Interrogating protein interaction networks through structural biology Patrick Aloy and Robert B. Russell * European Molecular Biology Laboratory -LRB- EMBL -RRB- , Postfach 10 22 09 , D-69012 , Heidelberg , Germany Communicated by I. M. Gelfand , Rutgers , The State University of New Jersey-New Brunswick , Highland Park , NJ , March 13 , 2002 -LRB- received for review November 16 , 2001 -RRB- Protein protein interactions are central to most biological processes . Although much recent effort has been put into methods to identify interacting partners , there has been a limited focus on how these interactions compare with those known from three-dimensional -LRB- 3D -RRB- structures . Because comparison of protein interactions often involves considering homologous , but not identical , proteins , a key issue is whether proteins that are homologous to an interacting pair will interact in the same way , or interact at all . Accordingly , we describe a method to test putative interactions on complexes of known 3D structure . Given a 3D complex and alignments of homologues of the interacting proteins , we assess the fit of any possible interacting pair on the complex by using empirical potentials . For studies of interacting protein families that show different specificities , the method provides a ranking of interacting pairs useful for prioritizing experiments . We evaluate the method on interacting families of proteins with multiple complex structures . We then consider the fibroblast growth factor receptor system and explore the intersection between complexes of known structure and interactions proposed between yeast proteins by methods such as two-hybrids . We provide confirmation for several interactions , in addition to suggesting molecular details of how they occur . databank suggests considerable variation in the interaction partners preferred by particular protein families -LRB- 14 -RRB- . Clearly detailed studies are required to understand when it is possible to infer an interaction between proteins when one is known to occur between homologues . Here , we present a method to model putative interactions on known 3D complexes and to assess the compatibility of a proposed protein protein interaction with such a complex . After identifying the residues that make atomic contacts in a known crystallographic complex , we look to homologues of both interacting proteins to see whether these interactions are preserved by means of empirical potentials . This method permits us to score all possible pairs between two protein families , and say which are likely to interact . We apply the method to the fibroblast growth factor receptor system , and explore the intersection between all complexes of known 3D structure and interactions between yeast proteins proposed by methods such as two-hybrids . We demonstrate and discuss the importance of incorporating 3D structure information into studies of protein protein interactions . Methods Overview of the Methodology . Our initial analysis of complexes of major goal of functional genomics is to determine protein interaction networks for whole organisms . Large-scale studies have identified hundreds of potentially interacting proteins or complexes in yeast -LRB- 1 3 -RRB- . Computational methods have used gene fusion -LRB- 4 , 5 -RRB- , gene order -LRB- 6 -RRB- , phyletic distribution -LRB- 7 -RRB- , or a combination of approaches -LRB- 5 -RRB- to predict functional associations -LRB- and putative interactions -RRB- between thousands of proteins , and efforts are underway to catalog interaction data contained within the literature -LRB- 8 , 9 -RRB- . Despite attempts to identify putative interactions , little attention has been paid to one of the best sources of protein interaction data : complexes of known three-dimensional -LRB- 3D -RRB- structure . Decades of x-ray crystallography have produced hundreds of structures for protein complexes , and these structures provide a rich source of data for learning principles of how proteins interact and for validating interactions determined by other methods . Although the number of complexes of known 3D structure is relatively small , it is possible to expand this set by considering homologous proteins . Any interaction , whether known from two-hybrids , crystallography , or another method , will typically involve two or more proteins that themselves are members of homologous families . A major problem in genome annotation efforts is to understand when it is possible to transfer functional information , determined by experiment , from one protein to its homologues . Recent years have seen progress in predicting whether details such as enzymatic specificity or binding sites can be extrapolated to other members of a protein family -LRB- e.g. , refs . 10 and 11 -RRB- , although similar studies on protein protein interactions have been limited . It is not known whether it is generally possible to say that proteins homologous to a known interacting pair will interact in the same way , or indeed interact at all . For example , cytokines in the same family can be either promiscuous or highly specific regarding the receptors they prefer -LRB- e.g. , ref . 12 -RRB- , and some homologues do not bind receptors at all -LRB- e.g. , ref . 13 -RRB- . Moreover , analysis of interactions within the protein 5896 5901 PNAS April 30 , 2002 vol . 99 no. 9 A known 3D structure showed that interactions between proteins occurred through a variety of main-chain and side-chain contacts -LRB- Fig. 1 -RRB- as described in part previously -LRB- 15 -RRB- . This prompted us to derive empirical potentials for amino acids to be involved in particular side-chain to side-chain and side-chain to main-chain contacts at protein protein interfaces -LRB- see below -RRB- . For a complex of known structure , we identify residues making atomic contacts at the interface of the two proteins . We then use the potentials to score the compatibility of any homologous intraspecies pair of sequences , and calculate a statistical confidence based on a comparison to scores for a background of sequences that are unlikely to make favorable complexes -LRB- see below -RRB- . In the sections that follow , we refer to scores as being significant if their probability to occur by chance is 0.01 or 0.1 when compared with the background . Database of 3D Protein Complexes . To construct a nonredundant database of interacting protein domains , we used BLAST2 -LRB- 16 -RRB- to compare sequences from representatives of the SCOP database -LRB- 17 -RRB- of protein structures to those from the Pfam database of protein domain families -LRB- 18 -RRB- . We defined links between the two databases as matches with an expectation E-value 0.01 . We then searched for instances where different Pfam domains matched different chains that were in contact -LRB- any atom distance 5 -RRB- in the same structure . We manually grouped the final , nonredundant set of 356 unique interacting pairs of different Pfam families into broad classes : -LRB- i -RRB- enzyme inhibitors ; -LRB- ii -RRB- cytokine receptors ; -LRB- iii -RRB- signaling Abbreviations : 3D , three-dimensional ; FGF , fibroblast growth factor ; FGFR , FGF receptor ; CKS , cyclin-dependent kinase regulatory subunit . * To whom reprint requests should be addressed . E-mail : russell@embl-heidelberg.de . The publication costs of this article were defrayed in part by page charge payment . This article must therefore be hereby marked `` advertisement '' in accordance with 18 U.S.C. 1734 solely to indicate this fact . www.pnas.org cgi doi 10.1073 pnas .092147999 where na and nb are the total number of amino acids a and b and T is the total number of interacting pairs . Eab is the molar expected frequency for the a b pair , Cab is the number of observed contacts between residues a and b and Sab the log-odds score . We assess the compatibility of a potential complex by evaluating a total score as the sum of Sab values for all interacting residue pairs identified in the crystallographic complex . Sab values are evaluated only if Cab 0 and Eab 5 to ensure sufficient data . Otherwise Sab is set to 0.5 and 1.0 for side-chain to main-chain and side-chain to side-chain potential , respectively , which are approximately the negative of the largest values . High scoring complexes are evaluated by this function to be more favorable . an interface contains a sufficient number and type of contacts to be used further , we generated 1,000 random sequences for each interacting protein , based on surface residue frequencies . We scored them on the known complexes by using the empirical potentials on the interacting residues identified from the crystallographic complex . We used random sequences because we could not systematically extract information about noninteracting pairs of proteins homologous to a known complex , because such information is scant and subjective . Only when the known complex gave a score with Z 2.3 -LRB- 2.3 standard deviations above the mean ; significance 10 3 -RRB- did we consider it suitable for further use . This process effectively removes complexes where there are few interactions or that are mediated mainly through main-chain to mainchain contacts . To test the method , we extracted 59 pairs of homologous , nonidentical complexes of known structure , and scored one complex by using the other . We ignored immune system complexes as different interacting partners are expected -LRB- e.g. , immunoglobulins -RRB- . To ensure the integrity of the potentials we performed the test as a jack-knife -LRB- leave-one-out -RRB- procedure during this process . Evaluating Potential Interactions . Given a new potential interaction , we first assign the two components to one or more Pfam domains by using BLAST2 to see whether any complex 3D structure for this pair exists in the database described above . If one or more complexes are found , we align the new sequences to their homologues of known structure , and apply the empirical potentials and statistical significance to assess the fit of the interaction . Assigning Statistical Significance and Evaluation . To assess whether Fig. 1 . Interaction types across the four classes . complexes ; -LRB- iv -RRB- hetero-multimers ; -LRB- v -RRB- immune system complexes -LRB- i.e. , antibody antigen or MHC complexes -RRB- ; and -LRB- vi -RRB- other protein protein interactions . Derivation of Empirical Potentials for Protein Protein Interactions . S ab log10 Cab Eab Eab T na 20 a 1 na nb 20 b 1 nb Table 1 . Accuracy of the method on interacting families of proteins with multiple complex structures Domain 1 Signaling GRhoGAP RhoGDI Cytokines Receptors FGF receptor Peptidases Inhibitors Peptidase S8 TIMP Trypsin Trypsin Trypsin Trypsin Other Colicin pyocin Elongation factor TU % Id range -LRB- interface -RRB- 97 -LRB- 88 -RRB- 99 100 -LRB- 90 100 -RRB- 87 -LRB- 96 -RRB- 71 99 -LRB- 62 100 -RRB- 40 84 -LRB- 35 84 -RRB- 42 -LRB- 36 -RRB- 27 99 -LRB- 22 100 -RRB- 12 100 -LRB- 10 100 -RRB- 85 -LRB- 88 -RRB- 65 100 -LRB- 61 100 -RRB- 56 -LRB- 30 -RRB- 69 -LRB- 100 -RRB- Domain 2 Guanylate cyclase ras ras FGF Potato inhibitor Peptidase M10 Kunitz BPTI Kazal Kringle Squash HNH endonuclease Elongation factor TS % Id range -LRB- interface -RRB- 99 -LRB- 100 -RRB- 50 100 -LRB- 82 100 -RRB- 51 -LRB- 78 -RRB- 53 100 -LRB- 60 100 -RRB- 36 100 -LRB- 38 100 -RRB- 47 -LRB- 50 -RRB- 38 100 -LRB- 28 100 -RRB- 27 100 -LRB- 0 100 -RRB- 80 -LRB- 83 -RRB- 57 100 -LRB- 44 100 -RRB- 47 -LRB- 17 -RRB- 27 -LRB- 14 -RRB- Nc 2 4 2 4 4 2 10 10 2 6 2 2 T 16 2 12 2 12 12 172 12 2 70 20 4 24 2 2 N A P 0.01 16 2 12 2 12 12 83 5 2 21 2 4 12 1 1 N A P 0.1 16 2 12 2 12 12 112 10 2 37 3 4 19 1 1 N A Domain 1 Domain 2 show the two interacting families . % Id range , Overall -LRB- and interface -RRB- sequence identity ranges between the proteins in the family . Nc , Number nonidentical complexes of known structure for each family . T , Number of predictions for which the template complex gave significant scores . P 0.01 and P 0.1 , Number of pairs identified with significances of 0.01 and 0.1 , respectively . N A , Not applicable . Aloy and Russell PNAS April 30 , 2002 vol . 99 no. 9 5897 BIOCHEMISTRY Empirically derived potentials incorporate thermodynamic effects without explicitly having to model proteins , which makes them useful in protein fold-recognition and protein protein docking -LRB- e.g. , refs . 19 and 20 -RRB- . We derived main-chain to side-chain and side-chain to side-chain potentials from the complexes above . We defined interacting residues by requiring one or more of : hydrogen bonds -LRB- NOO distances 3.5 -RRB- , salt bridges -LRB- NOO distances 5.5 -RRB- , or van de Waals interactions -LRB- COC distances 5 -RRB- . We also require a relative accessibility of the unbound proteins 10 % to exclude buried side-chains . We found a total of 4,517 side-chain to main-chain and 3,316 side-chain to side-chain contacts . We then defined the empirical potentials by using a molar-fraction random state model based on the observed tendency of residues to be on protein surfaces : data from the protein databank -LRB- 20 -RRB- -LRB- http : www.rcsb.org -RRB- and yeast protein protein interactions from the Munich Information Center for Protein Sequences Database -LRB- MIPS ; ref . 21 ; http : mips.gsf.de -RRB- , which collates interactions from a number of sources -LSB- including the Yeast Proteome Database , YPD -LRB- 22 -RRB- -RSB- . Data from two large-scale yeast two-hybrid experiments -LRB- 1 , 2 -RRB- were taken from the web sites http : depts.washington.edu sfields yp project and http : genome.c.kanazawa-u . ac.jp Y2H . All data used in this study are available at http : www.russell . embl-heidelberg . de add info . Results and Discussion Evaluation of the Method . To evaluate the ability of the method to Data Sources and Availability . We obtained protein 3D coordinate predict whether a pair of proteins homologous to a known complex could interact , we identified all examples of similar , but not identical , complex 3D structures . Essentially , these are examples where two or more 3D structures are known for interactions between the same homologous families . For example , structures are known for two different trypsin homologues -LRB- trypsin and proteinase B -RRB- in contact with different `` Kunitz '' inhibitors -LRB- tryptase and ovomucoid inhibitor -RRB- . The examples were diverse , coming from 20 different protein family pairs . For clarity , we divided them into classes : cytokine receptor , signaling , peptidase inhibitor , and other -LRB- see Methods -RRB- . First , we found that , on average , 70 % of pairs of residues in contact were common to these homologous complexes -LRB- cytokine receptor 92 % ; signaling 89 % ; peptidase inhibitor 59 % ; other 66 % -RRB- and thus might , in the absence of a known structure , be identified by homology . We then used the potentials to score one complex by using the other ; the results are summarized in Table 1 . For cytokine receptor , signaling , and other systems , all modeled complexes have a significant score , compared with 65 % for peptidases and inhibitors . The poorer results for the peptidase inhibitor complexes are likely because they interact via many main-chain to main-chain contacts -LRB- see ref . 15 and Fig. 1 -RRB- . Indeed , for 13 of 36 peptidase inhibitor complexes , the crystal structure itself did not score significantly -LRB- see Methods -RRB- , compared with only 3 18 from the three other classes . Inhibitors need to bind tightly to the target proteases , and this binding may be achieved by constrained mainchain conformations -LRB- 15 -RRB- . Cytokine receptor and signaling domain interactions are more transient , with lower affinities -LRB- 24 -RRB- , and involve more side-chain contacts . Fig. 2 . The FGFR system . ALSCRIPT -LRB- 37 -RRB- alignment and MOLSCRIPT -LRB- 38 -RRB- structure figures showing the FGF -LRB- Upper -RRB- receptor -LRB- Lower -RRB- interaction . Sequences are denoted by SWISSPROT or PDB identifiers . Residues are colored according to property conservation : hydrophobic , yellow background ; small , blue background ; and polar , red characters . Residues in the known structure -LRB- FGF-2 FGFR-1 ; PDB code 1cvs -LRB- 26 -RRB- participating in side-chain to side-chain contacts are shown and colored : positive , blue ; negative , red ; hydrophobic , yellow ; and tyrosine , green . Residues participating in side-chain to main-chain contacts are boxed . Contacts between side-chains are listed . 5898 www.pnas.org cgi doi 10.1073 pnas .092147999 Aloy and Russell There is no clear correlation between the performance of the method at modeling interacting interfaces and sequence similarity -LRB- data not shown -RRB- . Even at low sequence identities -LRB- see Table 1 -RRB- , we are able to use one complex to predict most of the correct side-chain to side-chain and side-chain to main-chain interactions that occur in another . The evaluation above considers only instances where different pairs of proteins are known to interact in the same way . It is clearly also important to consider examples where pairs of proteins homologous to a complex structure are known definitely not to interact . A search of the literature demonstrated that there are only a few instances like this , one of which is discussed in the section below . Note that many interaction detection methods , for example two-hybrids , can provide only possible positive examples , and do not rule out interactions . Fibroblast Growth Factors and Receptors . We sought an example from the literature to illustrate the operation and accuracy of the method . Some of the most intensively studied interactions are those between fibroblast growth factors -LRB- FGFs -RRB- and receptors . FGFs play key roles in morphogenesis , development , angiogenesis , and wound healing . There are more than 20 human FGFs that bind to one or more of 7 FGF receptors -LRB- FGFR1c , -1 b , -2 c , -2 b , -3 c , -3 b , and -4 ; c and b denote isoforms IIIc & IIIb formed by alternative splicing ; ref . 25 -RRB- . Four different structures are known , involving all possible interactions between FGF-1 & FGF-2 and receptors FGFR-1c and FGFR-2c -LRB- 26 28 -RRB- . Fig. 2 shows the identified interacting residues for the FGF-1 FGFR-1c complex . These results agree closely with those reported -LRB- 26 -RRB- , although the method finds several others , including a hydrogen bond between Tyr-24 and Lys-163 and many hydrophobic interactions . Table 1 shows that the method successfully predicts all four complexes starting from any of the others -LRB- 12 predictions in total -RRB- with significance 0.01 . FGFs adopt a - trefoil fold , and have been proposed to be ancient relatives of IL-1 , a cytokine that binds to a receptor in a similar fashion to FGFs , and to proteins sharing no apparent functional similarity , such as actin bundling proteins , toxins , and protease inhibitors -LRB- 29 -RRB- . The method correctly gives poor scores to all of these molecules when modeled Fig. 3 . Yeast interactions mapped onto known 3D complexes . Yeast proteins are given by their YPD names , codes , and Pfam families . Thick lines denote interactions significant at 0.01 , thin lines 0.01 0.1 , and dashed lines 0.1 . Aloy and Russell PNAS April 30 , 2002 vol . 99 no. 9 5899 BIOCHEMISTRY onto the FGF receptors -LRB- 0.1 -RRB- , although , interestingly , IL-1 scores highest , possibly reflecting the similarity with FGF receptor system as is known from the IL-1 receptor structure -LRB- 30 -RRB- . Ornitz et al. performed a study of FGFR specificity by measuring mitogenic activity of FGFR-inducible BaF3 cell lines -LRB- 12 -RRB- . They assayed the binding of FGFs 1 9 to all 7 receptors as a percentage relative to the affinity of FGF-1 , which binds well to all of them -LRB- i.e. , 100 % -RRB- . From this study , there are 252 different interactions that can be predicted -LRB- 4 FGF receptor structures , 7 receptors , and 9 FGFs -RRB- . For 112 of these , the experimentally determined binding affinity relative to FGF-1 is 10 % -LRB- which we term `` low affinity '' -RRB- ; for the other 140 , the binding is 10 % -LRB- `` high '' -RRB- . Our method predicts 158 interactions with highly significant values -LRB- 0.01 -RRB- , 105 of which are high affinity interactions . In addition , 59 of the 94 predictions that have low significance values -LRB- 0.1 -RRB- are low affinity , giving an overall prediction accuracy of 65 % . There is good agreement between the predicted scores and the observed affinities , showing a correlation between molecular interaction details and macromolecular observations . This result is despite other factors that may be involved in determining the strength of the FGFR interaction -LRB- e.g. , differing heparin affinities -RRB- . The interactions between FGF-2 with receptors FGFR-2b or FGFR-3b are consistently predicted to be significant despite being low affinity . The lack of affinity is thought to be the result of a deletion that changes the hydrophobic core of the receptor -LRB- 27 -RRB- . We make no attempt to model such changes here . Further improvements may come from explicit modeling of loops or side-chain conformations . An intriguing recent finding within the FGF family is that several members are not extracellular growth factors , but intracellular signaling molecules . FGFs-12 , -13 , -11 , and -14 have recently been renamed as FGF homologous factors -LRB- FHF -RRB- 1 4 , because , despite a high sequence identity to FGFs -LRB- up to 42 % -RRB- , they do not bind to FGF receptors , being instead associated with intracellular mitogenactivated protein -LRB- MAP -RRB- kinases -LRB- 13 -RRB- . Encouragingly , our method gives poor scores to all of the interactions between FHFs and the FGF receptors . Inspection shows that substitutions in these proteins lead to the loss of many key contacts discussed above -LRB- see Fig. 2 -RRB- . Fig. 4 . The kinase cyclase system in yeast . Sequences are denoted by YPD or PDB codes . The known structure -LSB- 1fin -LRB- 35 -RRB- -RSB- is that of human CDK2 -LRB- Upper -RRB- cyclin A -LRB- Lower -RRB- . Details are otherwise as for Fig. 2 . interactions proposed between yeast proteins provide a large set to be studied by our method . Fig. 3 shows those interactions that are homologous to a known 3D complex and how they are scored by the method . Of the 2,590 interactions proposed -LRB- by two-hybrids , coimmunoprecipitation , cross-linking , etc. ; see Methods -RRB- only 59 could be mapped onto our set of interacting complexes . For 23 of these , despite being homologous to a 3D complex , we found that the two interacting domains were not in direct atomic contact -LRB- see Methods -RRB- . For the 36 remaining interactions , we identified domains in direct contact , thus suggesting the molecular basis for the interaction . We ignored one crystallographic structure -LRB- chaperonin-TCP1 complex -RRB- that did not score significantly itself when compared with random sequences , leaving the 35 interactions shown . Considering data from the large scale two-hybrid studies , 7 of the 1,466 interactions proposed by Uetz et al. -LRB- 1 -RRB- and 2 of the 841 from Ito et al. -LRB- 2 -RRB- could be mapped on to this set -LRB- labeled in Fig. 3 -RRB- . The low intersection between these interactions and known 3D structures agrees with that discussed previously -LRB- 14 -RRB- , and also broadly agrees with that seen when comparing two-hybrid data to predictions made from gene-fusions -LRB- 31 -RRB- . Fig. 3 shows instances of interaction promiscuity where the 5900 www.pnas.org cgi doi 10.1073 pnas .092147999 Interrogating the Yeast Protein Interaction Network . The many method gives significant scores only to some of the proposed interactions . This result suggests that the approach could be used to rank interacting pairs that are to be investigated by further experimental means . Significant scores indicate those interactions that are likely to be most compatible with a known 3D structure , and thus where a model will give most accurate details regarding , for example , site-directed mutagenesis experiments . For those where the score is not significant , the interaction may either be weak , or may involve detailed loop or side-chain changes not considered here . Most experimental and computational methods for predicting interactions are unable to discern direct physical interactions from those involving intermediate proteins . Sometimes this result can be clarified simply by mapping proposed interactions onto 3D structures . For example , a cyclin cyclin-dependent kinase regulatory subunit -LRB- CKS -RRB- interaction was proposed by yeast two-hybrids -LRB- 1 -RRB- . 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Andrews , B. & Measday , V. -LRB- 1998 -RRB- Trends Genet . 14 , 66 72 . 35 . Jeffrey , P. D. , Russo , A. A. , Polyak , K. , Gibbs , E. , Hurwitz , J. , Massague , J. & Pavletiich , N. P. -LRB- 1995 -RRB- Nature -LRB- London -RRB- 376 , 313 320 . 36 . Sowa , M. E. , He , W. , Wensel , T. G. & Lichtarge , O. -LRB- 2000 -RRB- Proc . Natl. Acad . Sci . USA 97 , 1483 1488 . 37 . Barton , G. J. -LRB- 1993 -RRB- Protein Eng . 6 , 37 40 . 38 . Kraulis , P. J. -LRB- 1991 -RRB- J. Appl . Crystallogr . 24 , 946 950 Aloy and Russell PNAS April 30 , 2002 vol . 99 no. 9 5901 BIOCHEMISTRY bridge this distance , the structures suggest that the proposed CKS cyclin interaction may involve CDK2 as an intermediary . Several of the interactions shown in Fig. 3 are those between kinases and cyclins , which is a system that shows considerable promiscuity . Multiple cyclins control eukaryotic cell division by regulating CDKs . However , it is unclear whether all are required for cell cycle progression , because there is evidence for considerable redundancy in function -LRB- 32 , 33 -RRB- . Differential activation of CDKs by cyclins could occur through differences in affinities , but may also be a result of differences in expression , protein associations , or subcellular localizations . We applied our method to see whether the scoring system could model the promiscuity within this system . The structure of a human CDK2 cyclin A is the only kinase cyclin complex known in atomic detail -LRB- explaining its absence from Table 1 -RRB- . However , sequence comparison permits 14 of the 22 known yeast kinase cyclin interactions -LRB- 34 -RRB- to be modeled on this complex . Our method identifies all key interacting residues described previously -LRB- 35 -RRB- in addition to others -LRB- e.g. , Lys-278-Asp-9 hydrogen bond , Arg-122-Gln-141 salt-bridge ; Fig. 4 -RRB- . There is often little similarity between the yeast and human proteins in the known complex , considering either overall sequence identities -LRB- CDKs 38 58 % , cyclins 3 32 % -RRB- or only residues on the interacting surfaces -LRB- CDKs 24 78 % , cyclins 10 38 % -RRB- . Nevertheless , we find significant scores for all 14 interactions and for 12 scores have significances 0.01 . The kin28 ccl1 pair is the most different from the human complex -LRB- 24 % and 10 % identity on the interacting surface for the CDK and cyclin , respectively -RRB- . Although some key interactions are lost in the kin28 ccl1 complex with respect to CDK2 cyclin A -LRB- e.g. , Lys-278 Asp-9 changes to Val-278 Lys-9 -RRB- , others show a good correlation between the substitutions in the interacting partners -LRB- e.g. , Arg-122 Tyr-13 and Gln-141 in CDK2 cyclin A changes to Phe-122 Ile-13 and Ile-122 in kin28 ccl1 -RRB- . Despite the low degree of identity , our method is able to model the new interacting residues and correctly predicts the interaction between these two proteins with confidence . Considering the 28 putative CDK cyclin pairs not detected by experimental methods -LRB- i.e. , not in Fig. 3 -RRB- , all but one -LRB- cdc28 pcl1 -RRB- give significant scores , with 14 significant at 0.01 . The surprising suggestion that most kinase cyclin combinations can interact agrees with cyclin knockouts in yeast -LRB- 32 -RRB- that show that deletion of all but a single cyclin yields viable strains . Conclusions We have presented a method to model protein interactions on 3D complexes . The method can successfully model one known protein complex on another , and predicts correct interactions within several systems . Given a known 3D complex structure and homologous sequences for each interacting protein , the method can rank all of the possible interactions between homologues of the same species . For studies of protein families that are known to show different interaction specificities , such a ranking can be used to prioritize experiments , and save laboratory time and costs . This ranking could also help to interpret results from gene-fusion studies -LRB- 4 , 5 -RRB- , where multiple homologues in one organism relative to another can create ambiguities . More generally , we have shown how 3D structures can be used to interrogate whole interaction networks to validate and infer molecular details for interactions proposed by other approaches . There remain several questions regarding protein protein interactions . Whereas we can predict whether proteins homologous to a complex are likely to interact in the same way , we still do not generally know how protein-interacting partners change during evolution . Some systems appear to change gradually , or via genetic events such as alternative splicing , to optimize pairings . Ultimately , these changes may lead to differences in the relative orientations of the interacting molecules , or to homologous proteins interacting with entirely different molecules . However , it is also becoming clear that nature makes use of strategies other than alteration of surfaces to ensure that the correct interactions occur in the cell , foremost among these being regulation of gene expression -LRB- e.g. , ref . 36 -RRB- . Optimal interactions may be obtained simply by the presence or absence of proteins . Each pair of interacting protein families will need to be considered in context in order for strategies like that described here to be most useful . Structural genomics efforts and the increasing pace of structure determination will provide knowledge of many more complexes in the future . With these data , our method will permit critical interrogation of interactions predicted by other areas of functional genomics , and provide molecular details for proposed protein interaction networks in advance of experimental structural biology . We are grateful to I. Gelfand and A. Kister -LRB- Rutgers -RRB- for numerous helpful comments . We thank P. Bork , T. Gibson , R. Copley , E. Conti , C. Perez-Iratxeta , M. Andrade , D. Torrents & T. Sardon -LRB- EMBL -RRB- for critical reading of the manuscript .

O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O 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