hpcin

Targeting the Human Cancer Pathway ProteinInteraction Network by Structural Genomics*Yuanpeng Janet Huang, Dehua Hang, Long Jason Lu, Liang Tong,Mark B. Gerstein**, and Gaetano T. Montelione

Structural genomics provides an important approach forcharacterizing and understanding systems biology. As astep toward better integrating protein three-dimensional(3D) structural information in cancer systems biology, wehave constructed a Human Cancer Pathway Protein Inter-action Network (HCPIN) by analysis of several classicalcancer-associated signaling pathways and their physicalprotein-protein interactions. Many well known cancer-as-sociated proteins play central roles as hubs or bottle-necks in the HCPIN. At least half of HCPIN proteins areeither directly associated with or interact with multiplesignaling pathways. Although some 45% of residues inthese proteins are in sequence segments that meet crite-ria sufficient for approximate homology modeling (BasicLocal Alignment Search Tool (BLAST) E-value

sional (3D) structural information is increasingly being used forunderstanding evolution and the mechanisms of molecularfunction. 3D structure provides critical information connectingprotein sequence with molecular function. Although sequencealignments, which are broadly used by the molecular biologycommunity, provide useful suggestions about which residuesin homologous protein sequences are in corresponding posi-tions, 3D structure-based alignments provide the true deter-mination of corresponding residue positions (1820), whichmay be inaccurately identified by sequence alignment infor-mation alone especially in cases where the sequence conser-vation is weak. In favorable cases, protein structure can yieldinsights into mechanisms of enzyme activities and protein-ligand interactions. In addition, 3D structures of proteins in-volved in human disease can be used to discover and/oroptimize new pharmaceutical agents (21, 22).A complete understanding of molecular interactions re-

quires high resolution 3D structures as they provide keyatomic details about binding interfaces and information aboutstructural changes that accompany protein-protein interac-tions. Structural genomics is an international effort aimed atproviding 3D structures, either directly by x-ray crystallogra-phy or NMR spectroscopy or by homology modeling, for allproteins in nature (23). Such a comprehensive structure-func-tion database, containing experimental structures and homol-ogy models for hundreds of thousands of proteins, will accel-erate research in all areas of biomedicine (2426).Recently Xie and Bourne (27) have discussed the structural

coverage of human proteins grouped by the Enzyme Com-mission and the Gene Ontology classifications. This analysisprovides a valuable summary of the structural informationavailable for many human disease-related proteins and pro-vides guidance for protein target selection by structuralgenomics projects.As a component of this vision of structural genomics, we

have established the Human Cancer Pathway Protein Inter-action Network (HCPIN) database, a collection of human pro-teins that participate in cancer-associated signaling path-ways, and their protein-protein interactions. HCPIN (version1.0) includes 3000 proteins and 10,000 interactions.HCPIN integrates (embeds) pathway data with protein-proteininteraction data (17) and provides protein structure-functionannotations to inform cancer biology. The HCPIN Website,illustrated in Fig. 1, provides an extensive collection of exper-imental and homology models of proteins or domains asso-ciated with human cancers.Here we summarize the current 3D structural coverage of

HCPIN and present plans for targeting the remaining proteinsin this network for structural analysis. The Northeast Struc-tural Genomics Consortium (NESG) has selected proteinsfrom HCPIN for cloning, expression, purification, and 3Dstructure determination. This network-based target selectionapproach provides a framework not only for completing struc-tural coverage of a disease-associated protein interaction

network but also provides specific hypotheses regarding pro-tein interaction partners that can be tested by co-expression,co-crystallization, and 3D structure determination of the re-sulting protein-protein complexes (28, 29). The long rangegoal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins, thecorresponding protein-protein complexes, and their interac-tion network.

EXPERIMENTAL PROCEDURES

Database SearchesCell cycle progression, apoptosis, MAPK,Toll-like receptor, TGF-, phosphoinositide 3-kinase (PI3K), and JAK-STAT signal transduction pathways were downloaded from the KEGGdatabase (version 0.6, January 2006) (13). Protein-protein interactionsand multiprotein complexes were downloaded from the Human Pro-tein Reference Database (11) (09_13_05 release), which included16,000 proteins and 20,000 interactions. Interactions for all path-way proteins and also additional interactions between interactionproteins are included in the HCPIN. The list of 363 genes involved inhuman cancer was obtained from the Cancer Gene Census (CGC)Database (1). This list is exclusively restricted to genes in whichmutations that are reported are causally implicated in oncogenesis.We used an IPI human cross-reference file (release 3.12) (30) tocross-reference proteins from HCPIN, CGC, and Swiss-Prot (31).

HCPIN 3D structural coverage statistics is assessed by running aBLAST search against PDB sequences (February 2006) using theTargetDB search tool with standard default parameters. Disorderedresidues with missing coordinates for segments within otherwise welldetermined 3D structures are counted as structurally covered in ourstructural coverage statistics. HCPIN proteins with no cross-refer-enced Swiss-Prot ID are considered as not having verified genemodels and are excluded from structure statistical analysis.

Bioinformatics ProgramsSignalP v3.0 (32) and TMHMM v2.0 (33)were used for predicting secreted and transmembrane proteins. ThePfam domains are identified in the SwissPfam file provided from Pfamv19.0 (34, 35). The program COILS (36) was used to predict coiled coilregions. We labeled regions of low complexity by using the programSEG (37). Default options were used for all programs. An in-housePerl program was written to predict disordered regions based onmean charge and mean hydrophobicity (38).

Topology and Statistics AnalysisProgram Pajek was used fornetwork topology analysis (39). The program R was used for statisticsanalysis (40).

Homology Modeling and Structure Quality AssessmentHCPINhomology models are selected fromMODBASE (41) and/or built usingthe XPLOR homology modeling protocol of Homology Modeling Au-tomatically (HOMA) (42). If multiple models are available fromMODBASE, the model with highest sequence identity is selected byHCPIN. Structure quality reports for each of the experimental struc-tures and models were generated using the Protein Structure Valida-tion Software suite (43), which includes structure validation analysiswith ProsaII (44), Verify3D (45), Procheck (46), MolProbity (47), andother structure quality assessment tools. Over time, the homologymodel database of HCPIN will be updated and expanded.

The HCPIN Web-accessed DatabaseGeneration of Web pages(HTML) for the HCPIN server was done using Java and a relationaldatabase (MySQL). We recommendWeb browsers Firefox version 2.0or higher and Internet Explorer 7 or higher to provide full Java func-tionality. Ribbon diagrams were generated using PyMOL. We plan toupdate structure coverage annotation information weekly and updateHCPIN protein information every 4 months.

Human Cancer Pathway Protein Interaction Network

Molecular & Cellular Proteomics 7.10 2049

RESULTS


The HCPIN is a collection of proteins from cancer-associ-ated signaling pathways together with their protein-proteininteractions. The HCPIN version 1.0 was constructed by com-bining proteins from seven KEGG (13) classical cancer-asso-ciated signaling pathways together with protein-protein inter-action data from the HPRD (11). HPRD is a resource ofprotein-protein interaction information manually collectedfrom the literature and curated by expert biologists to reduceerrors (11). We used KEGG because of its high quality (48).Pathway interaction information from KEGG was excludedfrom HCPIN because of a lack of precise definitions (17).The seven pathways in this initial version of HCPIN include

(i) cell cycle progression, (ii) apoptosis, (iii) MAPK, (iv) innateimmune response (Toll-like receptor), (v) TGF-, (vi) PI3K, and(vii) JAK-STAT pathways. Many well known important cancer-associated proteins, such as p53 and NF-B, are associated

with at least one of these pathways. The current version ofHCPIN includes 2977 proteins and 9784 protein-protein inter-actions, including 240 multiprotein complexes each com-prised of at least three proteins (Table I).HCPIN proteins collected from the KEGG pathways are

called pathway proteins. Other HCPIN proteins that are notincluded in the KEGG pathways but interact with these path-way proteins are called interaction proteins. The representa-tion of protein complexes using a binary protein-protein inter-action graph remains a challenge because without detailedstructural studies it is often not possible to distinguish directphysical interactions from interactions mediated through thecomplex (49, 50). We used triangular pseudonodes, which linkproteins involved in the same complex, to represent multipro-tein complexes (50). These multiprotein complexes accountfor 1000 edges of the total 10,580 edges in the HCPIN.Table I summarizes other statistics of the HCPIN with andwithout these multiprotein complexes. Of 664 pathway pro-

FIG. 1. The HCPIN is a Web-accessible database. It is designed for use by cancer biologists interested in assessing 3D protein structuralinformation in the context of the protein interaction network. A, HCPIN home page. B, a snapshot of Networks view, visualizing protein-proteininteractions with structure annotations. The outside ring represents the percentage of structural coverage. Green ring, experimental model isavailable with99% sequence identities; yellow ring, homology model is available with80% sequence identities. The Website provides toolsfor interactive analysis of the HCPIN. C, a snapshot of Proteins view, listing sequence information and PDB BLAST hits, summarizing allstructural information available for the human HCPIN protein and its homologues and providing links to the corresponding PDB entries andother structure-function annotation information. D, a snapshot of Icon gallery, a collection of ribbon diagrams for each of the known structuresand the structural models in the HCPIN.


2050 Molecular & Cellular Proteomics 7.10

teins defined by KEGG, 150 have no annotated physical in-teractions in the HPRD. Some of these may be associatedwith the seven KEGG pathways by gene transcription or haveinteraction partners that are not yet identified or annotated inthe HPRD.The interaction data included in the current version of

HCPIN are a subset of the HPRD. Although including only15% of HPRD proteins, HCPIN accounts for about half ofthe protein-protein interactions in the HPRD (09_13_05 re-lease). Despite the fact that HCPIN represents only a portionof the signaling network of the human interactome, its degreeof distribution is similar to that of many other scale-free inter-actome networks (5156). The clustering coefficient in theHCPIN is better approximated by C(k) k1 than by a k-independent clustering coefficient C(k), which further indi-cates the modularity of HCPIN (51, 52, 57). Future expansionsand refinements of HCPIN will include cancer-related signal-ing pathways from other sources (15) as well as protein-protein interaction data from other manually curated sources(e.g. Database of Interacting Proteins (12); MINT, the molec-ular interaction database (58); or Reactome (14)). We envisionHCPIN as an evolving, curated resource of structure-functioninformation for the human cancer protein interactome.The Cancer Gene Census Database comprises 363 protein-

encoding human genes that are causally implicated in onco-genesis (1), defined here as CGC proteins. Among these 363CGC proteins, 186 CGC proteins are included in the HCPIN,and only 52 of these are pathway proteins. This high coverageof cancer genes in the HCPIN confirms that the cancer genesare heavily associated with signaling pathways and their in-teractions and also demonstrates that the seven pathwaysthat we selected for this analysis are central to cancer biology.This coverage may be increased by including additional cancer-related signaling pathways. Many HCPIN proteins that are fun-damental in cancer biology, such as Grb2, Jun, Src, etc., are notincluded in CGC, and many CGC proteins are not included inHCPIN because they are not characterized to date in the pro-tein-protein interaction literature covered by KEGG or HPRD.Network Centrality Measures Versus EssentialityThe de-

gree of a protein (node) is defined as the number of interac-tions in which a particular protein participates (vertex degree).The betweenness of a protein (vertex betweenness) measures

the number of non-redundant shortest paths going throughthis protein. Proteins with a high degree or high betweennessare central proteins, which are often critical for cell survival(5963). For many scale-free interaction networks, degreeand betweenness are highly correlated (63). Similarly strongcorrelations are observed for the HCPIN (Kendalls 0.79,p value 2.2e16). As can be seen in Fig. 2, top centralproteins of HCPIN with both high degree and high between-ness include key cancer-associated essential proteins, suchas p53, Grb2, Raf1, EGF receptor, and others. Fig. 2 alsoshows that proteins with high betweenness but low degreeare quite abundant, especially for CGC proteins (in red). Thissuggests that bottleneck proteins, like hub proteins, play es-sential biological roles; this is in agreement with previousobservations (6163).Cross-talk between Signaling PathwaysSignaling path-

ways interact with one another to form complex networks (64).The subnetwork of proteins in a specific pathway togetherwith their interaction partners forms a pathway interactionsubnet (also called embedded pathways (17)). Accordingly theseven core KEGG signaling pathways used to construct thisversion of HCPIN are associated with seven larger pathwayinteraction subnets. We have also estimated here the cross-talk of the seven signaling pathways by looking at the fre-quencies of specific proteins in (i) each of the seven signalingpathways and (ii) each of the seven associated pathway in-teraction subnets.We first analyzed the cross-talk between pathway proteins

associated with each of the seven KEGG signaling pathways.About 20% of all HCPIN pathway proteins are included inmore than one KEGG signaling pathway. Fig. 3A summarizesthe frequency of observing one pathway protein in multiplesignaling pathways. For example, the AKT family of paral-ogs, the PI3K family of paralogs, and the TNF protein areinvolved in four of the seven signaling pathways. Theuniqueness of particular proteins to particular KEGG path-ways differs for the different signaling pathways. Althoughsome 6070% of pathway proteins from either the innateimmune response or apoptosis pathways are directly asso-ciated with at least one other signaling pathway, for theother pathways studied only 30% of pathway proteins areassociated with more than one pathway.

TABLE IHCPIN statistics

HCPIN HCPIN pairwise protein-protein interaction onlya

Proteins/nodes 2,977 proteins (664 pathway proteins),240 multiprotein complexes

2,819 proteins

Interactions 9,784b 9,544Edges 10,583b (292 self-interaction loops) 95,44 (70 self-interaction loops)Diameterc (longest distance) 11 11Average distance 4.143 4.086

a One multiprotein complex is counted as one interaction. However, it is counted as multiple edges in the HCPIN graph.b Proteins from multiprotein complexes and pseudonodes are excluded for calculation.c Measured for the largest connected component.



We next analyzed the cross-talk between the pathway in-teraction subnets associated with each of the seven KEGGsignaling pathways by HPRD interaction data. Fig. 3B sum-marizes the frequency of observing one HCPIN protein inmultiple pathway interaction subnets. These data show thatHCPIN proteins are frequently shared between multiple path-way interaction subnets. Overall about 53% of HCPIN pro-teins are associated with more than one pathway interactionsubnet. In other words, more than half of the HCPIN proteinsare either directly associated with or interact with multiplesignaling pathways. Although only 20% of all pathway pro-

teins are directly associated with multiple (1) pathways (Fig.3A), 58% of pathway proteins are associated with multiplepathway interaction subnets (Fig. 3C). The percentage ofpathway proteins associated with multiple pathway interac-tion subnets (58%) is similar to the percentage of all HCPINproteins associated with these interaction subnets (53%); thecross-talk between pathways is mediated approximatelyequally by core pathway proteins and interaction proteins.Seven pathway proteins are involved in all seven pathway

interaction subnets (i.e. Raf1, a serine/threonine kinase; Stat1;Stat3; Rb; p53; CREB-binding protein; and TGFR1). Another

FIG. 2. Scatter plot of degree andbetweenness measures for HCPINproteins. Black, HCPIN proteins; red,proteins also listed in the Cancer GeneCensus Database (1). EGFR, EGF recep-tor; CREBBP, CREB-binding protein;DMPK, dystrophia myotonica proteinkinase; LCK, Lymphocyte cell-specificprotein-tyrosine kinase; MPL, myelopro-liferative leukemia protein; HSPCA, heatshock protein HSP 90-alpha.

FIG. 3. Cross-talk between pathways. A, frequency of observing one protein in one or more of the seven KEGG signaling pathways.20%of HCPIN pathway proteins are associated with two or more pathways. B, frequency of observing one HCPIN protein in one or more of sevenpathway interaction subnets. 50% of HCPIN proteins are associated with two or more interaction subnets. C, frequency of observing onepathway protein in one or more pathway interaction subnets. The frequencies (17) are also labeled on the side of these pie charts.



seven interaction proteins (i.e. proteins in the interaction sub-net that are not core pathway proteins) are included in allseven pathway interaction subnets (i.e. tyrosine kinase Lyn,estrogen receptor , -catenin, insulin receptor, casein kinaseII, Hsp90-, and Sam68). These proteins associated with allseven interaction subnets play central roles in cancer biology.

Structural Coverage of HCPIN Proteins

The accuracy of homology models is largely determined bythe percent sequence identity with the template 3D structureupon which the model is based (43, 65). Models built at3050% sequence identity with the template (a mediumaccuracy modeling level) tend to have 90% of the mainchain modeled within 1.5- root mean square deviations fromthe correct structure but with frequent side-chain packing,core distortion, and loop conformation errors (65, 66). Homol-ogy models built with more than 50% sequence identity tendto have about 1.0- root mean square deviation from correctstructures for the main-chain atoms, with larger deviations forside-chain packing (66). Our goal is to characterize the struc-tural coverage of the HCPIN using high quality experimentalstructures or accurate models, especially for enzyme activesites, based on structural templates with BLAST E-value106 and sequence identity 80% (a high accuracy mod-

eling level). Although this cutoff is somewhat arbitrary, modelsgenerated from such templates will usually be of high reliabil-ity and accuracy. Such high quality structures or models ofthese human proteins are potentially useful for active sitedocking, studying catalytic mechanism, and designing li-gands useful for drug discovery (67).We have estimated the structural coverage of HCPIN at

bothmedium accuracy modeling level (defined here as BLASTE-value 106) and high accuracy modeling level (definedhere as BLAST E-value 106 and at least 80% sequenceidentity). Human protein sequence information has been an-notated by different experimental and computational methodsand stored in different databases with various levels of genemodel accuracy (30). Alternative splice sites, translation initi-ation sites, and other gene modeling issues complicate theprotein sequence annotation process (68). Swiss-Prot is ahigh quality manually annotated protein knowledgebase (69).About 78% of HCPIN protein sequences (2328 sequences)can be validated by Swiss-Prot (IPI v3.12) gene model anno-tations (30). The structural coverage statistics discussed hereare for only these 2328 protein sequences that can be verifiedby Swiss-Prot data.Table II summarizes the structural coverage of HCPIN pro-

teins at medium and high accuracy homology modeling lev-

TABLE IIStructural coverage of Swiss-Prot-validated proteins from the seven KEGG signaling pathways of the HCPIN

Total structuralcoverage

Secreted/membraneprotein structural

coverage

Intracellular proteinstructural coverage

No. %SDa %Res.b %No.c %SD %Res. %No. %SD %Res.

Medium accuracy homology modeling level (BLASTE-value 106)

Apoptosis 72 94 72 19 100 78 81 93 71TGF 79 90 56 52 90 59 48 89 54PI3K 81 85 51 12 70 28 88 87 56Cell cycle 82 76 36 4 100 31 96 75 36TLRd 88 93 71 45 95 77 55 92 69JAK-STAT 139 78 54 59 76 49 41 81 58MAPK 216 94 64 25 98 70 75 92 63HCPIN pathway proteins 600 86 55 33 85 56 67 86 55Interaction proteins 1728 76 42 26 75 47 74 76 41Total 2328 78 45 28 78 49 72 78 44

High accuracy homology modeling level (BLASTE-value 106 and 80% sequence identity)

Apoptosis 72 68 36 19 64 32 81 69 36TGF 79 62 29 52 59 26 48 66 32PI3K 81 36 13 12 10 5 88 39 15Cell cycle 82 46 20 4 100 30 96 44 20TLR 88 68 36 45 75 38 55 63 35JAK-STAT 139 55 32 59 59 32 41 51 32MAPK 216 60 33 25 60 36 75 60 32HCPIN

pathway proteins600 52 25 33 54 26 67 51 25

Interaction protein 1728 44 18 26 39 16 74 46 19Total 2328 46 20 28 44 18 72 47 20

a The percentage of pathway proteins with single domain structural coverage.b The number of residues covered by PDB hit divided by total length of proteins in the pathways. Residues predicted to be low complexity

or coiled coil are not counted in the denominator.c The percentage of proteins in the pathways that are predicted to be secreted or have integral or one-pass transmembrane domains.d TLR, Toll-like receptor.



els. At the medium accuracy level, about 86% of Swiss-Prot-verified proteins from the seven HCPIN pathways (pathwayproteins) have at least one domain with structural informationavailable from the PDB. These proteins are defined as havingsingle domain coverage (27), i.e. either an experimental struc-ture or a structure template useful for medium accuracy mod-eling of at least part of the protein structure. These structuresand models cover about 55% of residues in HCPIN (definedhere as residue coverage), excluding predicted low complex-ity and coiled coil regions. Interestingly innate immune re-sponse and apoptosis pathways, which are heavily involved inpathway cross-talk, also have the highest residue coverage(70%). At the high accuracy modeling level, the structuralcoverage of pathway proteins is much lower; only 52% havesingle domain coverage with 25% of residues covered. Thesestructural coverage statistics are upper bounds because thisanalysis excludes the 20% of proteins in HCPIN for whichprotein coding sequences cannot be verified by the Swiss-Prot (IPI v3.12) database.The single domain and residue coverage of the interaction

proteins, which are included in the seven pathway interactionsubnets but not in the seven KEGG pathways, is much lowerthan for pathway proteins, 76 and 42%, respectively, at me-dium accuracy level and 44 and 18%, respectively, at highaccuracy level. These coverage statistics reflect the traditionalbias of targeting core signaling pathway proteins in structuralbiology projects. Overall HCPIN has 78% (45%), 46% (20%)single domain (residue) coverage at medium and high accu-racy modeling levels, respectively. This single domain cover-age of HCPIN proteins is significantly higher than the esti-mated average single domain coverage of the humanproteome (27).We have annotated the 3D structural coverage of all HCPIN

proteins in the network diagrams provided on the HCPINWebsite (Fig 1B). These Web-based graph representationsprovide direct interactive global views of the 3D structuralcoverage of these pathway protein interaction networks. Theoutside ring on each node represents the percentage of theresidue coverage of the protein.HCPIN DomainsDomains are the evolutionary modular

building blocks of proteins. Experimental protein structuredetermination processes using x-ray crystallography or NMRspectroscopy are generally domain-oriented. Pfam is a man-ually curated database of protein domain families derivedfrom sequenced genomes (34). There are 1000 PfamA do-mains identified in HCPIN with size ranging from 50 to 1000residues. At medium level modeling accuracy, 53% of HCPINPfamA domain families have complete fold coverage (i.e. atleast one member of the domain family has essentially com-plete 3D structural coverage), whereas 35% of HCPIN domainfamilies have no fold coverage at all. About 10% of Pfamdomain families in HCPIN have partial fold coverage; i.e. a 3Dstructure is available for part of the predicted Pfam domain.This reflects inherent differences between sequence align-

ment-based domain boundaries used in Pfam and the actualstructural domain boundaries.Some 10% of HCPIN domains occur at least 10 times in the

set of HCPIN proteins. The most abundant domain in HCPINis the collagen domain (appearing 265 times), which occursfrequently in extracellular structural proteins involved in for-mation of connective tissue. Other frequently occurring do-main types include Pkinase, zinc finger C2H2, and WD40domains.Table III summarizes the top 2% most abundant domain

types in the HCPIN together with their 3D structural coveragestatistics. All 21 of these most abundant domain families havecomplete fold coverage in that at least a medium levelaccuracy model or experimental structure is available for thefull sequence of the domain.Modeling coverage, defined hereas the percentage of domain members in HCPIN that can bemodeled at high level modeling accuracy, is also summarizedfor these domain families in Table III. The frequently occurringdomain families of intracellular proteins listed in Table III haverelatively high modeling coverage. For example, the SH2, anintracellular signaling domain, has the highest modeling cov-erage, 58%. Experimental structures are available for fewermembers of the frequently occurring secreted and mem-brane-associated domains listed in Table III, resulting in lowermodeling coverage of these domain families. Progress incompleting the HCPIN modeling coverage for these mostabundant domains of HCPIN will provide a comprehensiveunderstanding across the domain family of their structure-function relationships.The HCPIN Structure GalleryThe HCPIN Website in-

cludes over 1000 protein or domain structure models of whichtwo-thirds are experimental structures from the PDB (withgreater than 99% sequence identity to the human HCPINprotein or protein domain) and one-third are homology mod-els built with structural templates having BLAST E-values106 and at least 80% sequence identities (Fig. 1D). Todate, the NESG structural genomics project has determined3D structure of 10 human proteins or domains targeted fromthe HCPIN; some of these are shown in Fig. 4.

HCPIN Target Selection for Structural Genomics

With the goal of providing high accuracy structural mod-els of disease-associated human proteins, especially en-zymes, our homology models of HCPIN proteins require atemplate protein of known 3D structure with pairwiseBLAST E-value 106 and 80% sequence identity withthe target protein (67). As discussed above, our structurecoverage analysis shows that significant experimental ef-forts in x-ray crystallography and/or NMR spectroscopy arestill needed to complete the structure coverage of theHCPIN at this high accuracy modeling level. Accordinglythese structurally uncovered regions (defined at this highaccuracy level) of HCPIN proteins have been selected for



sample production and structure analysis efforts by theNortheast Structural Genomics Consortium.Are HCPIN Proteins Suitable for Structural Genomics Ef-

forts?Because of limitations of current protein structureproduction technologies, it is generally more challenging todetermine 3D structures of eukaryotic proteins or of secreted,

integral membrane, or one-pass transmembrane proteinscompared with intracellular proteins. Integral membrane pro-teins are particularly challenging to produce for 3D structureanalysis. About 10% of HCPIN intracellular proteins have100% residue coverage at high accuracy level, whereas only2% of HCPIN proteins predicted to be secreted and/ormembrane-associated have such complete coverage (Fig.5A). However, considering the HCPIN proteins with only par-tial structural coverage, our analysis shows that pathway pro-teins predicted to be secreted and/or membrane-associated(e.g. soluble domains of one-pass transmembrane proteins)have similar single domain and residue coverage comparedwith intracellular proteins (Table II). These statistics suggestthat structural genomics should not only target domains fromintracellular proteins but also the domain families of extracel-lular secreted and/or extracellular domains of one-pass trans-membrane human proteins of the HCPIN.Size limitations are a concern for structural genomics that

require large scale protein sample production and are partic-ularly relevant for structural NMR studies. Protein sampleproduction is generally more successful for proteins of 600residues. NMR studies usually require samples with 180residues. For this reason, we also analyzed size distributionsof Swiss-Prot-validated HCPIN protein chains (Fig. 5). Theaverage full-length HCPIN protein is about 600 residues. Thesize distribution of predicted intracellular proteins is similar tothe size distributions of predicted secreted and membrane-associated proteins (Fig. 5B). Size distributions are also sim-ilar for proteins with and without structural single domain

TABLE IIIMost frequently occurring HCPIN domains

Frq, frequency; seq id, sequence identity; PH, pleckstrin homology; zf, zinc finger; Ank, ankyrin.

Pfam domain name FrqModeling coveragea

(E-value 106 and 80% seq id)Molecular function

Collagen 265 0.01 Extracellular structural proteinsPkinase 184 0.31 Protein kinasezf-C2H2 176 0.17 Nucleic acid bindingWD40 173 0.23 Multiprotein complex assembliesLRR_1 148 0.22 Leucine-rich repeat, protein-protein interactionAnk 145 0.40 Protein-protein interaction motiffn3 134 0.18 Cell surface binding, signalingEGF 122 0.12 EGF-like domainSH3_1 104 0.46 Signal transduction related to cytoskeletal organizationLdl_recept_b 101 0.05 Low density lipoprotein receptor repeat class BTPR_1 99 0.32 Protein-protein interactionEGF_CA 94 0.10 Calcium-binding EGF domainIQ 81 0.00 Calmodulin-binding motifefhand 79 0.47 Calcium-binding domainig 77 0.27 Immunoglobulin domainLdl_recept_a 77 0.13 Low density lipoprotein receptor repeat class ASH2 74 0.58 Intracellular signalingPkinase_Tyr 73 0.41 Protein-tyrosine kinaseFilamin 72 0.03 Actin cross-linking proteinPH 65 0.26 Cytoskeleton, intracellular signalingSpectrin 64 0.08 Cytoskeletal structure

a The percentage of domain members that can be modeled at BLAST E-value 106 and 80% sequence identity.

FIG. 4. Ribbon diagram of some HCPIN proteins/domainssolved by NESG. At the bottom of each ribbon diagram, we havelisted Swiss-Prot (SW) name, NESG target ID, PDB ID, residue cov-erage, and method used for structure determination.



coverage (Fig. 5B). Even very large proteins contain domainswith some structural coverage that in most cases have beenstudied by expressing segments of the protein sequenceconstituting one or a few structural domains. Residue struc-tural coverage distributions (Fig. 5C) are also similar for pre-dicted intracellular, secreted, and/or membrane-associatedHCPIN proteins with an average coverage of 110 residues.These size statistics are within the size limitation ranges thatare currently addressed well by structural genomics efforts,supporting the feasibility of including these HCPIN proteins astargets of the Northeast Structural Genomics Consortium.Target Selection ProcessFig. 6 shows details of our target

selection process. HCPIN v1.0 consists of 664 pathway pro-teins identified from KEGG together with an additional 2313interaction proteins from HPRD. 2328 of these 2977 HCPINproteins are validated by Swiss-Prot (IPI v3.12) (30). For eachamino acid sequence of those validated proteins, we filteredout regions that are not suitable for high throughput struc-tural genomics efforts, including regions with low complex-ity, those predicted to be coiled coils, or those predicted tobe largely disordered (38). We have identified 1160 intracel-lular proteins that have regions/domains suitable for suchhigh throughput structural genomics efforts. Domains fromsecreted or membrane proteins have also been targeted aspart of technology development projects but with lowerpriorities.

Fig. 5D shows the size distribution of these targeted intracel-lular proteins. Although the size of the full-length targeted pro-teins varies, about 75% of targeted regions/domains have lessthan 300 residues. These protein targets are publicly accessible.Efforts have begun to clone, express, purify, and characterizethese 1160 human proteins and protein domains. We haveprioritized these targets mainly based on high throughput fea-sibility rather than other factors such as molecular and cellularfunctions. In addition, we prioritize for sample production andstructure analysis hub and bottleneck proteins with high net-work degree and/or betweenness measures. The network an-notations in the HCPIN database also provide biological andbioinformatics information that is being used on a case-by-casebasis to prioritize particular protein targets.

DISCUSSION

Protein Sample Production ConcernsProtein sample pro-duction is challenging for the HCPIN proteins for severalreasons. Cloning and expression of certain human proteins inEscherichia coli can be difficult or impossible. Many of thesesignaling proteins have multiple domains, evolved to conveybiological signals from different inputs, and require reliabletechniques for domain parsing. In addition, these cancer-associated signaling networks include significant numbers ofproteins with extensive disordered regions, which are inher-ently challenging for expression, purification, and structure

FIG. 5. A, percent residue coveragedistributions for HCPIN proteins. Intra-cellular, proteins inside the cell; s/m,proteins predicted to be secreted orhaving at least a segment that is inte-gral or transmembrane. B, box plots ofsize distributions of HCPIN proteinsand HCPIN proteins with single domaincoverage. Intracellular, proteins insidethe cell; s/m, as defined above; intra-cellular-SD, intracellular proteins withsingle domain coverage, s/m-SD, pro-teins predicted to be secreted or hav-ing at least a segment that is trans-membrane with single domaincoverage. C, box plots of size distribu-tions of HCPIN proteins with residuecoverage. Intracellular-residue ands/m-residue, residue coverage of intra-cellular proteins and predicted secret-ed/membrane-associated proteins, re-spectively. Single domain and residuecoverages are shown at high accuracylevel. A similar distribution is observedat medium accuracy level. D, box plotsof size distributions of full-length andtargeted subregions of proteins se-lected by the NESG structural genom-ics project.



determination (70, 71). Large macromolecular complexes notonly require larger amounts of material but also a precise andcoordinated assembly of the different subunits, conditionsthat are often not easy to reproduce in vitro (28).Despite the challenges, there are certain technical advan-

tages of targeting an extensive protein interaction network likethe HCPIN. Many proteins that fail expression when produced

alone can be expressed, purified, and crystallized by co-expressing and co-purifying them with their interacting part-ner proteins (29). We are taking advantage of this approachwith HCPIN targets as potential partners for co-expressionand co-purification are indicated from the network. Althoughcancer-associated signaling networks are likely to includesignificant numbers of proteins with extensive disordered re-gions (70, 71), such disordered regions may become orderedupon binding to their protein partners, making the corre-sponding complexes suitable for high throughput structuralgenomics (7275).General Strategy for Targeting Proteins from Pathway-

based Interaction NetworksWe propose a general strategyto select targets from pathway-based interaction networks.This target selection strategy can be applied to any biochem-ical pathway of interest. Previously reported target selectionstrategies for structural genomics have focused on family (76,77), whole genome (7880), pathways (25, 67), and com-plexes (28, 29). The target selection strategy we present herecombines the selection strategies that have been proposedfor structural genomics of biochemical pathways (25, 67) andof protein-protein complexes (28, 29).First, lists of proteins involved in a specific biological path-

way are collected. These proteins are called pathway pro-teins. Interaction proteins are then identified, including thosethat potentially interact directly with any pathway proteinsor contribute to multiprotein complexes formed with path-way proteins. Interactions can be derived from the literature,curated peer-reviewed databases (1114, 58), high through-put protein interaction experiments (5356), and/or inte-grated prediction methods (81, 82). Gene models for bothpathway and interaction proteins are then validated usingSwiss-Prot (83). Protein sequences not verified by Swiss-Prot will require further analysis to confirm their authenticity.Regions of proteins with known 3D structure informationfrom PDB are identified. Regions of proteins not coveredwith 3D structure information and also suitable for highthroughput structural determination are then selected asstructural genomics targets with emphasis on hub and bot-tleneck proteins. This BioNet target selection strategy notonly provides a systematic approach for complete structurecoverage for disease-associated pathways (25, 67) but alsoprovides a framework for studying protein interactions andcomplexes (28, 29).Community OutreachSince the era of genome sequenc-

ing, biologists now use protein sequence information exten-sively. However, the general biological community uses muchless structural information. The HCPIN Website (Fig. 1) is builtto make structural information about cancer-related proteinseasily accessible to cancer biologists. Our future plan forHCPIN includes mapping single nucleotide polymorphism/mutation information, protein-protein interactions, and vari-ous structural bioinformatics predictions onto the 3D struc-tures; adding gene ontology and structure-based functional

FIG. 6.HCPIN target selection process. SEG regions, low complex-ity regions predicted by the program SEG (37). SignalP region, signalingpeptide predicted by SignalP (32). TM region, transmembrane regionpredicted by TMHMM (33). C/U-region, structure covered (C) or uncov-ered (U) region. T-region, targeted region. Disordered regions are pre-dicted based on mean hydrophobicity and net charge (38). E, E-value;HTP, high throughput.



annotation; and incorporating microarray and protein expres-sion data. We envision HCPIN as an evolving, curated re-source of structure-function information for the human cancerprotein interactome.Many intermediate results, such as expression constructs

and biochemical reagents, generated in these ongoing struc-tural genomics efforts are freely available to the biology com-munity. Our structure-function database can be leveraged bymany other related initiatives. For example, the National Can-cer Institutes Initiative for Chemical Genetics aims to system-atically identify perturbational small molecules for each can-cer-related protein coded in the human genome (84). Ourstructural genomics efforts on HCPIN will provide biochemicaland structural information as well as key reagents, organizedat a single Website, beneficial for such chemical geneticsstudies (85).HCPIN website: http://nmr.cabm.rutgers.edu/hcpin

AcknowledgmentsWe thank Drs. T. Acton, J. Everett, C. Gelinas,A. Rabson, and E. White for helpful discussions and comments on themanuscript.

* This work was supported, in whole or in part, by National Insti-tutes of Health Grant U54-074958 from the NIGMS Protein StructureInitiative. The costs of publication of this article were defrayed in partby the payment of page charges. This article must therefore be herebymarked advertisement in accordance with 18 U.S.C. Section 1734solely to indicate this fact. Present address: Division of Biomedical Informatics, Cincinnati

Childrens Hospital Medical Center, 3333 Burnet Ave., Cincinnati,OH 45229. To whom correspondence should be addressed. E-mail:

[email protected].

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