Dengue fever is an increasingly significant arthropod-borne viral disease, with at least 50 million cases per year worldwide. As with other viral pathogens, dengue virus is dependent on its host to perform the bulk of functions necessary for viral survival and replication. To be successful, dengue must manipulate host cell biological processes towards its own ends, while avoiding elimination by the immune system. Protein-protein interactions between the virus and its host are one avenue through which dengue can connect and exploit these host cellular pathways and processes.Homo sapiens and the insect vector Aedes aegypti. Our approach is based on structural similarity between DENV and host proteins and incorporates knowledge from the literature to further support a subset of the predictions. We predict over 4,000 interactions between DENV and humans, as well as 176 interactions between DENV and A. aegypti. Additional filtering based on shared Gene Ontology cellular component annotation reduced the number of predictions to approximately 2,000 for humans and 18 for A. aegypti. Of 19 experimentally validated interactions between DENV and humans extracted from the literature, this method was able to predict nearly half (9). Additional predictions suggest specific interactions between virus and host proteins relevant to interferon signaling, transcriptional regulation, stress, and the unfolded protein response.We implemented a computational approach to predict interactions between Dengue virus (DENV) and both of its hosts, Dengue virus manipulates cellular processes to its advantage through specific interactions with the host's protein interaction network. The interaction networks presented here provide a set of hypothesis for further experimental investigation into the DENV life cycle as well as potential therapeutic targets. Aedes aegypti, after which it begins the infection and replication lifecycle within human cells. To perform the molecular functions required for invasion, replication, and spread of the virus, proteins encoded by DENV must interact with and alter the behavior of protein networks in both of these hosts. In this work, we used a computational method based on protein structures to predict interactions between DENV and its human and insect hosts. We predict numerous interactions, with many involved in known cell death, stress, and immune system pathways. Further investigation of these predicted protein-protein interactions should provide targets to combat the clinical manifestations of this disease in humans as well as points of intervention focused within the mosquito vector.Dengue virus (DENV) represents a major disease burden in tropical and subtropical regions of the world, and has shown an increase in the number of cases in recent years. DENV is transmitted to humans through the bite of an infected mosquito, typically  FlaviviridaeWith over 50 million cases per year, dengue virus (DENV) is a significant and growing threat to world-wide human health. Wide-spread among tropical and sub-tropical regions, this NIAID Category A pathogen consists of four serotypes, DENV1 to DENV4, and is a member of the family Aedes aegypti and to a lesser extent Aedes albopictus. Thus, DENV displays the remarkable capability to survive and replicate in two very different host organisms; accomplished by a genome encoding a mere 10 proteins An arbovirus, DENV is carried and spread to humans by the primary mosquito vector S. cerevisiae, prediction of protein interactions between species are rare, and are especially so for host-pathogen interactions. Recent computational work has considered interactions between P. falciparum and human proteins based on interactions between orthologous eukaryotic proteins or using statistics about protein domains involved in within-species interactions et al. used a data mining approach to predict host-virus interactions based on human protein features and knowledge of existing protein interactions While common within model organism species such as AedesProtein structure information can also be used as a basis for protein interaction prediction Here, we establish a network of predicted interactions between DENV proteins and proteins from its human and insect hosts. These predictions are based on protein structural similarity, where we first determine structural similarities between pathogen and host proteins using an established method for comparing 3D structures. We refer to host proteins having a region of high structural similarity to a DENV protein as “DENV-similar.” Next, we identify known intra-species interactions for these DENV-similar proteins, and refer to the one or more host proteins that they interact with as “targets.” We then assume that the similar structural features seen between DENV proteins and their host DENV-similar counterparts allow the DENV protein to participate in the same interactions as DENV-similar proteins; joining the host protein network at these points . We prioD. melanogaster interactions were taken from DroID v5.0 and IntAct A. aegypti orthologs of D. melanogaster proteins were determined using the Inparanoid ortholog annotation for the D. melanogaster genes in FlyBase v. FB2009 10 http://www.ebi.ac.uk/msd/sifts/) Structures of DENV2 proteins were taken from the PDB , and any DENV2 protein without a known structure was modeled using I-TASSER H. sapiens and D. melanogaster. We refer to these human proteins as hDENV-similar proteins and the fly proteins as dDENV-similar proteins.We investigated protein mimicry using structural similarities from Dali. Dali compares the 3D structural coordinates of two PDB entries by alignment of alpha carbon distance matrices, allowing for differences in domain order, and produces a structural similarity score To predict which human proteins may interact with DENV2 proteins, we sought those target human proteins that interact with the hDENV-similar proteins during cellular processes. To this end, we determined known interactions between hDENV-similar proteins and target human proteins, using data from the Human Protein Reference Database (HPRD) database, which contains literature curated interactions between pairs of human proteins A. aegypti proteins, but with the added step of finding orthologs between A. aegypti and D. melanogaster proteins. Known interactions between the dDENV-similar proteins and other D. melanogaster proteins were taken from DroID, using a cutoff confidence value of 0.4, and IntAct D. melanogaster proteins were found for A. aegypti using FlyBase A. aegypti target protein interacts with the DENV protein.A similar process was used to predict interactions between DENV2 proteins and The Gene Ontology (GO) provides a system of terms to consistently describe and annotate gene products Since there may be multiple PDB structures present in Dali to represent the same protein, there was some redundancy in the interaction predictions. In some cases, multiple PDB structures for the same DENV protein were found to be similar to multiple PDB structures for an DENV-similar protein, leading to the same interaction predictions. Therefore, the predictions were counted as unique pairs of human Uniprot accessions and DENV protein names. In addition, for ease of viewing the predicted interactome, each node representing an DENV protein is labeled with the protein name while each human protein is represented by Entrez GeneID.D. melanogasterA. aegypti orthologs of these host factors were recently curated by Guo et al. Support for the predicted interactions was obtained from literature. As few interactions between DENV and humans are known, we looked within the literature to see if any of them were predicted by our method A. aegypti target proteins were obtained through DAVID 6.7 GO annotations for the human and A. aegypti, C. elegans, and S. cerevisiae, we found proteins from all three of these species that show structural similarity with DENV2 using the Dali server A. aegypti orthologs of C. elegans and S. cerevisiae proteins were determined using InParanoid, and the D. melanogaster orthologs were taken from the InParanoid ortholog annotation for the D. melanogaster genes in FlyBase v. FB2009 10 Guo et al. recently generated a first draft of the mosquito interactome A. aegypti was downloaded from InParanoid 7.0 Refworks:161. Since InParanoid lists human proteins by their Ensembl Protein IDs, mappings to Uniprot accessions were downloaded from Ensembl 57 using BioMart Refworks:192. Orthologous human and A. aegypti targets that were predicted to interact with the same DENV2 protein were identified.The genome-wide set of orthologs between human and H. sapiens and A. aegypti, we employed a method we developed previously in the prediction of protein interactions between HIV and human To develop a network of interactions between DENV and its hosts, A. aegypti proteins. This is not surprising, given there are currently only 17 structures from A. aegypti in the PDB. Therefore, we looked for similarities between DENV proteins and the fly, Drosophila melanogaster, and found 15 proteins with structural similarity to DENV, which were then used as dDENV-similar proteins in downstream analyses.To determine structurally similar host proteins, we used DaliLite to compare DENV structures against every other structure in the PDB A particular challenge in host-pathogen studies is the general lack of interaction data. HIV is perhaps the most well-characterized virus in this regard, with over 800 direct interactions documented in NCBI's HIV-Human protein interaction database (over 2500 interactions if indirect interactions are included) Through a more comprehensive search of the literature, we have found 20 documented interactions between DENV and human proteins . Almost A. aegypti. However, in the C6/36 cell line from Aedes albopictus, tubulin is believed to interact with DENV2 E protein A. aegypti, having been shown to bind to the E protein We are currently unaware of any well-characterized protein-protein interactions between DENV and After determining which host proteins are structurally similar to DENV proteins, we inquired into the known protein-protein interactions that each DENV-similar protein participates in. For the hDENV-similar set, we obtained known human protein interactions from the Human Protein Reference Database (HPRD), which consists of over 37,000 interactions found in the literature D. melanogaster, as well as potential D. melanogaster interactions suggested by the yeast-2-hybrid data sets in DroID D. melanogaster proteins in A. aegypti, since this is the true host of DENV. We then predict that the A. aegypti ortholog of a D. melanogaster protein that interacts with a dDENV-similar protein may also interact with the corresponding DENV protein  annotation to filter our predicted interactions. While this filtering should highlight a smaller set of proteins having additional evidence of interaction, we note that CC annotation is very noisy, with the localization of proteins within a cell being often poorly characterized for even well-studied model organisms, let alone species such as DENV or As GO annotations taken from DAVID for DENV are linked to entries for the DENV polyprotein rather than the individual proteins, all DENV proteins receive the same annotations. To ameliorate this problem and to include proteins with modeled structures, we assigned GO terms to the sequence corresponding to each DENV protein structure using a different tool, GOanna, which finds possible annotations based on the annotations of highly significant BLAST hits A. aegypti, the reduction in predictions was more pronounced, with only 17 predictions and 12 targets passing the CC filter (A. aegypti).We used this information to create a smaller list of predicted interactions, containing only those predictions where the DENV protein and the host target share at least one GO CC term. After CC filtering, there were 2,073 predicted interactions between DENV and 1,099 human proteins. Seven of the 19 known DENV-human interactions remained after filtering (reduced from 9 of 19), as well as 20 interactions involving host factors . The twoC filter . One of It is difficult to judge the accuracy of predictions for protein interactions in host-pathogen systems, especially for those pathogens such as DENV that have received less attention than their worldwide burden deserves. However, our results include essentially half (47%) of the known interactions between DENV and human proteins, as well as a significant number of proteins from both human and fly that have been suggested to play a functional role in DENV infection We have also applied this methodology to the prediction of protein interactions between HIV and human, a host-pathogen system for which much is known, although the same problems still exist to some extent A. aegypti interactome was predicted by assuming that the mosquito orthologs of interacting proteins in C. elegans, D. melanogaster, and S. cerevisiae would also interact As discussed earlier, the major challenge in predicting protein interactions for pathogens is the lack of interaction data. A further complication is that, in addition to the lack of known host-pathogen interactions for DENV2, little is known about protein interactions within the mosquito vector itself. Very recently, a draft of the A. aegypti. Any A. aegypti proteins predicted to interact with these orthologs, according to the recently published mosquito interactome, were predicted to interact with DENV2. As a result, we found 263 mosquito targets participating in 351 interactions with DENV2 proteins  that showed structural similarities to DENV2 proteins. We then mapped these proteins to orthologs in proteins . This cans alone . The larns alone .A. aegypti, worm and yeast may be too great to provide accurate predictions of protein interactions using orthologs. A network of the fourteen predictions made using both datasets is given in A. aegypti by finding orthologs of proteins from any host species shown to interact with proteins from any flavivirus A. aegypti proteins they predict to interact with DENV2  are present among the predictions made with our method using Drosophila experimental interactions alone. In addition, we predict them to interact with different DENV2 proteins than what is predicted in It is difficult to directly assess prediction quality due to the absence of known interactions between DENV2 and mosquito. However, we note that despite the larger number of predictions, fewer involved proteins that were suggested to be host factors . This suggests that the larger number of predictions may represent an increase in the number of false positive predictions. Indeed, some of the within mosquito interactions used to produce this larger set of interactions may also be false positives, as the interactions of We note several links between DENV pathogenesis, stress responses, and apoptosis among our predictions and in the literature. The GO term “regulation of stress response” is enriched among hDENV-similar proteins, as well as several terms related to apoptosis among the human target proteins. Several potential DENV receptors are involved in stress responses, such as HSP90, HSPA4, and HSPA5 Activation of PKR-like ER kinase (PERK), leads to phosphorylation of the eukaryotic initiation factor 2ATF6 is a bZIP family transcription factor that transits from the ER to the Golgi in response to ER stress. It undergoes processing in the Golgi and transits to the nucleus, leading to upregulation of multiple apoptosis-relevant genes and eventual apoptosis. While we do not predict any direct interactions with ATF6, we do predict interactions between NS2A, NS4B, and C with associated pathway member NFYA, which forms a complex with ATF6 in response to ER stress In the third branch of the UPR, the ER transmembrane protein IRE1, containing both kinase and RNase activities, becomes autophosphorylated and activated in response to ER stress, leading to XBP-1 splicing and translation of UPR relevant genes. Both DENV2 and Japanese Encephalitis Virus infection have been shown to activate XBP1 and its downstream genes in N18 mouse neuroblastoma cells, reducing the cytopathic effect of the virus A recent study investigating protein interactions between DENV envelope protein and host proteins described direct interactions between E and BiP (HSPA5), Calnexin (CANX) and Calreticulin (CALR) In addition, several studies have implicated NS3 in DENV-induced apoptosis. The ability of DENV1 to cause apoptosis in HepG2 cells differs across strains. The mouse neurovirulent strain FGA/NA d1d differs from its parental strain, FGA/89 by 4 mutations, one of which leads to a non-conservative substitution in NS3. FGA/NA d1d was shown to have a reduced capacity to induce apoptosis, although whether this was mediated by the mutation in NS3 or by one of the other mutations, which were all in the E protein, is unclear A. aegypti are known to use conserved defenses against DENV infection, involving several signaling pathways of the innate immune system, which is consistent with our finding of enriched GO terms related to the immune system among the target proteins of both humans and A. aegypti. In particular, the JAK-STAT signaling pathway has been shown to modulate susceptibility to DENV infection, in both mosquitos and humans Humans and response . When IFIn fact, while infection induces an interferon response, several studies have shown that DENV interferes with the signaling pathway downstream of IFN-A few specific proteins have been identified as modulators of the IFN response. For instance, IFN-Our predictions suggest many potential interactions between DENV and multiple human proteins involved in the JAK-STAT pathway . In partA. aegypti to find proteins which are orthologous between the two hosts, as well as predicted to interact with the same DENV protein in each. We found 47 pairs of orthologs that were predicted to interact with the same DENV protein . Five of the interactions known to occur between DENV and human proteins involve the mRNA processing proteins HNRNPK, HNRNPC, PTBP1, and SSB To examine the functional role of these conserved interactions, we performed GO term enrichment for biological process and molecular function. No molecular function terms were significantly enriched . Most of the biological processes represented by orthologous interactions in both hosts were also found enriched in the predictions for a single host. For example, at least 17 of the human proteins and their orthologs in Culex pipiens quinquefasciatus mosquitos, West Nile Virus, another flavivirus, was also found near synapses and in synaptic vesicles A. aegypti, altering the mosquito's feeding behavior by prolonging feeding and possibly enhancing the spread of DENV3 by making it more likely that feeding will be interrupted, and the mosquito will have to feed on additional humans In addition, we found a number of enriched GO terms relating to the regulation or formation of synapses among the predictions conserved in both species. Previously, the term “brain development” was enriched in the set of dDENV-similar proteins. DENV2 virus particles have been found in vesicles near the presynaptic membrane in spinal cords of SCID mice, and it was suggested that fusion of these vesicles at the synapse might aid the spread of DENV2 from neuron to neuron The processes enriched in the predicted interactions conserved between the two hosts are consistent with the effects of DENV infection in each host. In particular, many of the terms enriched among the orthologous predictions are similar to terms enriched for mosquito predictions. This is not necessarily surprising, given that the mosquito prediction set is much smaller than the human one, but indicates that orthologous predictions in humans were made corresponding to many of the mosquito predictions. However, we found terms involving cell morphogenesis enriched among the orthologous predictions, but not within the predictions specific to either host. Therefore, the mosquito-specific predictions do not completely overlap with the human predictions, and new processes key to DENV infection in both hosts can be revealed.H. sapiens and A. aegypti. The computational methodology employed to generate this map assumes that proteins with comparable structures will share interaction partners. Therefore, we predict that DENV2 proteins may merge into the host protein interactome at the points normally occupied by structurally similar host proteins, creating an interface for the manipulation of downstream host processes. From previous studies, a number of human and fly proteins have been suggested to play some role in DENV2 infection, although the nature of this role is unknown in most cases. Using this methodology, we are able to make predictions regarding which host proteins may impact viral infection through interactions with specific DENV2 proteins. We note that the structural-based methodology here provides a larger picture of the interaction network, while more subtle changes at the sequence level are likely to explain experimentally observed differences in strain effects. Given the paucity of both structural and interaction data for this system, we cannot determine fine differences between strains, but this may be elucidated by further study. The networks presented here may help to provide a set of hypotheses for further investigation, potential therapeutic intervention, as well as help in improving our understanding of the DENV life cycle.We have created a map of potential protein-protein interactions between the host-pathogen triad DENV2 and its hosts Figure S1A. aegypti proteins based on data from Guo et al. A. aegypti targets, and pink is for terms from DENV-similar proteins. When more than ten terms were enriched for a set of proteins, only the ten most signicant terms are shown. Bonferroni corrected p-values were transformed by -log10. The following abbreviations are used: "translation factor nucleic acid bind" is "translation factor activity nucleic acid binding," and "macromolecular subunit organiz" is "macromolecular complex subunit organization."GO term enrichment of (1.39 MB TIF)Click here for additional data file.Figure S2Interactions predicted using both the original fly data and the mosquito interactome.(0.51 MB TIF)Click here for additional data file.Table S1Predicted interactions between DENV2 and human. Columns include the the DENV protein's Entrez Protein accession or PDB code, Uniprot accession, and name; the DENV-similar protein's PDB code, Uniprot accession, Entrez Gene ID, and Symbol; the target protein's Uniprot accession, Entrez Gene ID, and Symbol; if the target is a known host factor; and if the predicted interaction was already known in the literature.(4.18 MB TXT)Click here for additional data file.Table S2A. aegypti. Columns include the the DENV protein's Entrez Protein accession or PDB code, Uniprot accession, and name; the DENV-similar protein's PDB code, Uniprot accession, Entrez Gene ID, and Symbol; the D. melanogaster target protein's FlyBase gene number, and Symbol; the A. aegypti target protein's VectorBase protein ID, and Uniprot accession; and if the target is a known host factor.Predicted interactions between DENV2 and (4.18 MB TXT)Click here for additional data file.Table S3Predicted interactions between DENV2 and human after the CC Filter. Columns include the the DENV protein's Entrez Protein accession or PDB code, Uniprot accession, and name; the DENV-similar protein's PDB code, Uniprot accession, Entrez Gene ID, and Symbol; the target protein's Uniprot accession, Entrez Gene ID, and Symbol; if the target is a known host factor; and if the predicted interaction was already known in the literature.(3.01 MB TXT)Click here for additional data file.Table S4A. aegypti after the CC Filter. Columns include the the DENV protein's Entrez Protein accession or PDB code, Uniprot accession, and name; the DENV-similar protein's PDB code, Uniprot accession, Entrez Gene ID, and Symbol; the D. melanogaster target protein's FlyBase gene number, and Symbol; the A. aegypti target protein's VectorBase protein ID, and Uniprot accession; and if the target is a known host factor.Predicted interactions between DENV2 and (0.01 MB TXT)Click here for additional data file.Table S5A. aegypti target proteins. Columns include the the DENV protein's Entrez Protein accession or PDB code, and name; the A. aegypti target protein's VectorBase protein ID, Uniprot accession, and if it is a known host factor; the human target protein's Uniprot accession, Symbol, if it is a known host factor, and if the human interaction was already known in the literature.Predictions involving orthologous human and (0.01 MB TXT)Click here for additional data file.Table S6A. aegypti. Columns include the the DENV proteins Entrez Protein accession or PDB code, Uniprot accession, and name; the DENV-similar proteins PDB code, Uniprot accession, species, gene ID, and description; the A. aegypti ortholog proteins VectorBase pro- tein ID, geneID, and Uniprot; the A. aegypti target proteins VectorBase protein ID, gene ID, Uniprot accession, and description; and if the target is a known host factor or was also predicted by another group Predicted interactions between DENV2 and (0.04 MB TXT)Click here for additional data file.


Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. Proteins contain one or more domains each of which could have evolved independently from the rest of the protein structure and which could have unique functions,2. BecauThe output of a ranking procedure is usually a list of database protein domains that are ranked in descending order according to a measure of their similarity to the query domain. The choice of a similarity measure largely defines the performance of a ranking system as argued previously. A largePairwise protein domain comparison algorithms compute the similarity between a pair of protein domains either by protein domain structure alignment or by comparing protein domain features. Protein structure alignment based methods compare protein domain structures at the level of residues and sometime even atoms, to detect structural similarities with high sensitivity and accuracy. For example, Carpentier et al. proposed YAKUSA⊤ is the graph weight vector.where xq to database protein domains is unknown and thus the optimal graph weights μ cannot be learned in a supervised way. However, all the SCOP-fold labels of protein domain in the database are known, making the supervised learning of μ in an off-line way possible. We treat each database protein domainyq =⊤ as known because all the SCOP-fold labels are known for all the database protein domains as, In the on-line querying procedure, the relevance of query U = IN×N as a N × N identity matrix. The ranking score vector of the q-th database protein domain is also defined as fq =⊤. Substituting fq, yq and U to (1) and (14) and combining them, we have the optimization problem for the q-th database protein domain as, Therefore, we set μ over-fitting to one single graph, we also introduce the l2 norm regularization term ||μ||2 to the object function. The difference between fq and yq should be noted: fq∈{1,0}N plays the role of the given ground truth in the supervised learning procedure, whilefq is the ideal solution of yq, it is not always achieved after the learning. Thus, we introduce the first term in (16) to make yq as similar to fq as possible during the learning procedure.To avoid the parameter q = 1,…,Nas queries to learn μ, we obtain the final objective function of supervised multiple graph weighting and protein domain ranking as, Using all protein domains in the database F = is the ranking score matrix with the q-th column as the ranking score vector of q-th protein domain, and Y = is the relevance matrix with the q-th column as the relevance vector of the q-th protein domain.where F and μ. At each iteration, either F or μ is optimized while the other is fixed, and then the roles are switched. Iterations are repeated until a maximum number of iterations is reached. Because direct optimization to (17) is difficult, instead we adopt an iterative, two-step strategy to alternately optimize Optimizing F: By fixing μ, the analytic solution for (17) can be easily obtained by setting the derivative of O with respect to F to zero. That is, • Optimizing μ: By fixing F and removing items irrelevant to μ from (17), the optimization problem (17) is reduced to, • em = Tr(F⊤LmF) and e =⊤. The optimization of (19) with respect to the graph weight μ can then be solved as a standard quadratic programming (QP) problemN×N where yiq if li = lq, 0 otherwise; Initialize the graph weights asm = 1,⋯,M;Construct the relevance matrix for t = 1,⋯,T doFt according toUpdate the ranking score matrix previousμt according toUpdate the graph weight Ft by (19);updated endfor Output graph weight μ = μt.x0, its SCOP type label l0 will be unknown and the domain will not be in the databasex0, we extend the size of database to N + 1 by adding x0into the database and then solve the ranking score vector for x0 which is defined asGiven a newly discovered protein domain submitted by a user as query Laplacian matrix L: We first compute the m graph weight matricesx0,x1,⋯,xN}. Then with the graph weight μ learned by Algorithm 1, the new Laplacian matrix L can be computed as in (13).On-line graph weight computation: When a new query x0 is added to the database, we calculate its K nearest neighbors in the databaseWj0 and Wj0, j = 1,⋯,N. If adding this new query to the database does not affect the graph i n the database space, the neighbors and weights Wij, i,j = 1,⋯,N for the protein domains in the database are fixed and can be pre-computed off-line. Thus, we only need to compute N edge weights for each graph instead of (N + 1) × (N + 1).• Relevance vector y: The relevance vector for x0 is defined asy0 = 1 known and yi, i = 1,⋯,Nunknown.• Matrix U: In this situation, U is a (N + 1)×(N + 1)diagonal matrix with U00 = 1 and Uii = 0, i = 1,⋯,N.• f can be solved as, Then the ranking score vector The on-line ranking algorithm is summarized as Algorithm 2MultiG-Rank: on-line ranking algorithm.Require: protein domain databaseRequire: Query protein domain x0;Require: Graph weight μ;N + 1) size by adding x0 and compute M graph Laplacians of the extended database; Obtain multiple graph Laplacian L by linear combination of M graph Laplacians with weight μ as in (13); Construct the relevance vectory0 = 1 and diagonal matrixUii = 1 if i = 0 and 0 otherwise; Solve the ranking vector f for x0 as in (20); Ranking protein domains inf in descending order.Extend the database to , else it is identified as a false positive (FP). For a database protein domainA ranking procedure is run against the protein domains database using a query domain. A list of all matching protein domains along with their ranking scores is returned. We adopted the same evaluation metric framework as was described previously, and useTPR, FRP, recall and precision values are obtained.By varying the length of the returned list, different FPR as the abscissa and TPR as the ordinate, the ROC curve can be plotted. For a high-performance ranking system, the ROC curve should be as close to the top-left corner as possible.Using Using recall as the abscissa and precision as the ordinate, the recall-precision curve can be plotted. For a high-performance ranking system, this curve should be close to the top-right corner of the plot.The AUC is computed as a single-figure measurement of the quality of an ROC curve. AUC is averaged over all the queries to evaluate the performances of different ranking methods.We first compared our MultiG-Rank against several popular graph-based ranking score learning methods for ranking protein domains. We then evaluated the ranking performance of MultiG-Ranking against other protein domain ranking methods using different protein domain comparison strategies. Finally, a case study of a TIM barrel fold is described.540 query set. The average ranking performance was computed over these 540 query runs. We compared our MultiG-Rank to two graph-based ranking methods, G-Rank and GT, and agaThe figure shows the ROC and the recall-precision curves obtained using the different graph ranking methods. As can be seen, the MultiG-Rank algorithm significantly outperformed the other graph-based ranking algorithms; the precision difference got larger as the recall value increased and then tend to converge as the precision tended towards zero  which calls the phipsi program  to format the database. YAKUSA compares a query domain to a database and returns a list of the protein domains along with ranks and ranking scores. We used the default parameters of YAKUSA to perform the ranking of the protein domains in our database. The SHEBA software (version 3.11) source code was downloaded fromhttps://ccrod.cancer.gov/confluence/display/CCRLEE/SHEBA, complied and used it for ranking. The protein domain database was converted to “.env” format and the pairwise alignment was performed between each query domain and each database domain to obtain the alignment scores. First, we compared the different protein domain-protein domain ranking methods and computed their similarity or dissimilarity. An ordering technique was devised to detect hits by taking the similarities between data pairs as input. For our MultiG-Rank, the ranking score was used as a measure of protein domain-protein domain similarly. The ranking results were evaluated based on the ROC and recall-precision curves as shown in FigureWe compare the MultiG-Rank against several other popular protein domain ranking methods: IR Tableau, QP tablThe results in TableTo evaluate the effect of using protein domain descriptors for ranking instead of direct protein domain structure comparisons, we compared IR Tableau with YAKUSA and SHEBA. The main differences between them are that IR Tableau considers both protein domain feature extraction and comparison procedures, while YAKUSA and SHEBA compare only pairs of protein domains directly. The quantitative results in TableThis result strongly suggests that ranking performance improvements are achieved mainly by graph regularization and not by using the power of a protein domain descriptor.Plots of TPR versus FPR obtained using MultiG-Rank and various field-specific protein domain ranking methods as the ranking algorithms are shown in Figureα-helices and eight parallel β-strands that alternate along the peptide backbone[Besides considering the results obtained for the whole database, we also studied an important protein fold, the TIM beta/alpha-barrel fold (c.1). The TIM barrel is a conserved protein fold that consists of eight backbone. TIM barWhen the returned database protein domain is from the same fold type as the query protein domain.When the returned database protein domain is from the same superfamily as the query protein domain.When the returned database protein domain is from the same family as the query protein domain.The ROC and the recall-precision plots of the protein domain ranking results of MultiG-Rank for the query TIM beta/alpha-barrel domain at the three levels are given in FigureThe proposed MultiG-Rank method introduces a new paradigm to fortify the broad scope of existing graph-based ranking techniques. The main advantage of MultiG-Rank lies in its ability to represent the learning of a unified space of ranking scores for protein domain database in multiple graphs. Such flexibility is important in tackling complicated protein domain ranking problems because it allows more prior knowledge to be explored for effectively analyzing a given protein domain database, including the possibility of choosing a proper set of graphs to better characterize diverse databases, and the ability to adopt a multiple graph-based ranking method to appropriately model relationships among the protein domains. Here, MultiG-Rank has been evaluated comprehensively on a carefully selected subset of the ASTRAL SCOP 1.75 A protein domain database. The promising experimental results that were obtained further confirm the usefulness of our ranking score learning approach.The authors declare no competing interests.JW invented the algorithm, performed the experiments and drafted the manuscript. HB drafted the manuscript. XG supervised the study and made critical changes to the manuscript. All the authors have approved the final manuscript.


Bioinformatics can be used to predict protein function, leading to an understanding of cellular activities, and equally-weighted protein-protein interactions (PPI) are normally used to predict such protein functions. The present study provides a weighting strategy for PPI to improve the prediction of protein functions. The weights are dependent on the local and global network topologies and the number of experimental verification methods. The proposed methods were applied to the yeast proteome and integrated with the neighbour counting method to predict the functions of unknown proteins.A new technique to weight interactions in the yeast proteome is presented. The weights are related to the network topology  and the number of identified methods, and the results revealed improvement in the sensitivity and specificity of prediction in terms of cellular role and cellular locations. This method (new weights) was compared with a method that utilises interactions with the same weight and it was shown to be superior.A new method for weighting the interactions in protein-protein interaction networks is presented. Experimental results concerning yeast proteins demonstrated that weighting interactions integrated with the neighbor counting method improved the sensitivity and specificity of prediction in terms of two functional categories: cellular role and cell locations. Determining protein functions is an important challenge in the post-genomic era and Automated Function Prediction is currently one of the most active research fields. Previously, researchers have attempted to determine protein functions using the structure of the protein and comparing it with similar proteins. Similarities between the protein and homologues from other organisms have been investigated to predict functions. However, the diversity of homologues meant that these time-consuming methods were inaccurate. Other techniques to predict protein functions including analyzing gene expression patterns ,2, phyloNetwork topology represents an interaction between proteins and the mechanism of that interaction. Therefore, much information can be extracted from these networks with regards to the strength of the interaction and its contribution to new function prediction, i.e. weighted contribution. A PPI network can be described as a complex system of proteins linked by interactions, and the computational analysis of PPI networks begins with the representation of the PPI network structure ,16. The Neighbour counting is a method proposed by Schwikowski et al. [k functions are considered and assigned to the un-annotated protein. Some papers used this technique with k equalling three. This method makes use of information from the neighbours, but it has several drawbacks: (1) it considers the interactions to be of equal weights, which is not logical; (2) it does not consider the nature of the function and whether it is dominant; (3) it does not provide a confidence level for assigning a function to the protein. The problem of confidence levels was addressed in [The computational analysis of PPI networks is challenging and faces major problems. The first problem concerns the unreliability of protein interactions derived from large-scale experiments, which have yielded numerous false positive results (Y2H). Secondly, a protein can have more than one function and could be considered in one or more functional groups, leading to overlapping function clusters. The third problem concerns the fact that proteins with different functions may interact. Therefore, a PPI has connections between proteins in different functional groups, leading to expansion of the topological complexity of the PPI networks. i et al.  to inferessed in , where tDeng et al.  considerThis paper presents a new method for predicting protein function based on estimating a weight for the strength of the interaction between proteins in the PPI. The similarity between protein interactions and the connected routers in a certain autonomous number of networks was explored. Applying the idea of a network linked list of protocols such as OSPF (Open Shortest Path First) can allow information concerning surrounding routers to be obtained, according to the principles of cost and level (hop count) ,23. The overlapped proteins for "Biochemical function", "Subcellular location" and "Cellular role" were 1145, 1129 and 1407, respectively. In the first three Figures, the relationship between sensitivity and specificity was implemented for biochemical function, cell location and cellular role, respectively. In terms of the prediction method (neighbour counting method), a fixed number of the highest frequency functions can be compared. In the present study, although one data set is used, k (number of interactions) had a variety of values (from 2 to 5). Figures The proposed approach was applied to infer the functions of un-annotated proteins in yeast and used weighting interactions rather than free weights . In YPD, proteins are assigned functions based on three criteria: "Biochemical function", "Subcellular location" and "Cellular role". The numbers of annotated and un-annotated proteins, based on the three functional categories, are presented in Table The majority of methods concerning the estimation of protein functions through protein-protein interactions (PPI) use the same weights for all interactions. Such methods do not consider the various situations for each interaction including the number of experimental methods used to identify the interactions, the number of leaves connected to the interaction (whether or not the protein is sticky) and the most common graphs for the studied species within the network. Therefore, this research introduces new weights for protein interactions to enhance protein function prediction. These weights are W1-W5, and W1 depends of the number of experimental methods that identify the interaction. W1 has high confidence (100%) when the number of experimental methods used is more than one. W2 depends on the number of leaves connected to the studied interactions, which indicates whether the protein is sticky or not. The high confidence of W2 is apparent when the IG1 value is less than three (the protein is not sticky). W3 relates to the value of IG2, which indicates the global topology of the network of the studied species; its value is highly confident when IG2 is less than zero. In addition, there are two estimated weights, W4 and W5. W4 is the average of the basic weights , and W5 is the PCA value for the same weights. Applying the suggested weights to yeast protein functions and integrating these weights with the neighbor counting method led to enhanced results in two function categories: cell location and cellular role. The sensitivity and specificity of every point on the curves of the two function categories were higher than those obtained using the weightless technique ). W3 was the best weight to use in the cellular role category when the numbers of interactions were 3, 4 or 5. The cell location function category did not have a common weight for all cases but in each case (number of interactions), there was a better weight compared with other methods.link is an interface on a router and the protocols are the control system of all connected routers. The protocol includes information concerning the interface's IP address/mask, the type of network (ethernet (broadcast) or serial point-to-point link), the cost of that link and any neighbour routers on that link. In the protein system, a generic protocol is followed that identifies the protein by name , ID (determined number for each protein), sequence (amino acids in given number and order) and functions (if known). The type of network will be elucidated; interaction between two proteins (protein pair) or dense interactions (cluster), and the weight of the interaction (our contribution). Furthermore, neighbours of the adjacent protein (known interactions in the network) are identified  act as the connection between two routers Figures  and 5, wed Table . The prohttp://mips.gsf.de) for the yeast Saccharomyces cerevisiae [There is a difference between the degree and the level of any node. The degree of a node (protein) is defined as the total number of connected nodes or proteins directly surrounding this node  as shown in Figure revisiae . As demoProtein-protein interaction weights are introduced and each interaction has a specific weight. Three basic methods were considered in terms of calculating the weights of all the interactions and overcoming problems affecting the interaction network. The first method concerns the number of experimental methods. Protein-protein interactions are identified by high-throughput experimental methods such as Y2H -29, massThe second method for calculating weights of interactions is the IG1 concept  -41. A neThe third method for calculating the weight uses the IG2 concept , ,43. ThisAfter collecting the levels for each protein and the five different weights, the neighbour counting method (frequencies of interaction partners having certain functions of interest) was implemented to predict the functions of unknown proteins. The new weights and weightless ) algorithms were compared for proteins having up to five interactions. This demonstrated that for most selected new weights at a specific specificity (SP), the sensitivity (SN) was higher than obtained using W0. As demonstrated in Figures The sensitivity (SN) and specificity (SP) can be defined as:i nis the number of observed functions for protein iPWhere i mis the number of predicted functions for protein iP,i kare the overlaps between them.and The authors declare that they have no competing interests.KS collected the biological data, participated data implementation and drafted the manuscript. NS participated in the statistical analysis. YK conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.


Protein–protein interactions in cells are widely explored using small–scale experiments. However, the search for protein complexes and their interactions in data from high throughput experiments such as immunoprecipitation is still a challenge. We present "4N", a novel method for detecting protein complexes in such data. Our method is a heuristic algorithm based on Near Neighbor Network (3N) clustering. It is written in R, it is faster than model-based methods, and has only a small number of tuning parameters. We explain the application of our new method to real immunoprecipitation results and two artificial datasets. We show that the method can infer protein complexes from protein immunoprecipitation datasets of different densities and sizes.4N was applied on the immunoprecipitation dataset that was presented by the authors of the original 3N in Cell 145:787–799, 2011. The test with our method shows that it can reproduce the original clustering results with fewer manually adapted parameters and, in addition, gives direct insight into the complex–complex interactions. We also tested 4N on the human "Tip49a/b" dataset. We conclude that 4N can handle the contaminants and can correctly infer complexes from this very dense dataset. Further tests were performed on two artificial datasets of different sizes. We proved that the method predicts the reference complexes in the two artificial datasets with high accuracy, even when the number of samples is reduced.4N has been implemented in R. We provide the sourcecode of 4N and a user-friendly toolbox including two example calculations. Biologists can use this 4N-toolbox even if they have a limited knowledge of R. There are only a few tuning parameters to set, and each of these parameters has a biological interpretation. The run times for medium scale datasets are in the order of minutes on a standard desktop PC. Large datasets can typically be analyzed within a few hours. Protein–protein interactions (PPIs) constitute the core of inner cell communication,2. LargeProtein complexes work as functional units in the interaction network, and the members of a complex do not appear individually. For this reason, proteins of the same complex are predicted to have a similar abundance in different IP–experiments. Consequently, a similarity-based cluster analysis of these datasets leads to clusters that represent the protein complexes. The clustering should be based on both parts of the information, namely, the occurrence of proteins in the samples and their relative abundance values. Complex–complex interactions (CCIs) as a coarser view on the PPI network can be represented by a clustering in which clusters are allowed to share proteins as proof of the interaction.Large–scale IP/MS study by Gavin et al. first described such analysis for proteome wide characterization of protein complexes in yeast. SubsequA medium scale IP dataset for the components of the human Tip49a/b–PPI complex was presented by Sardiu et al. and used to illustrate complex inference. SeveralThe different clustering methods are scattered widely, and all studies concluded that there is no standard method available currently that is suitable for clustering all kinds of IP–based interaction data. In addition, some of those methods, for example biclust, involve many parameters and are very time–consuming as they need many iterations to get to a result.Malovannaya et al. introduced a method called Near Neighbor Network clustering (3N) for predicting core protein complexes in their large–scale IP/MS study on human cells. This alIn this study, we present a new universal complex inference method that is based on the idea of 3N clustering which we call "4N". Most of its parameters can be set automatically; moreover, it contains tools for visualizing complexes and their interactions. Major advantages of 4N over other methods include that it can process very large datasets, that it allows an immediate insight into the data and that it directly shows the effect of the parameters on the cluster result. 4N is simple and much faster than methods based on probabilistic models, such as biclust. The high speed of 4N in combination with the visualisation tools allows the quick interactive search for protein complexes. A software toolbox written in R is available in the software section on the website www.bdagroup.nl. It includes the algorithm itself, documentation, running examples and methods for plotting and evaluation of the cluster result based on given reference complexes.The article starts with the description of the empirical and simulated IP datasets that we used for testing 4N. The 3N and 4N methods are described in detail thereafter. The article closes with detailed results for all datasets and a Discussion and conclusion section.U of 0.125 to "malovIP" and removed all proteins that were not assigned to near neighbor networks. The subset contained, besides many other proteins, all protein subunits of the Integrator[For a direct comparison with 3N, we did an analysis using our methods on the very large dataset from the original 3N publication, which ctegrator, Mediatotegrator,21, and tegrator, the "meThe next dataset that we analyzed is the "human Tip49a/b" from. We obtahttp://www.string-db.org,[Unlike PPI networks from yeast–two–hybrid studies, only a few IP–based datasets are available; information about reference complexes especially is rare. We created another small–scale, and a medium–scale IP dataset from two simulated networks for this reason. The two PPI networks were created with help of the protein interaction database string-DB .Snap25 to search for proteins that are connected to it in string-DB. We extended this network further with indirectly connected proteins, until we obtained a network with 73 nodes and 116 edges  with help of the plugin clusterViz (http://chianti.ucsd.edu/cyto_web/plugins/displayplugininfo.php?name=ClusterViz) and using the MCODE method by[We first searched for complexes in these two networks using the software cytoscape , then high for its direct interactants and lower for the indirectly connected proteins. Thus, antibodies that target different complex subunits are expected to pull out slightly different protein subsets from the complex, which leads to redistribution of measured abundances for each protein.Kim et al. presented the following method to simulate this effect on graphs, where aThis paragraph contains a brief description of the 3N algorithm, details can be found in the Additional fileU,V in the IP-matrix as arccos(U ∗ V/||U|| × ||V||) like explained in[3N defines the near neighbor network for a certain protein as the set of proteins that appear reciprocally or together, and have correlated abundance values with this protein across the different-antibody samples where it has its highest abundance. The correlation of two proteins is defined in terms of the cosine-distance between their rows ained in and it hAlthough the original 3N algorithm performs well on the kind of data described in, it has C) is relatively weak for separating proteins that do not belong together, as two random vectors of only positive values are more likely to have a cosine-distance < 65 than one between 65 and 90. The 3N algorithm was not published as software implementation by the authors, which makes it impossible for others to immediately apply it to their own IP data.To calculate the near neighbor network for one protein, 3N considers only the samples where this protein has its highest abundance values . This can lead to a loss of proteins when the length of the Top list (parameter L) is too short. A set of close proteins is only detected as core complexes when all their NNNs contain the complete protein set. Less closely connected complexes easily become predicted incompletely. A threshold of 65 for the cosine-distance . In our analysis it is set to 40 such that the algorithm is able to keep proteins in one cluster that build an unbranched chain in the network. The value was determined experimentally using the two artificial datasets. This parameter has an influence on the absolute number of proteins in NNNs. The other external parameter (P), which is used to decide which core complexes should be joined finally, does not influence the total number of proteins that are put into complexes. Below we give an overview of 4N; a detailed description can be found in the Additional fileOnly two external parameters remain that need to be optimized manually for different datasets. One of them is the cosine-distance threshold parameter  is set to 0.01 initially to assign all proteins that fulfil this requirement to a near neighbor network. After this, U is set to the highest possible value, where each of the proteins still remains in at least one NNN. The cosine-distance threshold (C) is set to 40. The core complexes are calculated from the NNNs with the highest possible value for the Parameter S (Jaccard coefficient threshold for building CCs), for which all proteins occur in at least one core complex. Result of this step is set of core complex proteins for each protein in the dataset. These sets of proteins overlap highly, especially when they are built with a low S. For this reason, we added a step to join the core complexes (step "joinCC") based on the relative number of shared proteins.Joining core complexes We define the overlap between two complexes as the relative number of proteins in the smaller complex that occurs in the larger one. When the threshold for joining core complexes (P) is smallest, all core complexes that share at least one protein are joined, which leads to completely distinct sets of joined core complexes. They represent groups of core complexes that interact within but not across the groups. Higher thresholds create smaller sets of partly overlapping complexes that stand for core complex isoforms, a value of 1 creates sets that represent MEMOs. Here, two complexes are only joined when the larger one contains all proteins of the smaller one.P in a range from 0 to 1 are used to create a core complex plot alike to the one in FigureP, and the matrix cells for proteins that are in the same joined core complex become 1, shown by the large non-overlapping squares in FigureP and the matrix cells for proteins that are still in the same core complex are incremented in each step. A standard hierarchical clustering with Euclidean distance is applied to each of the large squares to display a heatmap of the inner structure and the overlap of the joined core complexes. The image shows the MEMOs as well as the core complex isoforms and can be used for visualisation of the complex assignments for each protein. It also shows whether the automatic value for U is high enough to distinguish between the complexes. A too low U leads to one very large square in the plot with large white areas, see the plots for "Tip49a/b" in the Additional fileU, the plot can be used to set P to decide whether the results should contain larger, less overlapping complexes or smaller dense complexes that share proteins. The output of 4N for one specific set of parameters U,C,S,P is a list of predicted complexes. Each list element contains the names of the proteins that are in the complex.Different values for The reference complexes for the three real datasets  are those mentioned in the corresponding paragraphs in the Data description section. The reference clusters for the simulated data  are those that were found by MCODE in the equivalent networks.The threshold for joining the core complexes was selected for each dataset by examining the corresponding core complex plots and set to 0.5 for the artificial datasets, 0.6 for "Tip49" and to 0.85 for the tests on the "malovIP" datasets. The complex prediction quality was tested by comparing the 4N results with the reference clusters using the method by Brohée and van Helden.G be the set of given clusters, P the set of predicted ones. Each given cluster g ∈ G is seen as a set of proteins, likewise each predicted cluster p ∈ P. The scoring method uses the Jaccard coefficients for each combination of p and g as |p ∩ g|/|p ∪ g| and sets them in relation to the sizes and number of the real and predicted complexes. A complete description of the method can be found in[Both the given and the predicted clusters can be seen as sets of sets. Let found in. The metA perfect complex prediction yields 1 for the accuracy score which means, that all reference complexes are completely covered by predicted complexes and the predicted complexes only contain proteins of their corresponding reference complex. The separation score is 1 when each reference complex is covered by exactly one predicted complex.P) was set to 0.85 after examining the core complex plot. The predicted complexes were compared to the 10 reference complexes. On both datasets, the same scores were reached as all relevant proteins are in the "malovIP_subset" and the additional number of proteins in the "malovIP" dataset did not influence the prediction. FiguresThe two real datasets "malovIP" and "malovIP_subset" were analyzed and the joining-parameter , it is very fast and can process very large IP datasets. The 4N analysis on the "malovIP"-dataset with its approx. 3200 IPs and 11500 proteins took approximately one day , the analysis of "Tip49a/b" can be performed in seconds.C) remains instead of four parameters as in the original 3N algorithm. The immediate feedback given by the core complex plot allows assessment of the result and a manual adaptation of the parameters if necessary. 4N can process datasets of different density from dense to sparse. It is easy to install and execute as only a basic R-installation and one extra library is needed as minimum requirement. The method can efficiently cluster IP data and suggest protein complex compositions.4N predicts the reference complexes in all tested datasets with high accuracy. The algorithm allows an immediate insight into the general density of an IP-dataset, the MEMOs, the core complex isoforms and the complex-complex interactions with the core complex plot. When this plot is seen as result, only one parameter that influences the result (the cosine-distance, The authors declare that they have no competing interests.JK implemented the software and performed the calculations. In the joint writing of the paper IvM focused on the mathematical background, AM and ABS concentrated on the biology and HH and AKS coordinated the whole project. All authors read and approved the final manuscript.Supplementary material.Click here for file


Protein-protein interactions take place at defined binding interfaces. One protein may bind two or more proteins at different interfaces at the same time. So far it has been commonly accepted that non-overlapping interfaces allow a given protein to bind other proteins simultaneously while no collisions occur between the binding protein structures. To test this assumption, we performed a comprehensive analysis of structural protein interactions to detect potential collisions. Our results did not indicate cases of biologically relevant collisions in the Protein Data Bank of protein structures. However, we discovered a number of collisions that originate from alternative protein conformations or quaternary structures due to different experimental conditions. Most molecular processes involve interactions between proteins. The physical contact between protein interaction partners is formed at defined binding interfaces, and one protein may bind various interaction partners at the same interface or at different interfaces. Due to the increasing number of protein structures available in the Protein Data Bank (PDB) et al. investigated the role of disorder in structural networks and discovered that disordered interface regions are more common in single-interface proteins It has been a commonly accepted assumption that a protein containing multiple, non-overlapping interfaces can always interact simultaneously with other proteins. As part of a large-scale structural analysis of a protein interaction network in yeast, Kim and colleagues presumed that the number of simultaneous interactions a protein can participate in is determined by the number of its non-overlapping binding interfaces et al.et al. et al. presented a concept integrating the time dimension into protein interaction networks using protein structures and interface information, which was utilized for the characterization of interactions in the p53 pathway Further protein network analyses concentrated on various aspects of single- and multi-interface proteins, ranging from protein interaction partners to interface specificity and interaction motifs. For instance, Keskin and Nussinov studied multi-specific interfaces known to bind proteins with different structures R and S of a protein P might collide in three-dimensional (3D) space, which would prevent the simultaneous interaction of R and S with P even though the binding sites are non-overlapping  P-R and P-S, where the UniProtKB accession numbers of the primary protein P were identical for both interactions while the UniProtKB accession numbers of the secondary proteins R and S were different.In detail, we first retrieved all protein structure files from the PDB P in the interaction pair P-R and P-S were defined to be distinct if all interface residues in P-R were different from those in P-S  to minimize the RMSD between corresponding Cα atoms. The rotation was determined by Kearsley's quaternion method et al.To quantify the extent of the collision between the two secondary proteins, we computed the volume of the overlap of the secondary proteins after superimposing the primary proteins. CP of the interaction pair P-R and P-S. We found 4,832 proteins that were contained in at least two PDB files . This resulted in 1,145,086 possible combinations of potential primary proteins P. However, while the number of pairwise combinations of P is large, the number of involved proteins is much smaller. Many PDB files contain the same proteins, and one PDB file may contain multiple copies of the same protein. Thus the number of possible combinations of primary proteins grows quadratic. Second, to obtain the interaction pairs, we filtered for those primary proteins that interact with at least two different secondary proteins. When examining the primary proteins and their respective secondary proteins, we identified a total of 2,309,561 interaction pairs with different secondary proteins according to their UniProtKB accession numbers. Again, as above, the number of interaction pairs is much larger than the number of involved proteins because we need to combine all protein instances in an all-versus-all approach. Third, we compared the interface residues forming the interactions P-R and P-S in order to remove those interaction pairs with overlapping interfaces. Regarding the overlap of binding interfaces that were excluded due to our strict definition that requires no overlapping residues, most overlapping interfaces share at least 20% of the interface residues  consistently reported an overlap of the secondary proteins of at least 2000 Å3 (MSMS) and 7049 Å3 (ALPHAVOL). The results were derived from 244 PDB structures, and 37 different primary proteins as well as 86 different secondary proteins participated in the interactions Click here for additional data file.Table S2GO annotations of proteins.(PDF)Click here for additional data file.Table S3Protein interactions with single-domain interface.(PDF)Click here for additional data file.Table S4GO annotations of Pfam domains.(PDF)Click here for additional data file.Table S5List of colliding protein interaction pairs after filtering with 3D Complex.(PDF)Click here for additional data file.


Small molecules that bind at protein-protein interfaces may either block or stabilize protein-protein interactions in cells. Thus, some of these binding interfaces may turn into prospective targets for drug design. Here, we collected 175 pairs of protein-protein (PP) complexes and protein-ligand (PL) complexes with known three-dimensional structures for which (1) one protein from the PP complex shares at least 40% sequence identity with the protein from the PL complex, and (2) the interface regions of these proteins overlap at least partially with each other. We found that those residues of the interfaces that may bind the other protein as well as the small molecule are evolutionary more conserved on average, have a higher tendency of being located in pockets and expose a smaller fraction of their surface area to the solvent than the remaining protein-protein interface region. Based on these findings we derived a statistical classifier that predicts patches at binding interfaces that have a higher tendency to bind small molecules. We applied this new prediction method to more than 10 000 interfaces from the protein data bank. For several complexes related to apoptosis the predicted binding patches were in direct contact to co-crystallized small molecules. S. cerevisiae, for example, interaction partners have been reported for more than 5 000 of the 6 000 yeast proteins Protein-protein interactions play important roles in most cellular processes Our structural understanding of how proteins interact with other proteins and with small molecule ligands is largely based on the atomistic three-dimensional protein structures deposited in the Protein Data Bank (PDB) Interestingly, not all interface residues play the same role for the stability (binding affinity) of the complex. There often exists a subset of interface residues, the so-called hot spots, that are mainly responsible for the binding affinity j and a second protein Pi2 compete for the same binding interface on the surface of the first protein Pi1/Pi3. For these interfaces, our method predicts ‘where at the binding interface small-molecule ligands will bind most likely’.Very few studies have compared the general properties of protein-protein and protein-ligand complexes All interface data and features were retrieved from our ABCsquare database i1, Pi2)∶ where Pi1, Pi2 and Pi3 are three proteins and Lj is a small molecule ligand. Precisely, we considered small molecules that are defined as ‘HETATOMs’ in the PDB file except for ions and water molecules. Valid tuples were required to fulfil the following four conditions:The main dataset for this study (see table S1) contains a list of PP:PL pairs where one protein may bind either a second protein or a small molecule ligand at the same interface. At first, we compiled a non-redundant set of tuples  and . For checking the last condition, the sequences of the reference proteins were aligned to each other. This resulted in a mapping of the respective interface regions. Residue pairs of proteins Pi1 and Pi3 that belong both to the Pi1–Pi2 as well as to the Pi3–Lj interface were termed ‘overlapping’ residues. The remaining interface residues of Pi1 were termed ‘non-overlapping’ residues, see In the following we denote Pi1, but both Lj and Pi2 may bind simultaneously, possibly in a cooperative manner. As this work focuses on identifying competitive binders, the reference proteins Pi3 were geometrically mapped onto the reference proteins Pi1 using structural superposition using the program VMD j. If any of the distances between the heavy atoms of Lj and Pi2 was shorter than the sum of the two atomic radii this indicated a collision. In that case, Lj and Pi2 are not likely to bind simultaneously to the same binding region on Pi1. All other pairs of complexes were deleted from the list. This led to about 1 000 pairs of PP and PL interfaces in total.However, this dataset may also contain PP:PL pairs where the ligand does not actually compete with the second protein for the interface on protein Pi1 of the remaining PP interfaces using the CD-hit program i2 as such peptides may only cover parts of the full PP interface. Therefore, we excluded a cluster entirely if it contained only small peptides shorter than 5 residues as Pi2 structures. Another requirement was that the two interfaces of a PP:PL pair should be similar to each other. To this end, we calculated the sequence identity for the sequence stretches of corresponding interface residues consisting of a combination of overlapping and non-overlapping residues for the PP complex and the PL complex, respectively. Within each cluster we selected the representative with the highest identity of interface residues. The final dataset comprises 175 PP:PL tuples. These are listed in the supplementary material.In order to remove sequence redundancy among the PP:PL pairs we clustered the reference proteins PFor all PP complexes of the final dataset, we computed structural and sequence features of the interfaces that reflect the role of individual residues in the complex . (1) The evolutionary conservation score for a single residue was obtained from Consurf-DB that provides pre-calculated normalized conservation scores for all PDB structures ith atom in a molecular structure as:Besides, we computed several structural features of the binding interfaces that characterize their packing density and curvature. (3) A measure representing the level of burial or exposal of residues was quantified by the protrusion value. For this, we used the implementation from ref. 31 that calculates a protrusion value for the atomsV denotes the volumes of the atoms within 10 Å radius around atom i and emptyV represents the value of the remaining empty space in this sphere. An atomic protrusion value of 0 refers to fully buried atoms. The larger the value, the more exposed the atom is to the solvent. The protrusion value for an entire amino acid was computed as the average of the values over all atoms of this residue. Here, i was computed following Illingworth et al. (4) The contact density of residue ij)contacts Also, for all interface residues i in a PP complex, whereby the total surface is the surface area of that residue located in the center of a tripeptide and surrounded by two alanines.Here, the solvent accessible surface area (SASA) was calculated by Naccess for an individual residue n surface residues, which consist of one central residue and n-1 neighboring residues. Thus, a patch describes the microenvironment for a central residue with respect to geometric parameters or physico-chemical properties. We applied a reimplementation of the algorithm in ref. 34 and calculated patches for every surface residue in our dataset with sizes between 5 and 8. Next, we considered the direct neighbors of the residue of interest forming a small surface patch on the interface region. This approach was inspired by the work of Thornton et al. The binary statistical classification of overlapping and non-overlapping residues was based on the random forests method Because of the large imbalance between the number of overlapping and non-overlapping residues in our dataset see , we obtaThis study aims at characterizing the nature of protein residues at overlapping protein-protein and protein-ligand binding interfaces. More precisely, given the three-dimensional structure of such a protein-protein interface, we aimed at developing a method for predicting where small molecule ligands would most likely bind at this interface. In a drug design project targeting a known protein interface, such a method would allow focusing the virtual or experimental screening efforts on ligands with physico-chemical properties that are complementary to the predicted binding patch at the protein interface.i1, Pi2) ∶ , where Pi1, Pi2 and Pi3 are three proteins and Lj is a small molecule ligand, Pi1 and Pi3 share at least 40% sequence identity, and the aligned positions in the binding interfaces of Pi1 – Pi2 and Pi3 – Lj have at least two residues in common. As explained in the Materials and Methods section, we derived a dataset of 175 tuples (PThe last example in The geometric relation between a PP:PL pair is illustrated in more detail in i1 residues (PP) and 8.7±6.1 Pi3 residues (PL). On average, only 25% of the residues in a PP interface participate in the overlapping region whereas 79% of the residues of the corresponding PL interface belong to the overlap region. In fact, 64 PL interfaces out of the 175 PP:PL pairs (37%) were fully covered by the overlapping area compared to none of the PP interfaces.The paired PP and PL interfaces in our dataset involve on average 35.5±24.0 PConcerning the size of the ligand, we found somehow unexpectedly that the number of ligand atoms was not related to the size of the ligand binding interface . On the other hand, For deriving the statistical classifier, we needed to identify features of the residues at binding interfaces that display different distributions for residues in the overlapping part of PP:PL interfaces and for non-overlapping residues. For this, we tested several structural and evolutionary features of the interfaces that were discussed previously in the literature.Due to their importance for the stability of the complex, residues at binding interfaces generally represent essential functional areas and thus may be potentially preserved during evolution. However, it is yet an unsettled issue whether binding interfaces are generally more conserved than the rest of the protein surface i2 that overlap with a high-affinity ligand in a corresponding protein:ligand structure. Obviously, the conservation of Pi2 residues may differ from that of Pi1 residues that are considered here. As a reference point, we also computed the conservation of the overall protein surfaces in our data set  whereas they are equally abundant as non-hot spot residues in the overlap regions . This observation can be interpreted that due to their relatively small size, ligands are engaged in a relatively larger number of contacts with energetically important residues than corresponding protein binding partners.i1–Pi2 complex structure, also shows clearly distinct distributions for overlapping and non-overlapping residues, see The protrusion index, computed on the basis of the PThe contact density of a residue, that is the average contact density of its solvent accessible heavy atoms, was computed following the work of Illingworth et al.Based on these five features  we constructed a statistical classifier using random forests n = 5 to 8 residues around all central residues that were predicted as overlap residue (‘O’). Consequently, the patches may contain O/(O+N) fractions of 1/n to n/n ‘O’ residues. Subsequently, we tested whether the values of neighboring individual residues can be used in a ‘patch’ analysis to boost the accuracy of the prediction for the central residue of this patch. For this, we measured the coherence of overlapping regions at PP interfaces. To this end, for every PP interface, the Euclidian distances between the heavy atoms of all residues were calculated and assigned to clusters. A cluster consists of a set of residues in which every residue has at least one neighboring residue in the same set within a distance of 5 Å. We found that 82% of the overlapping regions contained only one cluster, 13% contained two clusters, and the remaining 5% contained three clusters. This observation indicates that, as expected, overlap residues are not spread out over the interface but are located close to each other. Thus, we applied surface patches as described in the methods section as a further means for characterizing this class of residues. We tested patches of We then applied the prediction algorithm to the entire dataset of 48 440 interfaces that is currently stored in our ABCsquare database. To this end, we defined one chain for every interface as reference protein. We then generated surface patches of size 7 for the interface residues of the reference protein and focused on patches where the center residue is predicted as overlap and the total number of overlap residues is at least 5. Altogether we found 54 809 such patches belonging to 10 156 interfaces. (The entire list is provided in supporting table S2). Note that these patches all have different central residues but may be overlapping otherwise. The distribution of surface patches is shown in For characterizing these interfaces with respect to their biological role, we clustered the corresponding sequences from all reference proteins to exclude redundancy above 30% sequence identity and to get an overview to which sorts of proteins the ones with overlap patches at the interface belong. The non-redundant set of sequences was analyzed regarding the Gene Ontology (GO)-terms for the involved proteins using the ABCsquare database. In Complete lists are available in the supporting information -S7. NatuFor a practical case study, we selected all terms from the biological process category containing the expression ‘apoptosis’, see We emphasize that such predictions do not require the availability of a crystal structure of a given protein-protein complexes. Experimental knowledge about the binding interface, e.g. from chemical shift mapping by NMR or from accessibility measurements is a sufficient basis as input for a prediction by our method.As a caveat to this analysis we note that this analysis is of course limited by the amount of structural data on protein-protein and protein-ligand complexes currently available. This particularly affects the definition of non-overlap residues. It is clearly possible that these residues could be involved in binding alternative, possibly larger ligands. However, the clearly distinguishable properties of overlap and non-overlap residues derived in this study suggest that there may be only relatively few such cases.We have presented a new method that analyzes structural and physiochemical features of protein-protein binding interfaces. When given the three-dimensional structure of a protein-protein complex or the structure of a single protein with annotated PP interface, the method is able to identify to which parts of the PP interface small molecules will likely bind. In this regard our method differs from a related method recently presented by Davis Figure S1Protein chain surface colored according to protrusion values. Blue colors were used for atoms that are more exposed to the outside; red refers to atoms that are buried in the structure.(TIF)Click here for additional data file.Figure S2Maximum overlap patch for patch size 5.(TIF)Click here for additional data file.Figure S3Maximum overlap patch for patch size 6.(TIF)Click here for additional data file.Figure S4Maximum overlap patch for patch size 7.(TIF)Click here for additional data file.Figure S5Maximum overlap patch for patch size 8.(TIF)Click here for additional data file.Table S1Dataset of PP:PL pairs.(DOC)Click here for additional data file.Table S2Dataset of predicted overlap residues (predictedOverlapResidues.txt). The first column contains the PDB identifier and the chain name, the second column contains the position number of the residue that represents the central residue of the patch and is predicted as ‘overlap’.(TXT)Click here for additional data file.Table S3Term frequencies for GO function.(DOC)Click here for additional data file.Table S4Term frequencies for GO cellular component.(DOC)Click here for additional data file.Table S5GO function terms+frequency that are found in the dataset of predicted overlap residues (predictionGOfunction.txt).(TXT)Click here for additional data file.Table S6GO biological process terms+frequency that are found in the dataset of predicted overlap residues (predictionGOprocess.txt).(TXT)Click here for additional data file.Table S7GO cellular component terms+frequency that are found in the dataset of predicted overlap residues (predictionGOcellComp.txt).(TXT)Click here for additional data file.Table S8Biological process GO terms referring to apoptosis.(DOC)Click here for additional data file.