The Joint Evolutionary Trees (JET) method detects protein interfaces, the coreresidues involved in the folding process, and residues susceptible tosite-directed mutagenesis and relevant to molecular recognition. The approach,based on the Evolutionary Trace (ET) method, introduces a novel way to treatevolutionary information. Families of homologous sequences are analyzed througha Gibbs-like sampling of distance trees to reduce effects of erroneous multiplealignment and impacts of weakly homologous sequences on distance treeconstruction. The sampling method makes sequence analysis more sensitive tofunctional and structural importance of individual residues by avoiding effectsof the overrepresentation of highly homologous sequences and improvescomputational efficiency. A carefully designed clustering method is parametrizedon the target structure to detect and extend patches on protein surfaces intopredicted interaction sites. Clustering takes into account residues'physical-chemical properties as well as conservation. Large-scale application ofJET requires the system to be adjustable for different datasets and to guaranteepredictions even if the signal is low. Flexibility was achieved by a carefultreatment of the number of retrieved sequences, the amino acid distance betweensequences, and the selective thresholds for cluster identification. An iterativeversion of JET (iJET) that guarantees finding the most likely interface residuesis proposed as the appropriate tool for large-scale predictions. Tests arecarried out on the Huang database of 62 heterodimer, homodimer, and transientcomplexes and on 265 interfaces belonging to signal transduction proteins,enzymes, inhibitors, antibodies, antigens, and others. A specific set ofproteins chosen for their special functional and structural propertiesillustrate JET behavior on a large variety of interactions covering proteins,ligands, DNA, and RNA. JET is compared at a large scale to ET and to Consurf,Rate4Site, siteFiNDER|3D, and SCORECONS on specific structures. A significantimprovement in performance and computational efficiency is shown. Information obtained on the structure of macromolecular complexes is importantfor identifying functionally important partners but also for determining howsuch interactions will be perturbed by natural or engineered site mutations.Hence, to fully understand or control biological processes we need to predict inthe most accurate manner protein interfaces for a protein structure, possiblywithout knowing its partners. Joint Evolutionary Trees (JET) is a methoddesigned to detect very different types of interactions of a protein withanother protein, ligands, DNA, and RNA. It uses a carefully designed samplingmethod, making sequence analysis more sensitive to the functional and structuralimportance of individual residues, and a clustering method parametrized on thetarget structure for the detection of patches on protein surfaces and theirextension into predicted interaction sites. JET is a large-scale method, highlyaccurate and potentially applicable to search for protein partners. Interface residues are essential for understanding interaction mechanisms and areoften potential drug targets. Reliable identification of residues that belong to aprotein-protein interface typically requires information on protein structures Interfaces differ from the rest of the protein surface typically because buriedinterface residues are more conserved than partially buried ones and because thesequences associated with interfaces have undergone few insertions or deletions.However, on average, the most conserved patches of residues overlap only the37.5% (±28%) of the actual protein interface and ananalysis of 64 different types of protein interfaces  demonstrated that conservedpatches cannot clearly discriminate protein interfaces The composition of interacting residues appears to distinguish between differenttypes of interfaces We propose a predictive method, named Joint Evolutionary Trees (JET), that extractsthe level of conservation of each protein residue from evolutionary information,combines this information with specific physical-chemical properties of theresidues, and predicts conserved patches on the protein surface of knownthree-dimensional structures. Defined in this way, JET is able to detect proteininterfaces with very different types. It does not require information on potentialinteraction partners and it belongs to the family of methods which have beeninspired by the Evolutionary Trace approach (ET) Two main hypothesis on interaction sites have been tested. The first asserts thatspecific physical-chemical properties of patches always co-exist with some degree ofconservation of the patch. The second claims that interaction sites on a proteinsurface are composed of an internal core which is conserved, with concentric layersof residues around the core which are progressively less conserved.We also addressed four main methodological points. The first concerns the problem ofaccurately quantifying the strength of residue conservation in a set of sequenceswhose similarity to trace, a notion that quantifies the conservation ofa residue position within a distance tree of sequences similar to The second point concerns the core of the ET methodology which relies on thedefinition of a The third point concerns the evaluation of patches of conserved residues as potentialinternal cores of interaction sites. We tested the hypothesis that such corescorrespond to the largest patches found for the protein and observed that this isgenerally the case. A novel method estimating the size of relevant clusters ofconserved residues and of clusters of residues with specific physical-chemicalproperties has been tailored around the specific protein being treated Finally, since JET is based on the random choice of small sets of sequences forconstructing multiple trees, it could yield slightly different answers in differentruns. This fluctuation has been analyzed and exploited to further improve ouralgorithm. An iterative version of JET (iJET) provides a list of consensus residuesbelonging to interaction patches. When JET is used for large-scale analyses, thisturns out to be a safe and successful approach. When the user uses JET on a singleprotein, it is possible to run it once, or to explore the set of potentiallyinteracting residues by varying a consensus threshold during iterations. Indifficult cases, this can allow the user to refine the detection of interactingresidues.reference sequence.The sequence Below, we describe in detail the basic steps constituting JET. The methods developedfor each step are designed for large-scale applications. The aim is to insure thatthe system always provides a prediction even with weak signals. To achieve this wemade the approach adaptable to different datasets in terms of the number ofretrieved sequences, the amino acid distance between sequences, and the selectivethresholds for cluster identification.JET first recovers a set of sequences homologous to http://www.ncbi.nlm.nih.gov/Blast.cgi, or locally, to select asmany as 5000 sequences. It does it on chains with at least 20 residues.Retrieved sequences are filtered to eliminate redundant sequences, that issequences with >98% sequence identity to JET performs a PSI-BLAST search A second filter is defined on the length of the alignment which should cover atleast the 80% of the length of the reference sequence −5.A third filter cuts-off sequences with an e-value≥10−4. We repeatedly increase the e-value and decreasethe length by filtering sequences progressively with e-values10−3, 10−2,10−1, 1, 10, 100, until a sufficient number of sequences isretrieved.If the pool of remaining sequences does not contain at least 100 sequences, thenthe cut-off on the length of retrieved alignments is automatically decreased by10% of the length of At the end of the retrieval step we obtain a set We want to align small sets of This way, we obtain a good compromise between an ideally uniform distribution ofsequence identities within an alignment and the diversity of sequences amongstdifferent alignments.Sequences in a pool are aligned using CLUSTALW with the Blosum62 matrix To align distantly related proteins, Gonnet For each multiple alignment, a distance tree is constructed based on the NeighborJoining algorithm (NJ) rank If Consensus sequences of rank n and backtrace sequences ofrank n are used to define tree traces.consensus sequence associated to a leaf i-th residue of i-th position of consensus sequence of a node of rank n is a sequence  is greater than thecluster-trace threshold. Notice that residue traces may be smaller than thecluster trace threshold. The set of clusters percentile of a sizepercentile of the number of clustersIn the second step, the algorithm computes a threshold for the size of the“cluster seeds”. To do so it takes the distribution of tracevalues obtained by running JET on a given protein and randomly reassigns thesame trace distribution to surface residues of the protein. It clusters with theclustering algorithm described above and repeats this procedure 6000 times. Itcalculates the distribution of the size of the clusters and the distribution ofthe number of clusters obtained, to determine the cluster seeds.Then it selects the clusters in The third step of the algorithm extends the cluster seeds with neighboringresidues by maintaining a sufficiently high average trace of the cluster. To dothis, the cluster-trace threshold is set at a confidence level of The algorithm then outputs the final set of clusters. The number of clusters maybe smaller than the number of cluster seeds because extension may lead to thefusion of some initial clusters.Note that the residue-trace threshold guarantees that we are going to cluster apool of residues among the mixed trace value for a residue Statistical analysis of physical-chemical properties of protein-proteininterfaces reveals a biased amino-acid content within interfaces and allows thedefinition of propensity values for interface residues Size of predicted clusters computed with mixed trace values and number of surfaceresidues are reported in The experimental interaction sites for the proteins listed in ftp://ftp.genome.jp/pub/kegg/ligand/enzyme/enzyme) anddischarged all compounds which were absent in the list. All identifiedinteractions were grouped together to define the set of“true” interacting residues of the experimental structure tobe evaluated. We define a residue to belong to an interaction site if at least10% of the accessible surface of the residue (within the protein)becomes non accessible due to the interaction (within the complex).JET finds signals corresponding to different interactions of a protein, namelywith other proteins, ligands, DNA or RNA, as well as the chain-chaininteractions in multimeric proteins. Hence, it becomes important to consider allit is known of such interactions to correctly evaluate predictions see . Given aTo properly evaluate JET performance on a given protein we rely on the followingquantities: the number of residues correctly predicted as interacting , the number of residues correctly predicted as non-interacting, the number of non-interacting residues incorrectlypredicted as interacting  and the number of interactingresidues incorrectly predicted as non-interacting . We usefour standard measures of performance: sensitivity To compare JET performance and the ET analysis described in When JET gives no answer , then all interface residues are treated as negativeswith All evaluation scores reported in the Tables, The Huang dataset of 62 protein complexes constituted of 43 homodimeric chains,24 heterodimeric chains and 19 transient chains Several additional protein structures discussed in the text are listed in To check JET behavior on interfaces belonging to different functional categories,we used the Kanamori dataset of 265 interfaces which contains 72 signaltransduction proteins, 43 enzymes, 19 inhibitors, 36 antibodies, 31 antigens, 64other proteins http://www.rcsb.org/pdb/home/home.do.Structures of proteins and complexes used for the analysis were downloaded fromProtein Data Bank http://mammoth.bcm.tmc.edu/traceview/). ET default values are:500 BLAST retrieved sequences, a sequence identity between26–98% for retrieved sequences, a cut-off of 0.7 on thelength of retrieved sequences, a maximum BLAST e-value at 0.05, a coverage of25% for clustering residues belonging to the whole protein (not onlythose lying on the surface). Note that for small proteins, the 25%protein coverage corresponds essentially to surface coverage, but that, ingeneral, one should expect ET to cover much less protein surface.ET predictions  have beenobtained using locally ET Viewer  and areasonable length (longer sequences should provide better results). Amongthe proteins analyzed here, the Shc PTB domain 1shc:A (195aa), the oncogeneprotein 1ycr:A (85aa), and the protein mimicry of DNA 1ugh:I (82aa) fall inthis category. JET performs best on 1shc:A, the longest protein chain inthis group, with all sequence identity classes represented.Physical-chemical properties (and not only conservation) play a role in theprediction see . SimilarThe retrieval of few sequences might induce JET to accept large e-values. For1ugh:I, for instance, sequences of e-value 92 have been accepted. One mightwonder about the biological meaning of such filtering choice, and 1ugh:Idemonstrates that without such a lax condition, no prediction could be made.ET method failed to predict on this difficult example.Depending on the size of a protein we should expect that a different proportionof residues will belong to interfaces One of the characteristics of JET is to use several distance trees of randomlysampled sequences instead of just one distance tree grouping all sequencesrecovered with PSI-BLAST. In Given a protein structure, JET estimates the size of the largest surface clusterfor the protein (obtained by taking the largest cluster computed over 6000iterations of the random clustering procedure). Based on the number of estimatedresidues, it predicts an interaction site of the appropriate size. The need fora structure-specific estimation results from the absence of a correlationbetween protein size and size of the largest surface cluster as illustrated inJET is a prediction tool that uses evolutionary information to detect conservedinteraction sites and was inspired by the Evolutionary Trace approach.Comparisons with ET are therefore necessary. The performance of JET and ET onthe Huang dataset are presented in Differences in ET and JET performance with and without clustering suggests thatthe clustering procedure employed in ET is less successful than that proposedhere.In For small proteins, the usage of an adapted curve  starting from avery mixed pool of sequences. It is interesting to notice that iJET andRate4Site agree on the very variable residues, contrary to Consurf prediction ofvariability  discussed in JET is capable of detecting very different types of interface, as illustratedhere with several case studies. Some belong to the Huang dataset and otheProteins in the Huang dataset are organized in heterodimer, homodimer andtransient interfaces. We observe a similar behavior of JET (and iJET) onheterodimers and homodimers . As expeLigand interaction sites are often pockets involving very conserved residues.In contrast, protein interface sites are less conserved regions and theirresidues are often characterized by specific physical-chemical properties,especially hydrophobicity. This point is nicely illustrated by the leucinedehydrogenase structure 1leh in Two other examples involve the GTP-binding chain 1grn:A  and the The nucleotidyltransferase 2pol is characterized by four distinct interfacessee , twointFor multiple sites, the fraction of interface residues compared to thesurface size might be larger than the one estimated by the curve in Three receptor/inhibitor complexes have been analyzed: 1ugh, 2ptc and 1k9o.For all three, the receptor site forms a conserved pocket and is very wellpredicted by JET, while the inhibitor interface is not. The three proteinsdisplay catalytic activity within the conserved pockets and this isconsistent with the presence of the strong signals of conservation that wegenerally observe for ligand binding sites. The inhibitors 1ugh:I (82aa) and2ptc:I (58aa) are short peptide chains while 1k9o:I (376aa) is a long chain.This suggests that a small length should not be considered as the reason forthe failure. Multiple interactions of the inhibitor with several proteinsmight rather explain the lack of strong conservation, while thediscrimination of interacting partners might rely more on the geometricalshape of the inhibitor.Results are given in As for ligand interactions, interaction sites of a protein with DNA or RNAappear to be rather conserved as illustrated in the structures of thefragment of DNA polymerase I 2ktq  and of aFor 2cjk, there are three regions that enter in contact with RNA for therecognition of the specific termination signal AUAUAU. Two of them lie onthe conserved site detected by iJET with  see  and the For 2ktq, the strong conservation signal corresponds to a ligand site  and to roughly halfof the residues interacting with DNA. In contrast, thenucleotidyltransferase 2pol does not show any conservation of the residuesin contact with the DNA. In fact, for this protein there is no need forspecific recognition, its function depending on residue charges which bindDNA, but allow it to slide see .Even though JET detects several interaction sites and any evaluation isdifficult, we compared it with the performance of ET on the Kanamori dataset ofproteins organized in functional classes, where specific pairwise interactionswere targeted. The overall evaluation scores attaint by iJET cannot be very gooddue a potentially erroneous increase of false positives coming from JETdetection of multiple interaction sites, but an honest comparison of iJET to ETcan be drawn on functional classes following Conserved patches of residues on a protein surface can help to suggest thelocation of an interaction site. We have tested the evolutionary hypothesis thatinteraction sites are constituted by a conserved internal core, surrounded bysuccessively less conserved layers of residues. Based on this hypothesis we wereable to develop a new criterion for extending conserved patches (that is“cluster seeds”), improving predictions of realisticinterface clusters. The impact of this extension step in the algorithm is nicelyillustrated in By making multiple iterations, iJET predicts 40% of the interfaces forproteins within the Huang dataset  and physico-chemical interface propensity values indeed leads tosuccessful predictions. Future developments of JET will include an intelligentdetection of patches satisfying specific physical-chemical properties based onpropensity values differentiating multiple types of interaction in silico experiments on protein interfaces. These latterare possible due to the flexible parameterization provided by the system. Eachstep of JET can be monitored and improved by an accurate ad hoc understanding ofthe protein under study . The firsthand information coming from a run of JET are the clusters that it provides.Notice that for large-scale comparison of JET and ET, we considered a hit to bethe set of clusters issued by a single run of JET. For comparison with iJET, weconsidered a hit to be the set of clustered residues issued by iJET, with JET and iJET can be used for large-scale analysis or as platforms to makeMultiple interaction sites often occur on a protein surface and this makesevaluating JET difficult since only some of these sites may be experimentallycharacterized. JET is nevertheless capable of detecting all residues patcheswhich are susceptible to be involved in interactions with other ligands ormacromolecules. An example illustrating this point is leucine dehydrogenase 1lehsee  Which haLastly, it is remarked that JET can be applied to protein sequences for which thestructure is unknown, if the structure of a homologous protein is available.This approach can again be valuable to the biologist, notably in guidingsite-specific mutagenesis experiments Text S1Propensity values(0.05 MB PDF)Click here for additional data file.Text S2ET, JET and iJET performance on the Huang dataset(0.16 MB PDF)Click here for additional data file.Text S3JET and iJET performance on a pool of selected proteins discussed in thearticle(0.08 MB PDF)Click here for additional data file.Text S4JET program information(0.09 MB PDF)Click here for additional data file.Text S5iJET performance on the Kanamori dataset(0.10 MB PDF)Click here for additional data file.


An important problem in genomics is the automatic inference of groups of homologous proteins from pairwise sequence similarities. Several approaches have been proposed for this task which are "local" in the sense that they assign a protein to a cluster based only on the distances between that protein and the other proteins in the set. It was shown recently that global methods such as spectral clustering have better performance on a wide variety of datasets. However, currently available implementations of spectral clustering methods mostly consist of a few loosely coupled Matlab scripts that assume a fair amount of familiarity with Matlab programming and hence they are inaccessible for large parts of the research community.Saccharomyces cerevisiae .SCPS  is an efficient and user-friendly implementation of a spectral method for inferring protein families. The method uses only pairwise sequence similarities, and is therefore practical when only sequence information is available. SCPS was tested on difficult sets of proteins whose relationships were extracted from the SCOP database, and its results were extensively compared with those obtained using other popular protein clustering algorithms such as TribeMCL, hierarchical clustering and connected component analysis. We show that SCPS is able to identify many of the family/superfamily relationships correctly and that the quality of the obtained clusters as indicated by their F-scores is consistently better than all the other methods we compared it with. We also demonstrate the scalability of SCPS by clustering the entire SCOP database  and the complete genome of the yeast http://www.paccanarolab.org/software/scps.Besides the spectral method, SCPS also implements connected component analysis and hierarchical clustering, it integrates TribeMCL, it provides different cluster quality tools, it can extract human-readable protein descriptions using GI numbers from NCBI, it interfaces with external tools such as BLAST and Cytoscape, and it can produce publication-quality graphical representations of the clusters obtained, thus constituting a comprehensive and effective tool for practical research in computational biology. Source code and precompiled executables for Windows, Linux and Mac OS X are freely available at  An important problem in genomics is the automatic inference of groups of homologous proteins when only sequence information is available. Several approaches have been proposed for this task which are "local" in the sense that they assign a protein to a cluster based only on the distances between that protein and the other proteins in the set. In fact, the majority of these methods are based on thresholding a sequence similarity measure  at Saccharomyces cerevisiae.In the rest of this paper, we outline the general framework of our spectral clustering algorithm and then demonstrate its practical usage and usefulness via a number of benchmarks ranging from a few superfamilies to the entire SCOP database and the genome of the yeast The goal of SCPS is to infer homology relations between protein sequences based on pairwise sequence information only. The input dataset thus consists of either a set of protein sequences or a list of pairwise similarity scores between some protein domains. The output is a partition of the sequences such that each sequence is assigned to one and only one of the partitions in a way that the partitions represent groups of homologs.G. Each vertex of the graph corresponds to a protein sequence. Vertices are connected by undirected, weighted edges, each edge denoting a similarity relation between the two proteins it connects. The weight (label) of the edge is related to the probability of evolutionary relatedness. Edges with large weight are more likely to appear between domains of the same superfamily, hence the problem of partitioning the graph into subsets of vertices with mostly heavy-weight edges is an equivalent formulation of the original protein sequence classification problem. Spectral clustering solves the problem of finding the optimal partition by examining random walks on the similarity graph , as suggested by the documentation of the algorithm. The final inflation parameter was chosen in a way that resulted in the highest F-score. For the SCOPn denote the total number of proteins in the dataset, ni* the number of proteins in the ith superfamily, nj *the number of proteins in the jth calculated cluster and nij the number of proteins that are in superfamily i and cluster j at the same time.We used the combined F-score to compare a clustering result with the gold standard SCOP superfamily classification. Let Definition 5 (Precision) The precision of cluster j with respect to superfamily i is the fraction of proteins in cluster j that are also in superfamily i: pij = nij /nj*Definition 6  The recall of cluster j with respect to superfamily i is the fraction of proteins in superfamily i that are also in cluster j: rij = nij/ni*Now we can define the combined F-score, which combines precision and recall with equal weights.Definition 7 (Combined F-score) The combined F-score is defined as follows:The combined F-score attains its maximum at 1 if the two clusterings are identical.≥ 5 dataset contains all the SCOP superfamilies containing at least five sequences. The datasets were described in detail earlier in the Data subsection.The validity of the spectral clustering approach was tested on several datasets assembled from the SCOP database, version 1.75 . Sequencε = 1.02) were compared to the results obtained from hierarchical clustering [The results of the spectral clustering algorithm running in fully automatic mode with default parameters  v3Restrictions to use by non-academics: NoneTN designed and implemented the SCPS graphical and command line interfaces, contributed robustness, stability and scalability improvements to the original algorithm and performed benchmarks. RS provided important biological insights and ideas to the algorithm and the datasets and extensively tested the software. AP conceived and implemented the first version of the algorithm, contributed valuable ideas to the present implementation and tested the algorithm. TN, RS and AP contributed to the writing of the paper. All the authors have read and approved the final manuscript.


Here, we propose an approach that harnesses the increasing structural coverage of protein complexes to identify small molecules that may target protein interactions. Specifically, we identify ligand and protein binding sites that overlap upon alignment of homologous proteins. Of the 2,619 protein structure families observed to bind proteins, 1,028 also bind small molecules (250–1000 Da), and 197 exhibit a statistically significant (p<0.01) overlap between ligand and protein binding positions. These “bi-functional positions”, which bind both ligands and proteins, are particularly enriched in tyrosine and tryptophan residues, similar to “energetic hotspots” described previously, and are significantly less conserved than mono-functional and solvent exposed positions. Homology transfer identifies ligands whose binding sites overlap at least 20% of the protein interface for 35% of domain–domain and 45% of domain–peptide mediated interactions. The analysis recovered known small-molecule modulators of protein interactions as well as predicted new interaction targets based on the sequence similarity of ligand binding sites. We illustrate the predictive utility of the method by suggesting structural mechanisms for the effects of sanglifehrin A on HIV virion production, bepridil on the cellular entry of anthrax edema factor, and fusicoccin on vertebrate developmental pathways. The results, available at  Proteins function through their interactions with other biological molecules, including other proteins. Often times, these interactions underlie cellular processes that go awry in disease. Therefore, modulating these interactions with small molecules is an active area of research for new drugs to treat diseases and new chemical tools to dissect cellular interaction networks. However, targeting protein–protein interactions has proven to be more challenging than the typical drug targets found on individual proteins. Here, we present a computational approach that aims to help in this challenge by identifying regions of protein–protein interfaces that may be amenable to targeting by small molecules. Through a comprehensive analysis of all known protein structures, we identify closely related proteins that in one case bind a protein and in another case bind a small molecule. We find that a significant number of protein–protein interactions occur through surface regions that bind small molecules in related proteins. These “bi-functional” positions, which can bind both proteins and ligands, will serve as an additional piece of structural information that can aid experimentalists in developing small molecules that modulate protein interactions. Protein–protein interactions are a broad class of therapeutic and chemical biology targets The physicochemical, geometric, and evolutionary properties of ligand and protein binding sites have been extensively studied by analyzing three-dimensional protein structures The computational methods developed for traditional rational drug design, such as pocket detection and virtual screening, have also been applied to identify small molecules modulators of protein interactions. The methods are frequently adapted to the unique properties of protein interfaces, such as their adaptivity in forming small transient cavities that can bind small molecules Here, we perform a systematic analysis of structurally characterized ligand and protein binding sites, with a central goal of comprehensively identifying, enumerating, and describing those positions in protein structure families where both ligands and proteins have been observed to bind. We first analyze the overlap of these binding sites within protein families, characterizing the composition and conservation of these ‘bi-functional’ positions, and identifying the families in which they are more or less prevalent than expected by chance. Next, we describe protein–protein and protein–peptide interactions for which small molecules were observed to bind at corresponding or homologous positions in other protein structures. Finally, we describe known interaction modulators recovered by the analysis, and illustrate its predictive utility by suggesting structural mechanisms for the observed effects of three small molecules.We began by assembling a comprehensive list of protein and ligand binding sites. Protein–protein  binding sites were obtained from PIBASE (v200808) We first quantified the extent and significance of overlap between all ligand and protein binding sites observed in each protein family. The binding sites were mapped onto alignments of domain families obtained from the ASTRAL compendium http://r-project.org). We identified 197 families with significantly more  or solvent-exposed. The propensities of each amino acid residue at mono-functional and bi-functional positions were calculated relative to all exposed residues, and their significance estimated by a bootstrap resampling procedure . The magBi-functional positions are also significantly less conserved than mono-functional or solvent exposed positions, as measured by an entropy-based conservation score  as well ey tests . AlthougHaving established that ligand and protein binding sites often overlap within protein families, we aimed to determine the utility of known ligand binding sites for targeting particular protein–protein interactions. The ligand binding sites were mapped onto individual domain–domain and domain–peptide interfaces, using ASTRAL alignments as described earlier . The oveWhen the ligand binding site aligned to both sides of a domain–domain interface, the larger of the two overlap fractions was used as the overlap score.The ligand binding site coverage of each protein–protein interface was summarized using two scores. First, a maximal overlap score was used to quantify the maximum overlap observed by any ligand for the protein–protein interface. Second, a cumulative overlap score was computed by simultaneously aligning all homologous ligand binding sites onto each protein–protein interface and calculating the fraction coverage. This procedure is conceptually related to fragment-based drug discovery techniques, such as tethering The behavior of these overlap scores was examined as a function of the sequence identity between the ligand binding site and the corresponding positions in the interacting proteins , S2. As Although the analysis suggests that most interfaces do not have a homologous ligand binding site, as seen by the main peak over an interface overlap of 0 , there aThe systematic alignment of ligand binding sites onto protein interfaces generates a dataset useful for two primary purposes. First, it serves as a comprehensive collection of structurally characterized interaction modulators, in the cases where the ligand binding domain is identical to the sequence involved in the protein interaction . Second,To validate the accuracy of the mapping method, we checked whether known protein interaction modulators were recovered by the method. Indeed, all but one of the modulators discussed in a recent review article observed .Having established the accuracy of the binding site mapping, we next examined the results for their predictive utility in identifying small molecule modulators of protein interactions. Those ligand binding sites that mapped with a high sequence identity, in the context of different protein sequences, represent high confidence predictions where ligand binding may occur . This kiThe ligands identified in the analysis fell into four broad categories based on the kinds of protein–protein interactions that they overlapped , S6. TheA related group of ligands overlapped with the interface of an enzyme and its natural protein or peptide substrate. This class includes ligands that bound at signaling complexes such as MDM2–p53, farnesyltransfrease–h-ras, and histone acetyltransferase–p53. An example that is used therapeutically are HIV protease inhibitors bound at the protease dimer in place of its peptide substrate . We alsoA third class of ligands overlapped with the interface of structural or regulatory protein–protein interactions. These ligands include natural toxins, such as kabiramide C bound at the actin–gelsolin interface  and fusiThe fourth group of ligands were transferred from structures where they were present at domain interfaces. Although it is difficult to predict the effect of these ligands on the target interface, this group of ligands may be more likely to sterically complement protein interfaces than ligands in the other groups, which more likely sterically hinder protein interactions. This group includes elaidoylamide bound at the homodimeric interface of agkistrodotoxin Phospholipase A2 (PDB 1RGB), and bepridil bound at the interface of Troponins C and I 1LXF; . LigandsAnother class of protein complexes with overlapping homologous ligand binding sites are antibody–antigen complexes. These overlaps are an expected result of the diversity of the complementary-determining regions of immunoglobulins that enable binding to virtually all proteins and small molecules.The ligands that mapped to intra-molecular domain interfaces included natural ligands such as ATP, GTP, and Heme groups, as well as synthetic and natural toxins such as the Pulvomycin and Kirromycin antibiotics . Since wAlthough we observed overlaps that occur in a variety of functional classes, they can all contribute towards a structural understanding of bi-functional positions. Irrespective of the natural or synthetic source of the small molecule, or the particular functional class of protein interaction, the resulting overlaps are structurally informative for understanding what makes particular interface regions amenable to targeting by small molecules. This point can be further clarified by considering the known modulators of protein interactions that we used to test the fidelity of our mapping procedure. Although these examples involve synthetic small molecules, they have been extensively characterized structurally to understand what makes their particular binding sites amenable to targeting by small molecules Actinomycetes species, that has been observed to reduce HIV-1 virion production The results also suggest possible structural mechanisms for the observed effects of small molecules. We will describe three such examples, each from a different ligand class: sanglifehrin A, bepridil, and fusicoccin. Sanglifehrin A is an immuno-suppressant, synthesized by an Fusicoccum amygdali, that disrupts protein interactions mediated by plant 14-3-3 proteins XenopusFusicoccin is a toxin, synthesized by the fungus terfaces , Fig. 4BBepridil is an FDA-approved calcium channel blocker that was until recently used to treat refractory angina. Recently it was found to inhibit the cellular entry of two anthrax toxin components: the edema and lethal factors We presented a systematic analysis of protein structure families that identified bi-functional positions that bind both small molecules and proteins , S2, S3.Our results suggest that structural data might be harnessed in a comparative fashion to characterize small molecules that target protein-protein interactions. This approach is complementary to recent computational studies that characterize known modulators of protein interactions This preliminary analysis can be extended in several ways to overcome limitations inherent to the current implementation. First, the comparative basis of the method relies on the availability of homologous ligand-bound structures. Although the structural coverage of protein–ligand and protein–protein complexes continues to increase, homologous ligand binding sites are not available for the majority of protein interactions . This coSecond, comparing the bi-functional positions to hotspot residues, that disproportionately contribute to the free energy of protein interactions, will illuminate their biophysical role Finally, although we have focused on ligands that may directly modulate, by complementing or sterically competing with, protein–protein interactions, another relevant class of interactions is allosteric regulation. Allosteric control refers to signal propagation between two distal binding sites through a network of residues that traverses a protein We observed that several small molecule compounds, originally designed for traditional medicinal chemistry targets such as enzyme active sites, in fact target protein interfaces. These include several FDA-approved drugs, such as bepridil that binds at the interface between Troponins C and I, and HIV protease inhibitors that bind at the dimer interface. Although these examples involve fairly small protein interfaces, this observation suggests that protein–protein interactions are not completely novel targets for medicinal chemistry, and that the chemical, biophysical, and computational experience that has been developed in traditional rational drug design may also be applicable to interaction targets.http://pibase.janelia.org), to serve as a structural resource to aid in the rational design of small molecule modulators of protein–protein interactions.As protein interaction networks are resolved with greater accuracy and coverage 2 buried surface area), to remove small interfaces that are often crystallographic artifacts. A minimum domain participation of 5 residues was also imposed on domain–peptide interactions to remove small interfaces. This procedure identified 24,717 inter-molecular domain–domain, 13,228 intra-molecular domain–domain, and 6,911 domain–peptide interactions involving 2540, 1485, and 534 domain families, respectively.Residues in domain–domain and domain–peptide binding sites were obtained from PIBASE v200808 Ligand binding sites were obtained from LIGBASE Redundant binding sites were identified by single-linkage clustering of binding sites that shared more than 90% of their residues as measured by: /(positions in either binding site). This reduced the number of ligand binding sites from 39,085 to 35,168; peptide binding sites from 4,937 to 2,332; inter-molecular domain interfaces from 40,791 to 12,015, and intra-molecular domain interfaces from 17,863 to 4,290. The redundancy removal was performed with respect to the alignment positions, rather than amino acid sequence identity, because the binding site projection procedure relied on the alignment positions. This redundancy removal procedure aimed to reduce the effect of PDB bias in structural coverage of proteins, protein complexes, and protein-ligand complexes.The propensity of residue types in each class of position  was computed relative to all solvent exposed positions by counting the frequency of the 20 standard amino acid residue types:http://R-project.org). Propensity values were considered significant .Alignment positions that contain only one kind of amino acid residue receive a conservation score of 1, while those with a uniform distribution of residue types receive a score of 0. The distributions of conservation scores for each kind of alignment position  were compared using the Kolmogorov-Smirnov and Mann-Whitney tests, as implemented in R (Each family was assigned one of seven broad functions by SUPERFAMILY http://R-project.org). Propensity values were considered significant  and proteins.(0.03 MB PDF)Click here for additional data file.Table S2http://r-project.org).The ten families with the most significantly (p<0.01) higher or lower number of bi-functional positions than expected by chance. Bi-functional positions refer to alignment positions that bind both small molecules (250–1000 Da) and proteins. The significance of the overlap (Text Eqn 1) is assessed by the Fisher exact test ((0.04 MB PDF)Click here for additional data file.Table S3Summary of protein interactions and their overlap with aligned ligand binding sites from homologous structures. The numbers of protein interfaces with at least 20% cumulative or maximal overlap with homologous ligand binding sites are shown for each kind of protein interface. The overlap score refers to the fraction of interface residues aligned to ligand binding site residues (Text Eqn 2).(0.03 MB PDF)Click here for additional data file.Table S4The function propensities of families with significantly (p<0.01) higher or lower number of bi-functional positions than expected by chance. Bootstrap resampling was performed to compute 95% confidence intervals of the function propensities (Text Eqn 5). Propensities are considered significant (asterisk) at the alpha = 0.05 level if their confidence intervals do not include the value 1.(0.03 MB PDF)Click here for additional data file.Table S5The residue type propensity at alignment positions that bind both ligands and proteins, bind ligands, or bind proteins in comparison to all solvent-exposed residues. Bootstrap resampling was performed to compute 95% confidence intervals (CI) of the residue type propensities (Text Eqn 3). Propensities are considered significant (asterisk) at the alpha = 0.05 level if their confidence intervals do not include the value 1.(0.03 MB PDF)Click here for additional data file.Table S6Examples of ligand binding sites that align to protein–protein interfaces with a high sequence similarity. The overlap (Text Eqn 2) between each ligand and protein interface is shown along with the sequence identity of the ligand binding site and the full-length domain sequence. (d) refers to inter-molecular domain–domain interactions, (p) refers to domain–peptide interactions, and (*) indicates ligands that were present at domain interfaces.(0.04 MB PDF)Click here for additional data file.Table S7Examples of ligand binding sites that align to intra-molecular domain–domain interfaces. The overlap (Text Eqn 2) between each ligand and domain interface is shown along with the sequence identity of the ligand binding site and the full-length domain sequence.(0.03 MB PDF)Click here for additional data file.Figure S1Protocol for quantifying binding site overlap, functional, and evolutionary properties. (A) Ligand and protein binding sites obtained from LIGBASE and PIBASE, respectively, were mapped onto domain family alignments from the SCOP ASTRAL compendium. (B) The square labeled A is a cartoon representation of a protein domain family upon which ligand (diamonds) and protein (grey ellipses) have been mapped. These binding sites are mapped onto the ASTRAL alignment of the family and the cumulative overlap of ligand and protein binding positions is quantified. (C) The ligand binding sites are also mapped directly onto individual protein interfaces, in this case the interaction between domains A and B, and the overlap quantified. (D) The distribution of function propensities (Text Eqn 5) for significantly overlapping and non-overlapping families, as annotated by SUPERFAMILY. Function propensities were considered significant (asterisk) at the alpha = 0.05 level if the 95% confidence interval estimated by bootstrap resampling did not include the value 1 . (E) Res(0.34 MB TIF)Click here for additional data file.Figure S2Maximum and cumulative ligand-protein binding site overlap observed at protein–protein interactions as a function of sequence identity. The maximum and cumulative observed ligand binding site overlap (y-axis) for  inter-molecular,  intra-molecular domain–domain, and  domain–peptide interactions, as a function of the ligand binding site sequence identity (x-axis). The densities in these plots are represented by colors that range from yellow (no density) to blue (maximum density). The overlap profiles are shown at minimum ligand binding site identity thresholds of  30%,  50%, and  90% for inter-molecular (black), intra-molecular (orange) domain–domain, and domain–peptide (cyan) interactions. Tick marks, arranged as ‘rug plots’, represent interfaces of each type that exhibit a particular level of interface coverage. The overlap score refers to the fraction of interface residues aligned to ligand binding site residues (Text Eqn 2).(1.87 MB TIF)Click here for additional data file.


Analysis of large-scale experimental datasets frequently produces one or more sets of proteins that are subsequently mined for functional interpretation and validation. To this end, a number of computational methods have been devised that rely on the analysis of functional annotations. Although current methods provide valuable information , they do not specifically measure the degree of homogeneity of a protein set.In this work we present a method that scores the degree of functional homogeneity, or coherence, of a set of proteins on the basis of the global similarity of their functional annotations. The method uses statistical hypothesis testing to assess the significance of the set in the context of the functional space of a reference set. As such, it can be used as a first step in the validation of sets expected to be homogeneous prior to further functional interpretation.Saccharomyces cerevisiae, which are mostly significantly coherent. Finally, we illustrate the usefulness of our approach for validating 'functional modules' obtained from computational analysis of protein-protein interaction networks. Matlab code and supplementary data are available at We evaluate our method by analysing known biologically relevant sets as well as random ones. The known relevant sets comprise macromolecular complexes, cellular components and pathways described for  An increasing number of functional data are available at different genome databases and resources spanning all biological levels. Functional information is usually provided as annotations associated with gene products using functional terms from controlled vocabularies and ontologies . This inPredictive approaches are intended to infer new functional annotations for a gene product or a set of them from available data (some recent reviews can be found -4). MostIn contrast, descriptive approaches are intended to perform functional validation and interpretation of experimental results. The objective of these methods is to compare new experimental data with the current state of knowledge as stored in curated databases. In this way, experimental data can be validated and new insights can be highlighted from the analysis. Among descriptive methods, a distinction can be made between those that perform functional analysis of a protein set and those that perform pairwise functional analysis.). Those tools first retrieve all annotations of a protein set of interest from a functional scheme. The number of proteins annotated with each functional term is then counted in both the input and reference sets. Finally, a statistical test  is applied to measure the significance of each functional term, and this is subsequently adjusted for multiple testing. The result of this type of analysis is therefore a list of functional terms with their corresponding p-values. Those terms with p-values indicating statistical significance are considered representative and therefore give information about the 'enriched' functions in the protein set. Although some methods have been developed to obtain enriched co-annotations , most tIn addition, several studies have been reported that aim to establish a similarity score for a pair of proteins, accounting for the resemblance of their functional annotations. To this end, several similarity measurements have been described ,15,18-23Although these methods provide valuable information, they do not specifically address the issue of functional homogeneity, i.e. whether a set of proteins participates in related cellular processes, performs similar molecular activities, confers similar phenotypes, etc. An experimental set of proteins is usually grouped on the basis of shared experimental features , and it is expected that such a set can be distinguished from a random set when considering a particular functional aspect. Therefore, a method that measures the degree of overall functional homogeneity of a protein set would be useful for validating experimentally or computationally derived sets, highlighting those that merit further investigation. For example, when protein-protein interaction networks are analyzed to discover functional modules, protein clusters could first be filtered on the basis of functional homogeneity, avoiding any additional functional interpretation for those heterogeneous cases.coherence score) accounting for the global similarity of their functional profiles. This coherence score is computed using a previously-reported global pairwise functional similarity measure. Then we assess whether this score is statistically significant given a reference set . This significance is measured in terms of the number of proteins in the reference that are also similar to the set at its particular coherence level. Note that a very homogeneous protein set (with a high coherence score) will not be statistically significant in the context of a reference set if it contains only a few proteins of the reference that are functionally related. On the other hand, a relatively homogeneous set (with a lower coherence score) might be significant if it contains a sufficient number of functionally related proteins of the reference.To this end we propose a new descriptive method, based on functional annotations, that evaluates the statistical significance of the overall homogeneity of a protein set. Given a set of proteins, we first compute its degree of homogeneity . Therefore, each functional term will correspond to a coordinate of the vector space representation. In the case of hierarchical functional schemes (e.g. Gene Ontology and MIPS FunCat) this representation is constructed by assigning 1 to each functional term annotated to a gene product and to its corresponding ancestor terms in the hierarchy. The remaining vector coordinates are equal to 0.A protein is represented as an IC) of a term is inversely related to its probability of annotation in the reference set Pr(t). The weight is formally calculated as:To account for the specificity and generality of functional terms, a weighting scheme is applied to this vector representation using the information content of each term. The information content (t) is the probability of annotation of a term t, estimated as the number of gene products associated with t (#genest) divided by the total number of protein-term associations (m) in a reference set R. Note that the total number of gene products associated with t is the sum of those directly annotated with t and those annotated with any of its descendants in the functional hierarchy.where Pr to 1 .n-dimensional functional space. Therefore, each criterion is established for a set in the context of a reference and for the particular coherence score obtained for the set as computed in equation (4). These three criteria are as follows.To assess the significance of the coherence score calculated for a set S in the context of a reference set R, we take into account the proteins in R that are functionally related to S. The definition of functional-relatedness is somewhat arbitrary. Therefore, for evaluation purposes, we use three different criteria to decide whether a protein is functionally related to the set S. In turn, these three criteria define three neighbourhoods in the p ∈ R is functionally related to S if sim  ≥ score(S).• The first criterion defines proteins to be functionally related to S if their similarity to the set, as defined in equation (3), is greater than or equal to the coherence score of the set. This establishes a neighbourhood around the most homogeneous proteins of the set. Proteins in S fulfilling this criterion are defined as the 'core' of S, denoted as C(S). Thus, according to the first criterion, a protein p ∈ R is functionally related to S if ∃pi ∈ C(S), sim ≥ score(S).• The second criterion defines proteins to be functionally related to S if their similarity to at least one protein in C(S), as defined in equation (2), is greater than or equal to the coherence score of the set. This second neighbourhood can be described as open to the core of the set (as it captures proteins similar to one protein in the core). Thus, according to the second criterion, a protein p ∈ R is functionally related to S if ∃pj∈ S, sim ≥ score(S)• The third criterion defines proteins to be functionally related to S if their similarity to at least one protein in S, as defined in equation (2), is greater than or equal to the coherence score of the set. This third neighbourhood is open to the set . Thus, according to the third criterion, a protein s and r respectively .Given a reference set R with In summary, the coherence score of a protein set provides a global measure of the functional homogeneity of its proteins. Meanwhile, the significance measures we propose  account for the probability of obtaining a set from a reference with a given number of proteins functionally related to that set, just by chance. Note that the definitions of the three criteria for functional relatedness depend on the coherence score of the set. In this sense, the greater the coherence score, the fewer proteins in the reference will be found to be functionally related. Nevertheless, a particular set with a high coherence score might not be significantly coherent given the reference if it contains only a few of the proteins in the reference that are functionally related to the set (the exact number of proteins to be significant depends on both the size of the set and the number of similar proteins in the reference). Meanwhile, a set with a relatively low coherence score can be significantly coherent with respect to a reference if it contains a certain number of proteins of the reference that are functionally related at that coherence level.We have assessed the validity of our method by performing several analyses. First, we evaluate the method by comparing the results obtained from the analysis of protein sets known to be homogeneous to those obtained from randomly created sets. Secondly, we analyze its robustness in terms of the functional similarity used, the completeness of functional annotation of the organism and the inclusion or exclusion of annotations obtained by automatic methods. Finally, we demonstrate the usefulness of our approach for a particular application: the validation of functional modules obtained from the analysis of protein-protein interaction networks.Saccharomyces cerevisiae. In this scenario, our positive sets  correspond to macromolecular complexes, cellular components and proteins participating in the same pathway. As proteins in a complex or component act co-ordinately, participating in one or more cellular processes, these protein sets are expected to be significantly coherent from the biological process point of view. The same is expected in the case of proteins in the same pathway. Therefore, we restrict our analysis to GO 'biological process' terms (Gene Ontology annotation release 2007–12).To assess the validity of our method for characterizing the functional coherence of a set of proteins, as well as its significance, we analyzed both positive and random sets in the context of one of the most complete and expert-validated annotated genomes: S. cerevisiae were compiled from (i) the Gene Ontology cellular component ontology (release 2007–12), (ii) MIPS complex catalogue (release 18-05-2006) available from the CYGD [Specifically, positive sets from the CYGD  and , namely 'Limonene and pinene degradation', 'Lipoic acid metabolism', 'Tryptophan metabolism' and 'Alkaloid biosynthesis II'.98 protein sets containing at least two proteins annotated with a 'biological process' term were compiled from the Kegg pathways of S. cerevisiae genome. Both direct and hierarchical associations were considered, so a set comprises all the gene products directly annotated with a GOcc term as well as those annotated with its descendants in the GO structure. As some GOcc terms comprised exactly the same set of proteins, we analyzed only distinct sets. Of the 552 distinct protein sets, we analyzed 503 that contained at least two proteins annotated in the 'biological process' category. The coherence scores range from 0.01 (less coherent) to 1 (most coherent), with set sizes in the range 2 to 4682 gene products (corresponding to GO:0005623 'cell'). A simple estimator of 'random' similarity is the average similarity between all possible protein pairs (mean pairwise similarity). Sets that are not significantly coherent according to the three criteria established, with coherence scores above these mean pairwise similarities in the S. cerevisiae genome, are shown in Table For each GO cellular component (GOcc) term we created a protein set comprising the gene products annotated in the et al. [et al. [et al. [The catalogue of MIPS complexes comprises both curated data and the results of systematic analyses of protein complexes based solely on high-throughput methods -34. We het al. , 532 by  [et al.  and 62 bIn order to ensure that our method does not provide significant sets by chance, we analyzed various randomly created sets of different sizes. Out of a total of 100,000 random sets, with a uniform size distribution from 2 to 200 proteins at 2-protein intervals (similar to the sizes of most positive sets), 4455 were found to be statistically significant  according to pv1, 4379 according to pv2 and 682 according to pv3. These figures imply an FDR at or below a p-value of 0.05 using pv1, 0.045 using pv2 and a lower 0.0068 using pv3. The numbers of highly significant sets  drop to 115 (pv1), 104 (pv2) and 20 (pv3) . Additional file S. cerevisiae, though some of them are quite heterogeneous. On the other hand, the proportion of significantly coherent sets corresponding to complexes derived from high-throughput methods stored in the MIPS catalogue [As shown in Figure atalogue -34 is loAs most expert-annotated data on known biologically meaningful sets are statistically significant, while the probability of obtaining significant sets just by chance is low, the measures proposed in this work seem to be valuable criteria for assessing the significance of the functional coherence of a protein set. Therefore, this significance can be used as a means of validating new experimental or hypothetical functional modules .To evaluate the extent to which the statistical significance of the coherence score depends on various conditions such as functional similarity and completeness of annotation, we conducted the following experiments.The coherence score and the neighbourhoods constructed depend on a particular definition of global functional similarity. Therefore, we wanted to test the effect of choosing a different functional similarity measure. For that purpose we used Jaccard similarity on a set representation of gene products. The similarity between two gene products A and B is computed as the ratio of the number of common terms to the number of terms in A and B, as defined in equation (6), with no weights accounting for specificity/generality of terms. The number of significant sets among the expert-annotated datasets was, in general, slightly lower than the results obtained using cosine similarity . As expected from these data, the proportion of statistically significant coherent sets among cellular components in C. albicans is lower than in S. cerevisiae , we analyzed GO cellular components for a closely similar organism for which IEA annotations are plentiful: Schizosaccharomyces pombe (S. pombe). Nearly 20% of the assignments of biological process (BP) terms were inferred from electronic annotations with 270 products annotated only with IEA codes. The electronic annotations increase the number of BP terms per product (from 2.0 to 2.4) and also increase the number of cellular components analyzed. The analyses performed with and without IEA annotations give very similar results  has concentrated on the detection of the modular organization of cellular function . A functInformation about the overlap with known complexes, cellular co-localization, average semantic similarities for pairs of interacting proteins, and phenotype divergence -39 has bS. cerevisiae PPI networks  of a series of functional modules obtained from more recent computational analyses of • Chen & Yuan  used an et al. [• Krogan et al.  obtainedet al. [inter alia a Consolidated PPI network [et al. [et al. [• Pu et al.  performe network  that inc [et al.  and Gavi [et al. .• Dutkowski & Tiuryn  detectedThe conserved modules identified by Dutkowski & Tiuryn  show theet al. [et al. [Moreover, the proportion of significant complexes is greater in the data obtained by the analysis of the Consolidated network by Pu et al.  than in  [et al. . Therefo [et al. , namely S. cerevisiae revealed that most of them are significant in the context of the organism used as reference. However, the coherence scores obtained vary considerably, from very homogenous sets to fairly heterogeneous. This shows that the overall similarity of functional annotations (i.e. the coherence score) is not a good indicator of the functional completeness and separation of a protein set in the context of an organism. Therefore, in addition to measuring the functional homogeneity, a statistical assessment is performed.In this work we present a descriptive method, based on the analysis of functional annotations, for scoring the degree of homogeneity of a protein set and assessing its significance in the context of a reference set. The method has been evaluated using positive and randomly created datasets. Analysis of known biologically meaningful protein sets corresponding to macromolecular complexes, cellular components and pathways of et al. [et al. for pairs of interacting proteins within the same module. In contrast, the coherence score in the present work is computed over all protein-protein pairs within a protein set, as we are not using data on interactions themselves. Secondly, pairwise similarity is computed using dissimilar approaches. The similarity used by Pu et al., as described in [The coherence score proposed in this work is based on previously-defined pairwise functional similarities. Pairwise similarity methods are increasingly used in quite different bioinformatics applications, such as prediction of protein-protein interaction data , priorizet al.  to evaluribed in , is compet al. [In addition to providing a coherence score, our method assesses the statistical significance of the set in the context of the global functional space of a reference, providing an additional quality parameter. This statistical assessment is performed globally, as all functional annotations of each protein in the set are considered together in the analysis. In contrast, most enrichment methods perform a local statistical assessment, as they analyze each functional term individually. In this way, they provide a collection of functional annotations together with their significance values. Therefore, they are mainly used by experts in order to support the functional interpretation of their experimental results. Nevertheless, it is not straightforward to use the information provided by enrichment methods to account for the functional homogeneity of a protein set. For instance, neither the number of enriched functional terms nor the averaged p-values of significant terms have previously been found to be good indicators of the homogeneity of a protein set . In ordeet al. , using Get al. , there iTherefore, the coherence score and corresponding p-values are shown to be valuable indicators of the global functional homogeneity of a protein set, complementing the functional analysis performed by currently available methods. To illustrate the type of information provided by our method and other functional methods, as well as their complementary relationship, we provide the results of the analysis of 'biological process' annotations of one of the functional modules obtained in : 'ModuleS. cerevisiae sets, using an alternative similarity measure (Jaccard), to an incipient annotation project, C. albicans, and to a genome with nearly 20% of biological process term annotations inferred from electronic resources, S. pombe. As with other methods based on functional annotations, the completeness of annotations is by far the most important limiting factor in our methodology.Both the coherence score and significance measures are computed from a set of functional annotations, from which as a first step a similarity is established. This similarity therefore depends, among other things, on the completeness of a genome annotation. In addition, we have applied our method to the analysis of Finally, to illustrate the usefulness of our method, we have applied it to various protein sets corresponding to hypothetical functional modules and complexes obtained from PPI network analysis. Our results seem to agree with and complement other validation criteria, such as evolutionary conservation and overlap with known complexes.We have presented a method that scores the degree of homogeneity, or coherence, of a protein set on the basis of the global similarity of their functional annotations. It uses statistical hypothesis testing to assess the significance of the set in the context of the functional space of a reference set.We can conclude that our method is complementary to previous descriptive functional analysis approaches. On the one hand, like enrichment methods, it analyzes a protein set. On the other, like some pairwise similarity methods, it measures the functional relatedness of proteins from a global point of view. Finally, as in enrichment methods, a statistical test is performed, in our case to evaluate the significance of the global coherence score of the protein set in the context of a reference set. However, in contrast to enrichment methods, it does not provide a functional interpretation of the protein set, as it reports two numerical values  but not functional terms. As such it is a good filter prior to functional interpretation in cases where numerous protein sets are obtained .inter alia – cluster and bicluster analysis of gene expression profiles, protein-protein interaction clusters and sets of hypothetically homologous proteins.The coherence score and corresponding significance measures proposed in this work can be therefore used for validation of experimental sets where functionally homogeneous protein groups are expected. This is the case for – MC conceived the work, programmed the method, performed the analyses and wrote the manuscript. APM and JMC revised both the methodology and manuscript. All authors read and approved the final manuscript.Coherence score and significance measures of random sets.Click here for fileet al. [Functional analysis of 'Module 39' obtained by Pu et al.  using vaClick here for file


Comprehensive exploration of protein-protein interactions is a challenging route to understand biological processes. For efficiently enlarging protein interactions annotated with residue-based binding models, we proposed a new concept "3D-domain interolog mapping" with a scoring system to explore all possible protein pairs between the two homolog families, derived from a known 3D-structure dimmer (template), across multiple species. Each family consists of homologous proteins which have interacting domains of the template for studying domain interface evolution of two interacting homolog families.The 3D-interologs database records the evolution of protein-protein interactions database across multiple species. Based on "3D-domain interolog mapping" and a new scoring function, we infer 173,294 protein-protein interactions by using 1,895 three-dimensional (3D) structure heterodimers to search the UniProt database . The 3D- interologs database comprises 15,124 species and 283,980 protein-protein interactions, including 173,294 interactions (61%) and 110,686 interactions (39%) summarized from the IntAct database. For a protein-protein interaction, the 3D-interologs database shows functional annotations (e.g. Gene Ontology), interacting domains and binding models (e.g. hydrogen-bond interactions and conserved residues). Additionally, this database provides couple-conserved residues and the interacting evolution by exploring the interologs across multiple species. Experimental results reveal that the proposed scoring function obtains good agreement for the binding affinity of 275 mutated residues from the ASEdb. The precision and recall of our method are 0.52 and 0.34, respectively, by using 563 non-redundant heterodimers to search on the Integr8 database (549 complete genomes).http://3D- interologs.life.nctu.edu.tw.Experimental results demonstrate that the proposed method can infer reliable physical protein-protein interactions and be useful for studying the protein-protein interaction evolution across multiple species. In addition, the top-ranked strategy and template interface score are able to significantly improve the accuracies of identifying protein-protein interactions in a complete genome. The 3D-interologs database is available at  A major challenge of post genomic biology is to understand the networks of interacting genes, proteins and small molecules that produce biological functions. The large number of protein interactions -3, gener13[Protein Data Bank (PDB)  stores t13. In addia (in chain A) interacts with domain b (in chain B) in one species. Homolog families A' and B' of A and B are proteins, which are significant sequence similarity BLASTP E-values ≤10-10 and contain domains a and b, respectively. All possible protein pairs between these two homolog families are considered as protein-protein interaction candidates using the template T. Based on this concept, protein sequence databases can be searched to predict protein-protein interactions across multiple species efficiently. When the genome was deciphered completely for a species, we considered the rank of protein-protein interaction candidates in each species into our previous scoring system [To address these issues, we proposed a new concept "3D-domain interolog mapping" Fig. : for a kg system  to reducFigure AC and BC, Fig. AC and S2 aligning to BC). For each interacting candidate, the scoring function was applied to calculate the interacting score and the Z-value, which indicates the statistic significance of the interacting score. An interacting candidate was regarded as a protein-protein interaction if its Z-value was above 3.0 and it ranked in the Top 25 in one species. The candidate rank was considered in one species to reduce the ill-effect of the out-paralogs that arose from a duplication event before the speciation [After 3D-dimer template library and template profiles were built, we inferred candidates of interacting proteins by 3D-domain interolog mapping. To identify the interacting-protein candidates against protein sequences in the UniProt version 11.3 , the chain profile was used as the initial position-specific score matrix (PSSM) of PSI-BLAST in each template consisting of two chains  annotations , Fig. 2DTwo data sets were used to assess 3D-domain interolog mapping and the scoring functions. To determine the contribution of a residue to the binding affinity, the alanine- scanning mutagenesis is frequently used as an experimental probe. We selected 275 mutated  residues from the ASEdb  with 16 S. cerevisiae and in 549 species collected in Integr8 database , comprising 563 dimer protein structures from the set NR1895 to evaluate the performance of our scoring functions for predicting PPIs in proteins ).Z-value), the binding models and functional annotations between the query protein and its interacting partners. The scoring function is defined asWe have recently proposed a scoring function to determine the reliability of a protein- protein interaction . This sttot = Evdw + ESF + Esim + wEconsE (1)vdwE and SFE are the interacting van der Waals energy and the special interacting bond energy , respectively; and simE is the template interface similar score; and the consE is couple-conserved residue score. The optimal w value was yielded by testing various values ranging from 0.1 to 5.0; w is set to 3 for the best performance and efficiency on predicting binding affinity (BA- 275) and predicting PPIs in S. cerevisiae and in 549 species (Integr8) using the data set NR- 563. The vdwE and SFE are given aswhere CP denotes the number of the aligned-contact residues of proteins A and B aligned to a hit template; Vssij and Vsbij (Vsbji) are the sidechain-sidechain and sidechain-backbone van der Waals energies between residues i (in protein A) and j (in protein B), respectively. Tssij and Tsbij (Tsbji) are the sidechain-sidechain and sidechain-backbone special interacting energies between i and j, respectively, if the pair residues i and j form the special bonds  in the template structure. The van der Waals energies  and special interacting energies  were calculated from the four knowledge-based scoring matrices , which is the interacting score for a contact residue i, j pair , of a scoring matrix is defined as ijq and ije are the observed probability and the expected probability, respectively, of the occurrence of each i, j pair. For sidechain-sidechain van-der Waals scoring matrix, the scores are high (yellow blocks) if large-aliphatic residues  interact to large-aliphatic residues or aromatic residues  interact to aromatic residue. In contrast, the scores are low (orange blocks) when nonpolar residues interact to polar residues. The top two highest scores are 3.0 (Met. interacting to Met) and 2.9 (Trp interacting to Trp).These four knowledge-based matrices, which were derived using a general mathematical structure  from a net al., are thesimE was calculated from the BLOSUM62 matrix [simE is defined asThe value of 2 matrix  based onCP is the number of contact residue pairs in the template; i and j are the contact residue in chains A and B, respectively. ii'K is the score of aligning residue i (in chain A) to i' (in protein A') and ji'K is the score of aligning residue j (in chain B) to j' (in protein B') according to BLOSUM62 matrix. iiK and jjK are the diagonal scores of BLOSUM62 matrix for residues i and j, respectively. The couple-conserved residue score (consE) was determined from two profiles of the template and is given bywhere CP is the number of contact residue pairs; ipM is the score in the PSSM for residue type i at position p in Protein A; Mp is the score in the PSSM for residue type j at position p' in Protein B, and iiK and jjK are the diagonal scores of BLOSUM62 matrix for residue types i and j, respectively.where To evaluate statistical significance  of the interacting score of a protein-protein interaction candidate, we randomly generated 10,000 interfaces by mutating 60% contact residues for each heterodimer in 3D-dimer template library. The selected residue was substituted with another amino acid residue according to the probability derived from these 621 complexes . The mea-10. The threshold is E-value ≤10-2 applied in the 3D-partner server and DAPID utilized the E-value as the scoring function. Third, 3D-interologs utilized a new method for scoring the template interface similar score. Furthermore, 3D-interologs added a top ranked strategy for a specific species whose genome is deciphered completely. Finally, 3D-interologs and DAPID are databases, conversely, 3D- partner is a web-based service for identifying interacting partners of a query protein sequence.Some enhancements and modifications were applied to the DAPID database  and the The 3D-interologs database server is easy-to-use. Users input the UniProt AC or the FASTA format of the query protein Fig. . The serThe 3D-interologs database currently contains 15,124 species and 283,980 protein-protein interactions, including 173,294 interactions (61%) derived from our method based on 3D- domain interolog mapping and 110,686 interactions (39%) summarized from the IntAct database [Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, and Escherichia coli. To analyze couple-conserved residues and interface evolutions for providing evolutionary clues, the 15,124 species were divided into 10 taxonomic groups [Among 15,124 species in the 3D-interologs database, Table c groups , namely Figure The protein NXT1 interacts with the protein NXF1 to form a compact heterodimers (PDB code 1jkg )and an iConversely, some positions, which are not conserved in all species but conserved in an individual taxonomic group, are important for observing the co-evolution across multiple species. The interacting residue pair (NXT1 Phe6 and NXF1 Cys415) in mammalia and vertebrata is different from that in metazoan (NXF1 Cys415→Met and NXT1 Phe→Leu variant). The van-der Waals potential  and one matrix (black) proposed by Lu, et al.[et al.). The correlations are 0.91 and 0.88 for 3D-partner and 0.88 (only template similarity), respectively.The enhanced scoring functions were first evaluated on 275 mutated residues selected from the ASEdb database  to reveau, et al.. Among tEcons) is beneficial to model the binding energy of residues positioned in the interfaces. Although the hotspots of protein-protein binding are often for maintaining their function, the antibodies keep the diversity to recognize a wide variation of antigens. The correlation is 0.143 when the consE was used to model the binding energy of antigen-antibody complexes. Fortunately, integrating cons, EsimE and SFE is able to improve the correlation to 0.606 for antigen-antibody complexes.The binding free energy is often not evenly distributed across interfaces but involves a small subset of “hot spots” contributed extraordinarily high energy . For insS. cerevisiae. This set comprised 5,882 protein-protein interactions, which were recorded as the core subset in the DIP database as the positive cases, and 2,708,746 non-interacting protein pairs, defined by Jansen et al.[S. cerevisiae. Among these four scoring functions, the 3D-interologs and the template similar score (simE) were the best and achieved the similar accuracy. Conversely, one matrix , 0.82 (3D-partner), and 0.67 for one matrix . These results demonstrated that the proposed new scoring function can achieve good agreement for the binding affinity in PPIs and provide statistical significance  for predicting PPIs.Additionally, a non-redundant set (NR-563), comprising 563 dimer complexes from the 3D-dimer library, was adopted to evaluate the performance of this enhanced scoring function for interacting partner predictions in u, et al.) was theet al.[To evaluate the performance of the 3D-domain interolog mapping on multiple species, 563 non-redundant dimer complexes (NR-563) were used as queries to search on the Integr8 database (Release 65) which comprises 2,102,196 proteins in 549 species Fig. . The Intet al., to meassimE) outperform the 3D-partner server and one-matrix  method. In addition, the precision and recall were adopted to access the predicted quality of the 3D-interologs using these four scoring schemes , where hA and hF denote the numbers of hit positive cases and hit negative cases, respectively. The recall was defined as hA/A, where A is the total number of positives . Furthermore, the accuracy of our scoring function (red) is significantly better than that of the sequence identity (green).The structures in the NR-563 as queries to search the Integr8 database yielded 1,063 protein-protein interactions recorded in the IntAct database and 131,831 protein pairs, whose CC RSS scores were less than 0.4 as the negative cases. Based on ROC curves Fig.  for predmes Fig. . The preThe 3D-domain interolog mapping may yield many PPI candidates (e.g. > 200) for one species from a structure template because a eukaryote genome frequently contains multiple paralogous genes. Here, we proposed a top-rank strategy to limit the number of PPIs inferred from a structural template in the same species. For example, we discarded the PPI candidates whose ranks ≥ 25 for a species if the rank threshold is set to 25. Figure Adopting the top-rank strategy in one species as the scoring function is useful for distinguishing between positives and negatives when the 3D-domain interolog mapping yielded many protein-protein interactions for one species from a structure template. However, the rank cannot reflect the binding affinity of a PPI candidate, conversely, the Z-score cannot be adopted to identify the orthologs and in-paralogs arising from a duplication event following the speciation . These rTable This work demonstrates that the 3D-interologs database is robust and feasible for the interacting evolution of PPIs and DDIs across multiple species. This database can provide couple-conserved residues, interacting models and interface evolution through 3D-domain interolog mapping and template-based scoring functions. The scoring function achieves good agreement for the binding affinity in protein-protein interactions. We believe that the 3D- domain interolog mapping should be useful in protein-protein interacting evolution and is able to infer reliable physical protein-protein interactions across multiple genomes.The authors declare that they have no competing interests.Conceived and designed the experiments: YSL and JMY. Performed the experiments and analyzed the data: YSL, YCC and JMY. Contributed reagents/materials/analysis tools: YSL, YCC and JMY. Wrote the paper: YCC and JMY