchedagainst Swiss-Prot. Those samples that did not give positive identi-fication by MALDI-TOF where subjected to LC-MS/MS analysis by iontrap (IT) MS (Thermo Finnegan LCQ, Woburn, MA) and searchedagainst the National Center for Biotechnology Information (NCBI)database using SEQUEST.

LCMS1: Separation of Peptide Digests of SerumMinus Ig (LC2) � MS/MS Identification

A published dataset prepared by Adkins et al., (5) was used. Briefly,human blood serum was obtained from a healthy anonymous femaledonor. Igs were depleted by affinity adsorption chromatography usingprotein A/G. The resulting Ig-depleted plasma was digested withtrypsin and separated by strong cation exchange on a polysulfoethylA column followed by reverse-phase separation on a capillary C18column. The capillary column was interfaced to an IT-MS (ThermoFinnigan LCQ Deca XP) using electrospray ionization. The IT-MS wasconfigured to perform MS/MS scans on the three most intense pre-cursor masses from a single MS scan. All samples were measuredover a mass/charge (m/z) range of 400–2,000, with fractions contain-ing high complexity being measured with segmented m/z ranges.Tandem mass spectra were analyzed by SEQUEST as describedusing the NCBI May 2002 database.

LCMS2: Separation of Peptide Digests of Low-Molecular-MassSerum Proteins (LC2) � MS/MS Identification

The fourth dataset is that described by Tirumalai et al. (6), focusedon the lower-molecular-mass plasma proteome. Briefly standard hu-man serum was purchased from the National Institute of Standardsand Technology. High-molecular-mass proteins were removed in thepresence of acetonitrile using Centriplus centrifugal filters with amolecular mass cutoff of 30 kDa. The low-molecular-mass filtrate wasreduced, alkylated, and digested with trypsin. The digested samplewas fractionated by strong cation exchange chromatography on apolysulfoethyl A column. Reversed-phase LC was subsequently per-formed on 300A Jupiter C-18 column coupled on line to an IT-MS(Thermo Finnegan LCQ Deca XP). Each full MS scan was followed bythree MS/MS scans where the three most abundant peptide molec-ular ions were selected. MS/MS spectra were searched against the ahuman protein database using SEQUEST.

Bioinformatics

Sequence Clustering—The Blastp protein comparison algorithm (8,9) was used to query the sequence of each protein identified against

Nonredundant Human Plasma Proteome

312 Molecular & Cellular Proteomics 3.4

a database containing the aggregate sequences of all proteins iden-tified by any method. Sequences sharing greater than 95% identityover an aligned region were grouped into “unique sequence clusters.”Sequences were unmasked, and the minimum alignment length con-sidered was 15 aa. This similarity-based approach was sufficient togroup identical sequences, sequence fragments, and splice variants.Annotation in the nonredundant table was reported for the “bestannotated” protein in the cluster set.

Signal Peptide Prediction—Signal peptides were predicted usingthe commercially available SignalP version 2.0 neural net and hiddenMarkov model (HMM) algorithms (10) and sigmask (11) signal mask-ing program developed as part of Inpharmatica’s Biopendium (12)protein annotation database. Each sequence received a score of �1for a statistically significant positive signal peptide prediction fromany of the three algorithms. The scores 0, 1, 2, and 3 for a particularsequence were then converted to qualitative terms “no,” “possiblesignal,” “signal,” or “signal confident,” respectively.

Transmembrane Prediction—Transmembrane (TM) regions werepredicted using the commercial version of TMHMM version 2.0 algo-rithm (13). The total number of TM helices predicted per sequencewas reported for each protein sequence. When a predicted TM regionoverlapped a predicted signal sequence (as it did in 40 cases inH_Plasma_NR_v2), this was interpreted as a signal sequence only.

Structural and Sequence-based Domain Annotation—Sequenceswere scanned against a library of BioPendium and iPSI-BLAST (9,11)-like protein profiles constructed from SCOP (14), PFAM (15),PRINTS (16), and PROSITE (17) domain families. Hits to these profileswere reported at a statistical e-value cut-off of 1e-5. This cut-off waschosen to maximize profile coverage and minimize the occurrence offalse positives. Sequences were not masked for low complexity orcoiled coils prior to profile scanning.

Gene Ontology (GO) Term Annotation—NCBI GI number acces-sions for the sequences were matched to their SPTR (18) equivalentsbased on sequences sharing �95% sequence identity over 90% ofthe query sequence length. GO (19) component, process, and func-tion terms were then extracted from text-based annotation files avail-able for download from the GO database ftp site: ftp.geneontology.org/pub/go/gene-associations/gene_association.goa_human. Forgraphical reporting, a series of GO terms in each category wereextracted by text searching of relevant keywords (indicated by thecategory names on plots) through all the assigned GO definitions. AGO component summary for the whole human proteome was pre-pared by applying the same approach to the complete GO humandatabase referred to above.

Database Assembly—The nonredundant (NR) plasma databasewas assembled as a series of tables in a PostgreSQL relationaldatabase and queried to derive summary statistics for tables andfigures shown here.

RESULTS

Number of Distinct Proteins Detected in Plasma, and theNature of Nonredundancy

Four sets of accession numbers for proteins occurring inplasma (468 from Lit, 319 from 2DEMS, 607 [reported as 490nonredundant accessions] from LCMS1, and 341 fromLCMS2) were combined to yield 1,735 total initial accessions(Table I). A total of 55 of the input accessions referred tononhuman sequences, and these were not considered furtherin the present analysis. A very conservative method of select-ing distinct proteins was used in order to avoid countingsequence variants, splice variants, or cleavage products ofone gene product as different: any sequences that shared a

region larger than 15 aa with greater than 95% sequenceidentity were assigned to the same cluster and reported as asingle entry in the nonredundant set. Fig. 1 shows one resultof applying these criteria, in this case resulting in the assign-ment of 10 initial accessions to a single cluster for haptoglo-bin, a major plasma protein found in all four initial datasetsand whose three separate subunit types are derived from asingle translation product. This case also highlights the gen-eral observation that not all datasets used the same primaryaccession database (NCBI GI, Swiss-Prot, or RefSeq as ex-amples). The largest cluster (109 “redundant” entries) is ac-counted for by Igs, where all the Ig heavy and light chains ofall types were clustered together as one entry arbitrarily cho-sen as S40354 (an Ig � chain sequence). Thus 6.2% of theinput accessions were Igs, despite the fact that each of theexperimental methods included steps to remove thesemolecules.

This approach is more conservative (fewer distinct proteinsreported) than the methods used in some of the input datasources, which accounts for the decrease in each set whenintra-set redundancy is removed (1,509 human accessionsremain). When inter-set redundancies are removed (makingthe full list nonredundant by the criteria described above), atotal of 1,175 distinct proteins remain. The entire nonredun-dant set, here abbreviated H_Plasma_NR_v2 (H_Plas-

FIG. 1. Diagram of redundant accessions for haptoglobin. Vari-ous database accessions related to haptoglobin are shown in therectangles, pentagon, and hexagons. The triangles represent theidentifications made in the input datasets. The dark filled trianglerepresents the accession number upon which all other accessionnumbers were collapsed to give a single nonredundant accession.

TABLE IProtein redundancy within and between datasets

The numbers accession numbers contributed by each data sourceand remaining after specific filter operations are shown.

Lit LCMS1 LCMS2 2DEMS Total

Beginning accessions 468 607 341 319 1735Minus nonhuman 458 580 330 312 1680Minus intrasource redundancy

and nonhuman accessions433 475 318 283 1509

Unique to source in NR 284 334 221 141 980Total combined NR list – – – – 1175


Molecular & Cellular Proteomics 3.4 313

ma_NR_v1 being Table I of Ref. 1), is provided as a supple-mental data table. Of these, a total of 980 occur in only onesource. Because so many entries occur only once, and giventhe non-zero frequency of false MS identifications, independ-ent confirmation will be required to validate most of this list astrue plasma components.

Protein Coverage by Data Source

Of the 1,175 nonredundant human proteins in H_Plas-ma_NR_v2, 195 entries, or 17%, were present in more thanone dataset (set H_Plasma_195: Fig. 2 and Table II). Only 46(4%) were found in all four sets of accessions (Total_sources � 4, shown in bold type in Table II). Of these only one(inter-� trypsin inhibitor heavy chain H1) is predicted to haveeven a single transmembrane domain, and only one (the he-moglobin � chain presumably released from red cell lysis) ispredicted not to have a signal sequence. These characteris-tics (presence of signal sequence and absence of transmem-brane domains) are those expected for major plasma proteinssecreted by organs such as the liver.

An additional 47 proteins (4%) were found in three of thefour datasets (Table II). Of these 47 proteins, only three werefound in all three experimental datasets but not the literaturedataset. These three proteins were pigment epithelium-de-rived factor; a nonmuscle myosin heavy chain; and secretory,extracellular matrix protein 1. Pigment epithelium-derived fac-tor and secretory, extracellular matrix protein 1 are both se-creted proteins (Swiss-Prot annotations), but upon furthersearching only pigment epithelium-derived factor has beenreported as plasma associated (20). Nonmuscle myosin is anintracellular protein (21) possibly derived from platelets. Theremaining 43 proteins seen in three datasets were all docu-mented plasma proteins.

A further 102 proteins (9%) were found in two datasets(Table II). Of these, 43 proteins were found in two experimen-tal datasets but not in the literature dataset. These include a

number of proteins that would not typically be thought of aslikely plasma components, including a chloride channel and acopper-transporting ATPase (with 10 and 7 predicted trans-membrane domains, respectively), an oxygen-regulated pro-tein, three hypothetical proteins, and a group of likely nuclearproteins including mismatch repair protein, mitotic kinesin-like protein 1, and centromere protein F.

The remaining 980 proteins (83% of NR) were found in onlyone of the four input datasets. Of these, 696 proteins (71%)were found only in the experimental sets, with LCMS1,LCMS2, and Lit having similar large percentages of source-unique proteins (70, 69, and 66%, respectively, versus 50%for 2DEMS).

Characterization of the Plasma Proteome ViaAnnotation Statistics

Predicted Signal Sequences—The signal sequence predic-tion algorithm used yielded four levels of likelihood: “no”(strong probability of no signal sequence), “possible signal,”“signal,” and “signal confident” (strong probability of a signalsequence) in order of increasing likelihood of a signal se-quence (i.e. the number of algorithms out of the three usedthat predict a signal sequence). The procedure does not dis-tinguish between the signal sequences of secreted and mem-brane-bound proteins (including e.g. plasma, Golgi, and mi-tochondrial membranes), and thus does not directly predictfinal protein location. Most of the 1,175 H_Plasma_NR_v2nonredundant sequences (83%) yielded a strong positive ornegative prediction (i.e. good agreement between the threeprediction algorithms used), with these two results occurringin about a 2:3 ratio overall. Approximately 49% of H_Plas-ma_NR_v2 had no evidence of a signal sequence, while onlyabout 25% of the H_Plasma_195 lacked such evidence; con-versely only 34% of H_Plasma_NR_v2 gave a “signal confi-dent” while 54% of H_Plasma_195 gave this signal. Compar-ing the four data sources over H_Plasma_195 (Fig. 3A), entriesfrom all four sources were likely to have signal sequences: theLit set had the highest bias toward “signal confident” proteins(indicated by a ratio of “signal confident” to “no” signal of5.05), while 2DEMS showed less bias (ratio of 3.44) andLCMS1 and LCMS2 showed a higher representation of “no”signal predictions (ratios of 2.03 and 1.96, respectively). Com-paring the four sources across all the 1,175 H_Plasma_NR_v2proteins (Fig. 3B), these ratios were reduced, reflecting agreater preponderance of “no” signal proteins, but the relativedifferences between the sources remained, yielding ratios forLit, 2DEMS, LCMS1, and LCMS2 of 3.85, 0.91, 0.49, and0.44, respectively.

Predicted Transmembrane Segments—The number of pre-dicted TM segments (0–21) is shown for each of the fourdatasets (Fig. 4) in comparison with the distribution summa-rizing The Human Membrane Protein Library (HMPL, cbs.umn.edu/human/info/hs.dat; Fig. 4B, line). The HMPL con-

FIG. 2. Diagram of proteins found in multiple datasets. All over-laps are shown (2-way, 3-way, and 4-way) for all four input datasets:Lit (dotted line), 2DEMS (dashed line), LCMS1 (solid line), and LCMS2(double solid line). Numbers represent the number of shared acces-sions in the respective overlapping areas.



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t0

Ap

olip

op

rote

inA

-IV

pre

curs

or

(Ap

o-A

IV)

P04

114

11

20

43

Sig

nalc

onfid

ent

0A

pol

ipop

rote

inB

-100

pre

curs

or(A

po

B-1

00)

[con

tain

s:ap

olip

opro

tein

B-4

8(A

po

B-4

8)]

P02

655

11

11

44

Sig

nalc

onf

iden

t0

Ap

olip

op

rote

inC

-II

pre

curs

or

(Ap

o-C

II)P

0265

61

11

14

4S

igna

lco

nfid

ent

0A

po

lipo

pro

tein

C-I

IIp

recu

rso

r(A

po

-CIII

)P

0509

01

11

14

4S

igna

lco

nfid

ent

0A

po

lipo

pro

tein

Dp

recu

rso

r(A

po

-D)

(ap

od

)P

0264

91

32

17

4S

igna

lco

nfid

ent

0A

po

lipo

pro

tein

Ep

recu

rso

r(A

po

-E)

Q13

790

11

11

44

Sig

nalc

onf

iden

t0

Ap

olip

op

rote

inF

pre

curs

or

(Ap

o-F

)O

1479

11

11

14

4S

igna

l0

Ap

olip

op

rote

inL1

pre

curs

or

(ap

olip

op

rote

inL-

I)(a

po

lipo

pro

tein

L)(a

po

l-I)

(Ap

o-L

)(a

po

l)P

0851

91

01

02

2S

igna

l0

Ap

olip

opro

tein

(a)

pre

curs

or(E

C3.

4.21

.�)

(Ap

o(a)

)(L

p(a

))P

0657

60

11

02

2P

ossi

ble

sign

al0

ATP

synt

hase

�ch

ain,

mito

chon

dria

lpre

curs

or(E

C3.

6.3.

14)

P01

160

10

10

22

Sig

nalc

onfid

ent

0A

tria

lnat

riure

ticfa

ctor

pre

curs

or(A

NF)

(atr

ialn

atriu

retic

pep

tide)

(AN

P)

(pre

pro

natr

iod

ilatin

)[c

onta

ins:

card

iod

ilatin

-rel

ated

pep

tide

(CD

P)]

P02

749

11

11

44

Sig

nalc

onf

iden

t0

�-2

-gly

cop

rote

inI

pre

curs

or

(ap

olip

op

rote

inH

)(A

po

-H)

(B2G

PI)

(�(2

)GP

I)(a

ctiv

ated

pro

tein

C-b

ind

ing

pro

tein

)(A

PC

inhi

bit

or)



TAB

LEII—

cont

inue

d

Acc

essi

onLi

t2D

EM

SLC

MS

1LC

MS

2To

tal_

acce

ssio

nsTo

tal_

sour

ces

Sig

nal

TMD

escr

iptio

n

P01

884

10

01

22

Sig

nalc

onfid

ent

0�

-2-m

icro

glob

ulin

pre

curs

orI3

9467

00

11

22

No

0B

ullo

usp

emp

higo

idan

tigen

,hu

man

(frag

men

t)P

0400

31

11

03

3S

igna

l0

C4b

-bin

din

gp

rote

in�

chai

np

recu

rsor

(c4b

p)

(pro

line-

rich

pro

tein

)(P

RP

)P

2085

11

10

02

2S

igna

lcon

fiden

t0

C4b

-bin

din

gp

rote

in�

chai

np

recu

rsor

P05

109

11

00

22

No

0C

algr

anul

inA

(Mig

ratio

nin

hib

itory

fact

or-r

elat

edp

rote

in8)

(MR

P-8

)(c

ystic

fibro

sis

antig

en)

(CFA

G)

(P8)

(leuk

ocyt

eL1

com

ple

xlig

htch

ain)

(S10

0ca

lciu

m-b

ind

ing

pro

tein

A8)

(cal

pro

tect

inL1

Lsu

bun

it)N

P_0

0172

90

11

02

2N

o0

Car

bon

ican

hyd

rase

I;ca

rbon

icd

ehyd

rata

se(H

omo

sap

iens

)P

2279

21

11

03

3N

o0

Car

box

ypep

tidas

eN

83-k

Da

chai

n(c

arb

oxyp

eptid

ase

Nre

gula

tory

sub

unit)

(frag

men

t)P

1516

91

10

02

2S

igna

l0

Car

box

ypep

tidas

eN

cata

lytic

chai

np

recu

rsor

(EC

3.4.

17.3

)(a

rgin

ine

carb

oxyp

eptid

ase)

(kin

ase

1)(s

erum

carb

oxyp

eptid

ase

N)

(SC

PN

)(a

nap

hyla

toxi

nin

activ

ator

)(p

lasm

aca

rbox

ypep

tidas

eB

)P

0733

91

10

02

2S

igna

lcon

fiden

t0

Cat

hep

sin

Dp

recu

rsor

(EC

3.4.

23.5

)P

0771

11

10

02

2S

igna

lcon

fiden

t0

Cat

hep

sin

Lp

recu

rsor

(EC

3.4.

22.1

5)(m

ajor

excr

eted

pro

tein

)(M

EP

)P

2577

40

10

12

2S

igna

lcon

fiden

t0

Cat

hep

sin

Sp

recu

rsor

(EC

3.4.

22.2

7)N

P_0

0518

50

01

12

2N

o0

CC

AA

T/en

hanc

erb

ind

ing

pro

tein

�,

inte

rleuk

in6-

dep

end

ent

O43

866

11

00

22

Sig

nalc

onfid

ent

0C

D5

antig

en-l

ike

pre

curs

or(S

P-�

)(C

T-2)

(igm

-ass

ocia

ted

pep

tide)

NP

_005

187

00

21

32

No

0C

entr

omer

ep

rote

inF

(350

/400

kd,

mito

sin)

;m

itosi

n;ce

ntro

mer

eP

0045

01

11

14

4S

igna

lco

nfid

ent

0C

erul

op

lasm

inp

recu

rso

r(E

C1.

16.3

.1)

(fer

roxi

das

e)N

P_0

0642

10

01

12

2N

o0

Cha

per

onin

cont

aini

ngTC

P1,

sub

unit

4(�

);ch

aper

onin

NP

_004

061

00

11

22

No

10C

hlor

ide

chan

nelK

a;ch

lorid

ech

anne

l,ki

dne

y,A

;hc

lc-K

a(H

omo

sap

iens

)P

0627

61

10

02

2S

igna

lcon

fiden

t1

Cho

lines

tera

sep

recu

rsor

(EC

3.1.

1.8)

(acy

lcho

line

acyl

hyd

rola

se)

(cho

line

este

rase

II)(b

utyr

ylch

olin

ees

tera

se)

(pse

udoc

holin

este

rase

)P

1090

91

11

14

4S

igna

lco

nfid

ent

0C

lust

erin

pre

curs

or

(co

mp

lem

ent-

asso

ciat

edp

rote

inS

P-4

0,40

)(c

om

ple

men

tcy

toly

sis

inhi

bit

or)

(CLI

)(N

A1

and

NA

2)(a

po

lipo

pro

tein

J)(A

po

-J)

(TR

PM

-2)

P00

740

11

01

33

Sig

nal

0C

oagu

latio

nfa

ctor

IXp

recu

rsor

(EC

3.4.

21.2

2)(C

hris

tmas

fact

or)

P12

259

10

11

33

Sig

nalc

onfid

ent

0C

oagu

latio

nfa

ctor

Vp

recu

rsor

(act

ivat

edp

rote

inC

cofa

ctor

)P

0045

11

01

02

2S

igna

l0

Coa

gula

tion

fact

orV

IIIp

recu

rsor

(pro

coag

ulan

tco

mp

onen

t)(a

ntih

emop

hilic

fact

or)

(AH

F)P

0074

21

10

02

2S

igna

lcon

fiden

t0

Coa

gula

tion

fact

orX

pre

curs

or(E

C3.

4.21

.6)

(Stu

art

fact

or)

P00

748

11

11

44

Sig

nalc

onf

iden

t0

Co

agul

atio

nfa

cto

rX

IIp

recu

rso

r(E

C3.

4.21

.38)

(Hag

eman

fact

or)

(HA

F)P

0048

81

10

13

3N

o0

Coa

gula

tion

fact

orX

IIIA

chai

np

recu

rsor

(EC

2.3.

2.13

)(p

rote

in-g

luta

min

e�-g

luta

myl

tran

sfer

ase

Ach

ain)

(tran

sglu

tam

inas

eA

chai

n)P

0516

01

11

03

3S

igna

lcon

fiden

t0

Coa

gula

tion

fact

orX

IIIB

chai

np

recu

rsor

(pro

tein

-glu

tam

ine

�-g

luta

myl

tran

sfer

ase

Bch

ain)

(tran

sglu

tam

inas

eB

chai

n)(fi

brin

stab

ilizi

ngfa

ctor

Bsu

bun

it)P

0246

21

01

13

3S

igna

l0

Col

lage

n�

1(IV

)ch

ain

pre

curs

orP

0073

61

11

14

4S

igna

lco

nfid

ent

0C

om

ple

men

tC

1rco

mp

one

ntp

recu

rso

rP

0987

11

11

14

4S

igna

lco

nfid

ent

0C

om

ple

men

tC

1sco

mp

one

ntp

recu

rso

r(E

C3.

4.21

.42)

(C1

este

rase

)P

0668

11

11

03

3S

igna

lcon

fiden

t0

Com

ple

men

tC

2p

recu

rsor

(EC

3.4.

21.4

3)(C

3/C

5co

nver

tase

)P

0102

41

11

14

4S

igna

lco

nfid

ent

0C

om

ple

men

tC

3p

recu

rso

r(c

ont

ains

C3a

anap

hyla

toxi

n)P

0102

81

12

15

4S

igna

l0

Co

mp

lem

ent

C4

pre

curs

or

(co

ntai

nsC

4Aan

aphy

lato

xin)

P01

031

11

10

33

Sig

nalc

onfid

ent

0C

omp

lem

ent

C5

pre

curs

or(c

onta

ins

C5a

anap

hyla

toxi

n)P

1367

11

11

14

4S

igna

l0

Co

mp

lem

ent

com

po

nent

C6

pre

curs

or

P10

643

11

10

33

Sig

nalc

onfid

ent

0C

omp

lem

ent

com

pon

ent

C7

pre

curs

orP

0735

71

11

14

4S

igna

lco

nfid

ent

0C

om

ple

men

tco

mp

one

ntC

8�

chai

np

recu

rso

rP

0735

81

11

03

3S

igna

lcon

fiden

t0

Com

ple

men

tco

mp

onen

tC

8�

chai

np

recu

rsor

P07

360

11

10

33

Sig

nalc

onfid

ent

0C

omp

lem

ent

com

pon

ent

C8

�ch

ain

pre

curs

or



TAB

LEII—

cont

inue

d

Acc

essi

onLi

t2D

EM

SLC

MS

1LC

MS

2To

tal_

acce

ssio

nsTo

tal_

sour

ces

Sig

nal

TMD

escr

iptio

n

P02

748

11

11

44

Sig

nalc

onf

iden

t0

Co

mp

lem

ent

com

po

nent

C9

pre

curs

or

P00

751

11

11

44

Sig

nal

0C

om

ple

men

tfa

cto

rB

pre

curs

or

(EC

3.4.

21.4

7)(C

3/C

5co

nver

tase

)(p

rop

erd

infa

cto

rB

)(g

lyci

ne-r

ich

�g

lyco

pro

tein

)(G

BG

)(P

BF2

)P

0860

31

12

04

3S

igna

lcon

fiden

t0

Com

ple

men

tfa

ctor

Hp

recu

rsor

(Hfa

ctor

1)A

4045

50

11

02

2N

o0

Com

ple

men

tfa

ctor

H-r

elat

edp

rote

in(c

lone

H36

–1)

pre

curs

or,

hum

an(fr

agm

ent)

P05

156

13

10

53

Sig

nal

0C

omp

lem

ent

fact

orI

pre

curs

or(E

C3.

4.21

.45)

(C3B

/C4B

inac

tivat

or)

P48

740

11

00

22

Sig

nalc

onfid

ent

0C

omp

lem

ent-

activ

atin

gco

mp

onen

tof

Ra-

reac

tive

fact

orp

recu

rsor

(EC

3.4.

21.�

)(R

a-re

activ

efa

ctor

serin

ep

rote

ase

p10

0)(r

arf)

(man

nan-

bin

din

gle

ctin

serin

ep

rote

ase

1)(m

anno

se-b

ind

ing

pro

tein

asso

ciat

edse

rine

pro

teas

e)(M

AS

P-1

)Q

0465

60

11

02

2N

o7

Cop

per

-tra

nsp

ortin

gA

TPas

e1

(EC

3.6.

3.4)

(cop

per

pum

p1)

(Men

kes

dis

ease

-ass

ocia

ted

pro

tein

)P

0818

51

11

03

3S

igna

l0

Cor

ticos

tero

id-b

ind

ing

glob

ulin

pre

curs

or(C

BG

)(tr

ansc

ortin

)P

0274

11

10

02

2S

igna

lcon

fiden

t0

C-r

eact

ive

pro

tein

pre

curs

orP

0673

21

10

02

2N

o0

Cre

atin

eki

nase

,M

chai

n(E

C2.

7.3.

2)(M

-CK

)P

4990

21

01

02

2N

o0

Cyt

osol

icp

urin

e5�

-nuc

leot

idas

e(E

C3.

1.3.

5)1D

SN

01

20

32

No

0D

60S

N-t

erm

inal

lob

ehu

man

lact

ofer

rinP

0917

21

10

02

2S

igna

lcon

fiden

t0

Dop

amin

e�

-mon

ooxy

gena

sep

recu

rsor

(EC

1.14

.17.

1)(d

opam

ine

�-h

ydro

xyla

se)

(DB

H)

JC25

210

01

12

2P

ossi

ble

sign

al1

End

othe

linco

nver

ting

enzy

me

(EC

3.4.

24.�

)1,

umb

ilica

lvei

nen

dot

helia

lcel

lfor

m,

hum

anN

P_0

0441

60

11

13

3S

igna

lcon

fiden

t0

Ext

race

llula

rm

atrix

pro

tein

1is

ofor

m1

pre

curs

or;

secr

etor

yP

0267

11

01

13

3S

igna

lcon

fiden

t0

Fib

rinog

en�

/�-E

chai

np

recu

rsor

(con

tain

sfib

rinop

eptid

eA

)P

0267

51

10

13

3S

igna

l0

Fib

rinog

en�

chai

np

recu

rsor

(con

tain

sfib

rinop

eptid

eB

)P

0275

11

13

16

4S

igna

lco

nfid

ent

0Fi

bro

nect

inp

recu

rso

r(F

N)

(co

ld-i

nso

lub

leg

lob

ulin

)(C

IG)

P23

142

11

01

33

Sig

nalc

onfid

ent

0Fi

bul

in-1

pre

curs

orO

7563

61

10

02

2S

igna

lcon

fiden

t0

Fico

lin3

pre

curs

or(c

olla

gen/

fibrin

ogen

dom

ain-

cont

aini

ngp

rote

in3)

(col

lage

n/fib

rinog

end

omai

n-co

ntai

ning

lect

in3

P35

)(H

akat

aan

tigen

)P

0122

51

10

02

2S

igna

l0

Folli

trop

in�

chai

np

recu

rsor

(folli

cle-

stim

ulat

ing

horm

one

�su

bun

it)(F

SH

-�)

(FS

H-B

)P

0910

41

10

02

2N

o0

Gam

ma

enol

ase

(EC

4.2.

1.11

)(2

-pho

spho

-D-g

lyce

rate

hyd

roly

ase)

(neu

rale

nola

se)

(NS

E)

(eno

lase

2)P

0639

61

11

14

4S

igna

lco

nfid

ent

0G

elso

linp

recu

rso

r,p

lasm

a(a

ctin

-dep

oly

mer

izin

gfa

cto

r)(A

DF)

(Bre

vin)

(AG

EL)

P14

136

11

00

22

No

0G

lialf

ibril

lary

acid

icp

rote

in,

astr

ocyt

e(G

FAP

)Q

0460

91

11

03

3P

ossi

ble

sign

al1

Glu

tam

ate

carb

oxyp

eptid

ase

II(E

C3.

4.17

.21)

A47

531

01

10

22

Pos

sib

lesi

gnal

1G

luta

myl

amin

opep

tidas

e(E

C3.

4.11

.7),

hum

anN

P_0

0149

40

11

02

2S

igna

l0

Gly

cosy

lpho

spha

tidyl

inos

itols

pec

ific

pho

spho

lipas

eD

1is

ofor

m1

AA

B34

872

00

52

72

No

0G

P12

0,IH

RP

�IT

Ihe

avy

chai

n-re

late

dp

rote

in(in

tern

alfr

agm

ent)

JW00

570

11

02

2N

o0

Gra

vin,

hum

anP

0073

73

33

110

4S

igna

lco

nfid

ent

0H

apto

glo

bin

-1p

recu

rso

rP

0192

21

00

12

2N

o0

Hem

oglo

bin

�ch

ain

P02

023

11

11

44

No

0H

emo

glo

bin

�ch

ain

P02

790

11

10

33

Sig

nalc

onfid

ent

0H

emop

exin

pre

curs

or(�

-1B

-gly

cop

rote

in)

P05

546

11

11

44

Sig

nalc

onf

iden

t0

Hep

arin

cofa

cto

rII

pre

curs

or

(HC

-II)

(pro

teas

ein

hib

ito

rle

user

pin

2)(H

LS2)

Q04

756

01

01

22

Sig

nalc

onfid

ent

0H

epat

ocyt

egr

owth

fact

orac

tivat

orp

recu

rsor

(EC

3.4.

21.�

)(H

GF

activ

ator

)(H

GFA

)Q

1452

01

01

02

2S

igna

l0

HG

Fac

tivat

orlik

ep

rote

in(h

yalu

rona

nb

ind

ing

pro

tein

2)P

0419

61

11

14

4S

igna

lco

nfid

ent

0H

isti

din

e-ri

chg

lyco

pro

tein

pre

curs

or

(his

tid

ine-

pro

line

rich

gly

cop

rote

in)

(HP

RG

)P

3115

10

11

02

2N

o0

Hum

anp

soria

sin

(s10

0a7)

T147

380

01

12

2P

ossi

ble

sign

al0

Hyp

othe

tical

pro

tein

dkf

zp56

4a24

16.1

,hu

man

(frag

men

t)



TAB

LEII—

cont

inue

d

Acc

essi

onLi

t2D

EM

SLC

MS

1LC

MS

2To

tal_

acce

ssio

nsTo

tal_

sour

ces

Sig

nal

TMD

escr

iptio

n

T087

720

01

12

2N

o0

Hyp

othe

tical

pro

tein

dkf

zp58

6m12

1.1,

hum

an(fr

agm

ent)

T000

630

01

12

2N

o0

Hyp

othe

tical

pro

tein

KIA

A04

37,

hum

an(fr

agm

ent)

S40

354

1114

786

109

4S

igna

l0

Imm

uno

glo

bul

in�

chai

n,hu

man

P01

591

11

10

33

Pos

sib

lesi

gnal

0Im

mun

oglo

bul

inJ

chai

nP

0847

61

01

02

2S

igna

lcon

fiden

t0

Inhi

bin

�A

chai

np

recu

rsor

(act

ivin

�-A

chai

n)(e

ryth

roid

diff

eren

tiatio

np

rote

in)

(ED

F)P

1793

61

00

12

2S

igna

lcon

fiden

t0

Insu

lin-l

ike

grow

thfa

ctor

bin

din

gp

rote

in3

pre

curs

or(IG

FBP

-3)

(IBP

-3)

(IGF-

bin

din

gp

rote

in3)

P24

593

10

11

33

Sig

nalc

onfid

ent

0In

sulin

-lik

egr

owth

fact

orb

ind

ing

pro

tein

5p

recu

rsor

(IGFB

P-5

)(IB

P-5

)(IG

F-b

ind

ing

pro

tein

5)P

3585

81

11

03

3S

igna

lcon

fiden

t0

Insu

lin-l

ike

grow

thfa

ctor

bin

din

gp

rote

inco

mp

lex

acid

lab

ilech

ain

pre

curs

or(A

LS)

P01

343

10

01

22

Sig

nal

0In

sulin

-lik

egr

owth

fact

orIA

pre

curs

or(IG

F-IA

)(s

omat

omed

inC

)P

1982

71

12

15

4S

igna

lco

nfid

ent

1In

ter-

�-t

ryp

sin

inhi

bit

or

heav

ych

ain

H1

pre

curs

or

(ITI

heav

ych

ain

H1)

(Inte

r-�

-in

hib

ito

rhe

avy

chai

n1)

(Inte

r-�

-try

psi

nin

hib

ito

rco

mp

lex

com

po

nent

III)

(ser

um-

der

ived

hyal

uro

nan-

asso

ciat

edp

rote

in)

(SH

AP

)P

1982

31

11

14

4S

igna

lco

nfid

ent

0In

ter-

�-t

ryp

sin

inhi

bit

or

heav

ych

ain

H2

pre

curs

or

(ITI

heav

ych

ain

H2)

(inte

r-�

-in

hib

ito

rhe

avy

chai

n2)

(inte

r-�

-try

psi

nin

hib

ito

rco

mp

lex

com

po

nent

II)(s

erum

-d

eriv

edhy

alur

ona

n-as

soci

ated

pro

tein

)(S

HA

P)

Q06

033

11

11

44

Sig

nal

0In

ter-

�-t

ryp

sin

inhi

bit

or

heav

ych

ain

H3

pre

curs

or

(ITI

heav

ych

ain

H3)

(Inte

r-�

-in

hib

ito

rhe

avy

chai

n3)

(ser

um-d

eriv

edhy

alur

ona

n-as

soci

ated

pro

tein

)(S

HA

P)

Q14

624

12

11

54

Sig

nal

0In

ter-

�-t

ryp

sin

inhi

bit

or

heav

ych

ain

H4

pre

curs

or

A33

481

10

10

22

No

0In

terf

eron

-ind

uced

vira

lres

ista

nce

pro

tein

mxa

,hu

man

P29

459

10

10

22

Sig

nalc

onfid

ent

0In

terle

ukin

-12

�ch

ain

pre

curs

or(IL

-12A

)(c

ytot

oxic

lym

pho

cyte

mat

urat

ion

fact

or35

-kD

asu

bun

it)(C

LMF

p35

)(N

Kce

llst

imul

ator

yfa

ctor

chai

n1)

(NK

SF1

)P

4093

31

00

12

2S

igna

l0

Inte

rleuk

in-1

5p

recu

rsor

(IL-1

5)P

0523

11

10

02

2S

igna

lcon

fiden

t0

Inte

rleuk

in-6

pre

curs

or(IL

-6)

(B-c

ells

timul

ator

yfa

ctor

2)(B

SF-

2)(in

terf

eron

�-2

)(h

ybrid

oma

grow

thfa

ctor

)P

C11

020

02

13

2N

o0

Ker

atin

10,

typ

eI,

cyto

skel

etal

(clo

neH

K51

),hu

man

(frag

men

t)N

P_0

0898

50

11

02

2N

o0

Kin

esin

fam

ilym

emb

er3A

;ki

nesi

nfa

mily

pro

tein

3A(H

omo

sap

iens

)P

0104

21

14

28

4S

igna

lco

nfid

ent

0K

inin

og

enp

recu

rso

r(�

-2-t

hio

lpro

tein

ase

inhi

bit

or)

(co

ntai

nsb

rad

ykin

in)

P02

750

11

10

33

Sig

nal

0Le

ucin

e-ric

h�

-2-g

lyco

pro

tein

pre

curs

or(L

RG

)P

1842

81

10

02

2S

igna

lcon

fiden

t0

Lip

opol

ysac

char

ide-

bin

din

gp

rote

inp

recu

rsor

(LB

P)

P07

195

11

00

22

No

0L-

lact

ate

deh

ydro

gena

seB

chai

n(E

C1.

1.1.

27)

(LD

H-B

)(L

DH

hear

tsu

bun

it)(L

DH

-H)

P51

884

01

10

22

Sig

nalc

onfid

ent

0Lu

mic

anp

recu

rsor

(ker

atan

sulfa

tep

rote

ogly

can

lum

ican

)(K

SP

Glu

mic

an)

NP

_005

920

10

10

22

Sig

nalc

onfid

ent

1M

elan

oma-

asso

ciat

edan

tigen

p97

isof

orm

1,p

recu

rsor

P10

636

10

10

22

No

0M

icro

tub

ule-

asso

ciat

edp

rote

in�

(neu

rofib

rilla

ryta

ngle

pro

tein

)(P

aire

dhe

lical

filam

ent-

�)(P

HF-

�)I3

7550

00

11

22

No

0M

ism

atch

rep

air

pro

tein

MS

H2,

hum

anQ

0224

10

01

12

2N

o0

Mito

ticki

nesi

n-lik

ep

rote

in-1

(kin

esin

-lik

ep

rote

in5)

P08

571

11

00

22

Sig

nalc

onfid

ent

0M

onoc

yte

diff

eren

tiatio

nan

tigen

CD

14p

recu

rsor

(mye

loid

cell-

spec

ific

leuc

ine-

rich

glyc

opro

tein

)P

3557

90

11

13

3N

o0

Myo

sin

heav

ych

ain,

nonm

uscl

ety

pe

A(c

ellu

lar

myo

sin

heav

ych

ain,

typ

eA

)(n

onm

uscl

em

yosi

nhe

avy

chai

n-A

)(N

MM

HC

-A)

P12

882

01

01

22

No

0M

yosi

nhe

avy

chai

n,sk

elet

alm

uscl

e,ad

ult

1(m

yosi

nhe

avy

chai

niix

/d)

(myh

c-iix

/d)

NP

_006

380

01

10

22

Sig

nalc

onfid

ent

1O

xyge

nre

gula

ted

pro

tein

pre

curs

or;

oxyg

enre

gula

ted

pro

tein

P01

270

11

00

22

Sig

nal

0P

arat

hyro

idho

rmon

ep

recu

rsor

(Par

athy

rin)

(PTH

)(P

arat

horm

one)



TAB

LEII—

cont

inue

d

Acc

essi

onLi

t2D

EM

SLC

MS

1LC

MS

2To

tal_

acce

ssio

nsTo

tal_

sour

ces

Sig

nal

TMD

escr

iptio

n

NP

_006

784

00

11

22

Pos

sib

lesi

gnal

0P

erox

ired

oxin

3;an

tioxi

dan

tp

rote

in1;

thio

red

oxin

-dep

end

ent

Q15

648

10

01

22

No

0P

erox

isom

ep

rolif

erat

or-a

ctiv

ated

rece

pto

rb

ind

ing

pro

tein

(PB

P)

(PP

AR

bin

din

gp

rote

in)

(thyr

oid

horm

one

rece

pto

r-as

soci

ated

pro

tein

com

ple

xco

mp

onen

tTR

AP

220)

(thyr

oid

rece

pto

rin

tera

ctin

gp

rote

in2)

(TR

IP2)

(p53

regu

lato

ryp

rote

inR

B18

A)

P04

180

11

00

22

Sig

nalc

onfid

ent

2P

hosp

hatid

ylch

olin

e-st

erol

acyl

tran

sfer

ase

pre

curs

or(E

C2.

3.1.

43)

(leci

thin

-cho

lest

erol

acyl

tran

sfer

ase)

(pho

spho

lipid

-cho

lest

erol

acyl

tran

sfer

ase)

NP

_001

074

01

10

22

No

0P

hosp

hod

iest

eras

e5A

isof

orm

1;cg

mp

-bin

din

gcg

mp

-sp

ecifi

cP

1525

91

11

03

3N

o0

Pho

spho

glyc

erat

em

utas

e2

(EC

5.4.

2.1)

(EC

5.4.

2.4)

(EC

3.1.

3.13

)(p

hosp

hogl

ycer

ate

mut

ase

isoz

yme

M)

(PG

AM

-M)

(BP

G-d

epen

den

tP

GA

M2)

(mus

cle-

spec

ific

pho

spho

glyc

erat

em

utas

e)N

P_0

0620

90

01

12

2N

o0

Pho

spho

inos

itid

e-3-

kina

se,

cata

lytic

,�

pol

ypep

tide

P36

955

01

11

33

Sig

nalc

onfid

ent

0P

igm

ent

epith

eliu

m-d

eriv

edfa

ctor

pre

curs

or(P

ED

F)(E

PC

-1)

P03

952

11

10

33

Sig

nal

0P

lasm

aka

llikr

ein

pre

curs

or(E

C3.

4.21

.34)

(pla

sma

pre

kalli

krei

n)(k

inin

ogen

in)

(Fle

tche

rfa

ctor

)P

0515

51

11

03

3S

igna

lcon

fiden

t0

Pla

sma

pro

teas

eC

1in

hib

itor

pre

curs

or(C

1In

h)(C

1Inh

)P

0275

31

11

03

3S

igna

lcon

fiden

t0

Pla

sma

retin

ol-b

ind

ing

pro

tein

pre

curs

or(P

RB

P)

(RB

P)

(PR

O22

22)

P05

154

11

10

33

Sig

nalc

onfid

ent

0P

lasm

ase

rine

pro

teas

ein

hib

itor

pre

curs

or(P

CI)

(pro

tein

Cin

hib

itor)

(pla

smin

ogen

activ

ator

inhi

bito

r-3)

(PA

I3)

(acr

osom

alse

rine

pro

teas

ein

hib

itor)

P05

121

11

00

22

Sig

nalc

onfid

ent

0P

lasm

inog

enac

tivat

orin

hib

itor-

1p

recu

rsor

(PA

I-1)

(end

othe

lialp

lasm

inog

enac

tivat

orin

hib

itor)

(PA

I)P

0074

71

12

15

4S

igna

l0

Pla

smin

og

enp

recu

rso

r(E

C3.

4.21

.7)

(co

ntai

nsan

gio

stat

in)

P02

775

10

01

22

Sig

nal

0P

late

let

bas

icp

rote

inp

recu

rsor

(PB

P)

(sm

alli

nduc

ible

cyto

kine

B7)

(CX

CL7

)P

0277

61

00

12

2S

igna

lcon

fiden

t0

Pla

tele

tfa

ctor

4p

recu

rsor

(PF-

4)(C

XC

L4)

(onc

osta

tinA

)(Ir

opla

ct)

P43

034

11

00

22

No

0P

late

let-

activ

atin

gfa

ctor

acet

ylhy

dro

lase

IB�

sub

unit

(EC

3.1.

1.47

)(P

AF

acet

ylhy

dro

lase

45kD

asu

bun

it)(P

AF-

AH

45-k

Da

sub

unit)

(PA

F-A

H�

)(P

AFA

H�

)(L

isse

ncep

haly

-1p

rote

in)

(LIS

-1)

NP

_006

197

00

11

22

Sig

nalc

onfid

ent

1P

late

let-

der

ived

grow

thfa

ctor

rece

pto

r�

pre

curs

or(H

omo

sap

iens

)N

P_0

0043

60

01

12

2N

o0

Ple

ctin

1,in

term

edia

tefil

amen

tb

ind

ing

pro

tein

500

kDa;

ple

ctin

1P

2074

21

11

03

3S

igna

lcon

fiden

t0

Pre

gnan

cyzo

nep

rote

inp

recu

rsor

P07

288

20

10

32

Sig

nalc

onfid

ent

0P

rost

ate-

spec

ific

antig

enp

recu

rsor

(EC

3.4.

21.7

7)(P

SA

)(�

-sem

inop

rote

in)

(kal

likre

in3)

(sem

enog

elas

e)(s

emin

in)

(P-3

0an

tigen

)P

0723

71

10

02

2S

igna

lcon

fiden

t0

Pro

tein

dis

ulfid

eis

omer

ase

pre

curs

or(P

DI)

(EC

5.3.

4.1)

(pro

lyl4

-hyd

roxy

lase

�su

bun

it)(c

ellu

lar

thyr

oid

horm

one

bin

din

gp

rote

in)

(P55

)N

P_0

0272

50

11

02

2N

o0

Pro

tein

kina

se,

cam

p-d

epen

den

t,re

gula

tory

,ty

pe

I,�

AA

B22

439

00

11

22

No

0P

rote

inty

rosi

nep

hosp

hata

se;

ptp

ase

(Hom

osa

pie

ns)

P00

734

11

21

54

Sig

nal

0P

roth

rom

bin

pre

curs

or

(EC

3.4.

21.5

)(c

oag

ulat

ion

fact

or

II)P

2261

42

00

13

2S

igna

lcon

fiden

t0

Put

ativ

ese

rum

amyl

oid

A-3

pro

tein

P04

626

10

10

22

Sig

nalc

onfid

ent

2R

ecep

tor

pro

tein

-tyr

osin

eki

nase

erb

b-2

pre

curs

or(E

C2.

7.1.

112)

(p18

5erb

b2)

(NE

Up

roto

-onc

ogen

e)(C

-erb

b-2

)(ty

rosi

neki

nase

-typ

ece

llsu

rfac

ere

cep

tor

HE

R2)

(MLN

19)

NP

_005

397

00

11

22

No

0R

ho-a

ssoc

iate

d,

coile

d-c

oilc

onta

inin

gp

rote

inki

nase

1;p

160r

ock

P49

908

10

10

22

Sig

nalc

onfid

ent

0S

elen

opro

tein

Pp

recu

rsor

(sep

)N

P_0

0620

60

11

02

2S

igna

lcon

fiden

t0

Ser

ine

(or

cyst

eine

)p

rote

inas

ein

hib

itor,

clad

eA

(�-1

)P

0278

71

11

14

4S

igna

lco

nfid

ent

0S

ero

tran

sfer

rin

pre

curs

or

(tra

nsfe

rrin

)(s

ider

op

hilin

)(�

-1-m

etal

bin

din

gg

lob

ulin

)(P

RO

1400

)P

0276

81

11

03

3S

igna

lcon

fiden

t0

Ser

umal

bum

inp

recu

rsor



TAB

LEII—

cont

inue

d

Acc

essi

onLi

t2D

EM

SLC

MS

1LC

MS

2To

tal_

acce

ssio

nsTo

tal_

sour

ces

Sig

nal

TMD

escr

iptio

n

P02

735

11

11

44

Sig

nalc

onf

iden

t0

Ser

umam

ylo

idA

pro

tein

pre

curs

or

(SA

A)

(co

ntai

nsam

ylo

idp

rote

inA

(am

ylo

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tains predictions for a total of 8,817 proteins having 2–38 TMhelices, of which 99.7% have 2–21 (covered by the rangeshown). About 7% of proteins of H_Plasma_195 (19 of 195)contained TM segments (Fig. 4A), whereas 18% of 1,175H_Plasma_NR_v2 proteins had TM segments (Fig. 4B). Inboth cases, most proteins predicted to have a TM segmenthad only one. The distribution of multiple TM segments gen-erally reflected the shape obtained for HMPL, including peaksat 7 and 12 TM segments. The Lit and 2DEMS datasetscontained few transmembrane proteins, whereas the LCMSmethods found more, particularly at higher TM segment num-bers. LCMS1 detected more proteins with TM segments thanLCMS2, which concentrated on smaller proteins.

The relationship between signal sequence and TM segmentpredictions across H_Plasma_NR_v2 is shown in Table III.Proteins with TM segments make up 11% of “no” signalsequence proteins, and 20% of “signal confident” proteins.The former group (�TM -Sig) shows a much higher represen-tation of multiple TM segments than the latter (�TM �Sig),including a majority of the 7- and 12-TM entries (not all ex-tracellular proteins or domains have a “classical” signal se-quence). Sig� proteins were much more likely (15%) thanSig- proteins (4.5%) to have a single TM segment, typical ofmany receptors.

GO Component Assignments—Fig. 5 presents a compari-

son of four different subsets of the human proteome begin-ning with the whole human proteome and continuing throughthe H_Plasma_NR and H_plasma_195 sets to those 46 seenin all four of our datasets. In this progression, there is a steadyincrease in the proportion of extracellular proteins and a steadydecrease in the proportions of membrane, mitochondrial, andnuclear proteins. Several categories appear at a higher propor-tion in H_Plasma_NR_v2 than in either of the smaller versions ofthe plasma proteome or the whole human proteome: the kinesincomplex and lysosomal and cytoskeletal proteins.

Fig. 6 uses the GO component assignments for H_Plas-ma_NR_v2 to further compare the four input datasets. The Litproteins are primarily derived from extracellular (50% of thetotal), membrane, and cytoplasmic categories. 2DEMS ac-cessions showed fewer extracellular and similar membraneand cytoplasmic entries, with substantial increases in a seriesof other categories including kinesin complex and nuclear.The two LCMS methods showed similar GO component dis-tributions, displaying a smaller proportion of extracellular andcytoplasmic entries and more nuclear and mitochondrial en-tries than 2DEMS. None of the methods has a distributionclose to that for the whole human proteome (Fig. 5A), as theywould if the MS identifications were random.

FIG. 3. Signal sequence predictions for different data sources.Signal sequences as predicted by three algorithms (SignalP version2.0 neural net, HMM algorithms, and sigmask signal masking pro-gram) in the NR plasma proteome. The four outcomes (no, possiblesignal, signal, signal confident) correspond to 0, 1, 2, or 3 methodspredicting a signal sequence. The Lit dataset is represented in black,LCMS2 is dark gray, LCMS1 is light gray, and 2DEMS is white. A,signal sequence predictions for proteins repeated in multiple datasets(H_Plasma_195). B, signal sequence predictions for nonredundantproteins (H_Plasma_NR_v2). Bar segments should be compared forrelative size between sources and outcomes, but total stacked barheight is not relevant because of redundancy between the componentsources.

FIG. 4. Numbers of transmembrane segments as predicted byTMHMM. The Lit dataset is represented by black bars, LCMS2 bydark gray bars, LCMS1 by light gray bars, and 2DEMS by white bars.The line represents the TM distribution for all proteins contained in theHuman Membrane Protein Library. The left-hand scale is for theexperimental dataset (0–1 segments) and proteins in the HumanMembrane Protein Library. The right-hand scale is for the experimen-tal dataset (2–21 segments; �10� enlarged relative to the left scale inorder to show smaller values). A, signal sequence predictions forproteins repeated in multiple datasets (H_Plasma_195). B, signal se-quence predictions for nonredundant proteins (H_Plasma_NR_v2).



GO Function Assignments—A set of eight summary func-tion categories were used to analyze aspects of function overH_Plasma_NR_v2 and to compare the four input datasets(Fig. 7). Proteins with cytokine and hormone activities, whichare generally expected to be present at low abundance, werepredominantly found in Lit only, while Lit contained almostnone of the proteins with DNA-binding activity. Among theother categories reported, the experimental methods per-formed similarly except for underrepresentation of receptorand DNA-binding proteins in 2DEMS.

GO Process Assignments—Eight selected summary proc-ess categories were also used to analyze dataset differencesover H_Plasma_NR_v2. As shown in Fig. 8, a number of majorGO process categories were more evenly represented acrossthe four datasets, though the representation of Lit proteinsseems increased relative to the other sources, possibly due tomore extensive process annotation of these molecules.

DISCUSSION

We have assembled an enlarged list of proteins observed inhuman plasma by combining three published experimentaldatasets, generated by different proteomics approaches, witha large set of proteins drawn from individual published reportson serum or plasma. Of the combined total of 1,680 humanprotein accession numbers, 1,175 were judged to be distinctproteins (defining set H_Plasma_NR_v2) using a very conserv-ative set of criteria (95% sequence identity over 15 or moreamino acid subsequence). As shown in Fig. 2, the overlapbetween the four sets is surprisingly small, with only 46 pro-teins occurring in all four sets, and only 195 proteins (Table II)

occurring in more than one set (i.e. confirmed by identificationusing at least two approaches). This result suggests the in-volvement of one or more of several factors in limiting theoverlap between different views of the plasma proteome: 1)the methods used may be different enough to expose quitedifferent subsets of proteins (particularly because only a frac-tion of peptides observed are typically subjected to MS/MSand identified); 2) the samples used, though all human serumor plasma, may be different in the rank order of medium- andlow-abundance protein components; or 3) identifications gen-erated by some proteomics approaches could suffer frommore or less random errors associated with mistaken MS/MShits. We believe the first two factors are likely to account forthe relatively small overlap, while the third should be a minimalinfluence for several reasons. First, the stringency of MSidentification criteria used (described in detail in the originalpublications on each dataset) was reasonably high in eachcase, and false-positive identifications should represent lessthan 5–10% of the entries.2 Second, the distribution of anno-tation characteristics observed over the 1,175 plasma NR setis quite different from the human proteome as a whole (seedataset results of Fig. 6 versus the whole proteome in Fig. 5A),which suggests that random human hits are not dominatingthe accessions. Finally, the sets all show fairly similar levels ofdifference between every pair (Fig. 2), indicating that noneappears to be a major outlier. The observed differences be-tween approaches represented here thus suggest that addingadditional methods, or even repetition of these methods,would substantially expand the plasma proteome list from thetotal we obtained.

The set of 195 accessions that occur in at least two of thefour datasets represents a confirmed list of targets that shouldbe accessible for routine measurement by multiple proteom-ics technologies in human plasma or serum: these proteinshave been detected by at least two methods in differentlaboratories. This set actually comprises more than 195 ob-servable protein subunits because of the collapse of multipleforms into a single entry: for example haptoglobin’s � and �

chains collapse onto one primary gene product (cleaved aftersynthesis to yield the individual chains), and all Ig chains (�and � light chains, and �, �, �, , and � heavy chains) arelumped onto one accession because of their sequence simi-larity. The fact that a total of 96 Ig accessions occurred in theexperimental input data (14 in 2DEMS, 76 in LCMS1, and 6in LCMS2, in addition to the 11 Ig entries in Lit) indicatesthat a substantial number of heterogeneous Ig sequencesremain in plasma/serum samples even after the treatmentsused in these studies to remove antibodies prior to digestion/fractionation.

H_Plasma_195 contains most of the expected high- andmedium-abundance plasma components, but also contains anumber of proteins that might not ordinarily be expected to be

2 J. N. Adkins, manuscript in preparation.

TABLE IIIRelationship between signal sequence and TM segment predictions

over the 1,175 proteins of H_Plasma_NR_v2

Predicted TMsegments

Signal prediction

No Possiblesignal Signal Signal

confident Total

0 514 51 85 318 9681 26 15 18 61 1202 3 4 5 8 203 1 4 – 1 64 1 – 2 2 55 1 2 1 – 46 2 1 – 1 47 9 3 2 3 178 2 1 – 3 69 1 – – – 1

10 2 1 – – 311 2 1 – – 312 6 2 1 – 913 – 1 – – 114 1 2 – – 315 – – – – 016 – – – – 017 1 – – – 118 – – – – 019 2 – – – 220 1 – – – 121 1 – – – 1

Total 576 88 114 397



abundant enough to appear in a “common component” list.These include adiponectin (involved in the control of fat me-tabolism and insulin sensitivity), atrial natriuretic factor (a po-tent vasoactive substance synthesized in mammalian atriaand thought to play a key role in cardiovascular homeostasis),various cathepsins (D, L, S), centromere protein F (involved inchromosome segregation during mitosis), creatine kinase Mchain (an abundant muscle enzyme), glial fibrilary acid protein(distinguishes astrocytes from other glial cells), psoriasin(S-100 family, highly up-regulated in psoriatic epidermis), in-terferon-induced viral-resistance protein MxA (confers resist-ance to influenza virus and vesicular stomatitis virus), mela-noma-associated antigen p97 (a proposed cancer markeralso expressed in multiple normal tissues), mismatch repairprotein MSH2 (involved in postreplication mismatch repair,and whose defective forms are the cause of hereditary non-polyposis colorectal cancer type 1), oxygen-regulated protein(which plays a pivotal role in cytoprotective cellular mecha-nisms triggered by oxygen deprivation), peroxisome prolifera-tor-activated receptor binding protein (which plays a role in

transcriptional coactivation), prostate-specific antigen (a pro-tease involved in the liquefaction of the seminal coagulum,and one of the few successful cancer diagnostics), seleno-protein P (contains selenocyteines encoded by the opalcodon, UGA), signal recognition particle receptor � subunit(an integral membrane protein ensuring, in conjunction withsrp, the correct targeting of the nascent secretory proteins tothe endoplasmic reticulum membrane system), squamous cellcarcinoma antigen 1 (which may act as a protease inhibitor tomodulate the host immune response against tumor cells), andV-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene hom-olog (the receptor for stem cell factor). A number of theseproteins have obvious relevance to important disease mech-anisms, and thus are of potential diagnostic value. CathepsinS, centromere protein F, psoriasin, mismatch repair proteinMSH2, oxygen-regulated protein, and signal recognition par-ticle receptor � subunit did not occur in the Lit accession list,but were rather found via detection in two of the experimentaldatasets.

Two types of protein features (signal sequences and TM

FIG. 5. Major GO__component categories for proteome subsets. The proportions of proteins assigned to major component categories areshown for the human proteome (A); the nonredundant protein set H_Plasma_NR_v2 (B); the H_Plasma_195 proteins seen in more than twodatasets (C); and the 46 proteins seen in all four datasets (D).



domains) were predicted from the H_Plasma_NR_v2 se-quences. These two parameters are somewhat related, be-cause transmembrane, as well as secreted, proteins are likelyto contain signal sequences. In the 1,175 proteins of H_Plas-ma_NR_v2, approximately one-third were confidently pre-dicted to contain signal sequences (34% overall, with 32%having 0 or 1 TM segments) as compared with 19% contain-ing signal sequences over the whole human proteome (andonly 10% containing signal sequences with 0 or 1 TM seg-ments; R.F., unpublished observation). H_Plasma_NR_v2 isthus substantially (�3:1) enriched in a set of proteins havingsignal sequences and 0 or 1 TM segments (compared with thegenome), which is consistent with the presence of a largenumber of classical secreted proteins. However, becausemore than half of H_Plasma_NR_v2 proteins do not contain asignal sequence, the total representation of nonsecreted mol-ecules (presumably cellular constituents) is high.

In the full NR set of 1,175 proteins (H_Plasma_NR_v2),several major groups of proteins occur in patterns that sug-

gest interesting biases between our four data sources. Atleast 10 transcription factors were observed in the experimen-tal sets (each by only a single method), and none of thesewere found in the Lit accession set. Similarly, proteins GO-annotated with a DNA-binding function were essentially ab-sent from the Lit set. In contrast, only 4 of 39 cytokines andgrowth factors included were found in any of the experimentaldatasets (IL-6, IL-12A, ciliary neurotrophic factor, and FGF-12), while 37 occurred in the Lit set. These results suggest thatwhile the experimental proteomics methods were not sensi-tive enough to detect most cytokines and hormones, they diddetect important classes of proteins not detected in literaturereports using targeted assay methods. On a more global level,the distribution of GO_component assignments (Fig. 6) showssubstantial differences overall between the set of proteinsfound in a literature search versus the three experimentalproteomics technologies. Predicted features of protein se-quence also show major source-related differences. Moststriking is the fact that the Lit set was strongly biased toward

FIG. 6. Major GO_component categories in H_Plasma_NR_v2 by data source. Major component categories (same as Fig. 5) are shownfor Lit (A), LCMS1 (B), LCMS2 (C), and 2DEMS (D) data sources.



proteins that were confidently predicted to have signal se-quences (ratio of “signal confident” to “no” of 3.85), while2DEMS showed little preference (0.91) and the LCMS meth-ods showed a moderate (2 to 1) bias toward proteins withoutsignal sequences (ratios of 0.49 and 0.44). The strong bias ofthe Lit set toward signal sequences is likely due to the greaterease with which these more soluble proteins can be isolated

and studied by biochemical techniques. Similarly, the differ-ence between 2DEMS and LCMS may be due to the failure ofmany less-soluble proteins to focus in the first (isoelectricfocusing) dimension of the 2DE procedure (e.g. intact mem-brane or very large proteins), or else to the presence ofnumerous isoforms that divide the protein among members ofa charge train, and thus decrease the limit of detection (e.g.heavily glycosylated extracellular domains cleaved frommembrane proteins). By placing fewer requirements on thebehavior of sample proteins prior to digestion, and by provid-ing identifications based on a few soluble peptides, the MS-based techniques provided a significantly less biased, thoughnot necessarily more complete, view of the plasma proteome.

Because the four input protein sets were of similar size, it isclear that the literature, viewed as an historical summary ofresearch on proteins in plasma, shows a bias toward secretedproteins and against investigation of cellular proteins in solu-tion in blood. This effect cannot be due to detection sensitivityalone, because the low-abundance cytokines, generally notdetected by the experimental proteomics methods, are ac-cessible via immunoassay and widely reported in the litera-ture. The bias may instead be due to a general skepticism thatdetectable amounts of many cellular proteins are being re-leased into plasma (absent some major cause of tissue dam-age), or to a view that cellular protein release, if it occurred,would not be especially informative. The present results, builton experimental studies of multiple groups, demonstrate thatmany (perhaps all) cellular proteins are present in plasma. Thedemonstrated utility of cardiac muscle protein markers asserum diagnostics for myocardial infraction provides a per-suasive argument that many may have diagnostic use.

The next major challenge thus becomes the systematicexploration of protein abundance and structural modificationin relation to disease, normal physiological processes, andtreatment effects. In a sense this shift can be seen as analo-gous to the current evolution in pharmaceutical target selec-tion: the genome has provided a wealth (in practical terms anoverabundance) of previously unknown therapeutic targets,creating a major challenge in selecting those that are “drug-gable” and specifically linked to disease mechanisms. A shift,in other words, from protein discovery to target validation. Inthe context of diagnostics based on proteins in blood, we nowhave in H_Plasma_NR_v2, and a growing body of other ex-perimental data, a substantial set of candidate disease mark-ers that can be detected in plasma. While these will continueto be supplemented by discovery techniques (22), the stage isset for systematic efforts to validate disease markers for near-term application in clinical trials, medium-term use in diseasedetection, staging, and therapy selection, and long-term usein population screening. While some of these proteins havebeen examined individually as potential markers and found tohave low sensitivity or specificity as individual tests, growingevidence indicates that these limitations may be overcomeusing fingerprints of change across panels of proteins that

FIG. 7. Major categories of GO_function assignments overH_Plasma_NR_v2 by data source. Cellular function categories wereextracted from GO_function annotations. The Lit dataset is repre-sented by black bars, LCMS2 by dark gray bars, LCMS1 by light graybars, and 2DEMS by white bars.

FIG. 8. Major GO_process categories over H_Plasma_NR_v2 bydata source. The Lit dataset is represented by black bars, LCMS2 bydark gray bars, LCMS1 by light gray bars, and 2DEMS by white bars.



together better represent patient status. Thus even the pres-ent H_Plasma_NR_v2 offers an abundance of candidates de-serving of measurement in selected clinical sample sets. Mak-ing such measurements, accurately and on a large scalepresents a series of technical challenges likely to requiresubstantial efforts for much of the next decade. The results ofsuch a “targeted” proteomics effort can transform diagnos-tics, improve therapy, and lead to substantial and neededimprovements in the economics of healthcare.

* The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be herebymarked “advertisement” in accordance with 18 U.S.C. Section 1734solely to indicate this fact.

□S The on-line version of this article (available at http://www.mcponline.org) contains supplemental materials.

§ To whom correspondence should be addressed: The PlasmaProteome Institute, P.O. Box 53450, Washington DC 20009-3450.Tel.: 301-72811451; Fax: 202-234-9175; E-mail: [email protected].

� Current address: The Institute for Genomic Research, 9712 Med-ical Center Drive, Rockville, MD 20850.

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A NONREDUNDANT LIST DEVELOPED BY COMBINATION OF FOUR SEPARATE SOURCES*□ S

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