InSite is a computational method that integrates high-throughput protein and sequence data to infer the specific binding regions of interacting protein pairs. We propose InSite, a computational method that integrates high-throughput protein and sequence data to infer the specific binding regions of interacting protein pairs. We compared our predictions with binding sites in Protein Data Bank and found significantly more binding events occur at sites we predicted. Several regions containing disease-causing mutations or cancer polymorphisms in human are predicted to be binding for protein pairs related to the disease, which suggests novel mechanistic hypotheses for several diseases. Much recent work focuses on generating proteome-wide protein-protein interaction maps for both model organisms and human, using high-throughput biological assays, such as affinity purification -4 and yeM on protein A binds to protein B'. A key difference between InSite and previous methods [We propose an automated method, called InSite (for Interaction Site), for predicting the specific regions where protein-protein interactions take place. InSite assumes no knowledge of the three-dimensional protein structure, nor of the sites at which binding occurs. It takes as input a library of conserved sequence motifs ,14, a he methods -20 is thInSite is based on several key assumptions. The first is that protein-protein interactions are induced by interactions between pairs of high-affinity sites on the protein sequences. Second, we assume that most binding sites are covered and characterized by motifs or domains - conserved patterns on protein sequences that recur in many proteins.  Although an approximation, this assumption is supported in the literature, as interaction sites tend to be more conserved than the rest of the protein surface . These ma,d, explaining the interactions P1-P3 and P1-P4, and high affinity for the pair b,e, explaining the interactions P1-P5 and P2-P5. By contrast, the motif pair c,d is not as good an explanation, because the motif pair also appears in the non-interacting protein pair P3, P5. We note that the motif pair a,c is also a candidate hypothesis, as it predicts the interactions P1-P3 and P1-P5 and does not incorrectly predict any other interaction. However, it leaves the interaction P1-P4 unexplained, therefore leading to a less parsimonious model that also contains the motif pair a,d.InSite is structured in two phases. In the first phase, the algorithm searches for a set of affinity parameters between pairs of motif types that provides a good explanation of the interaction data, roughly speaking: every pair of interacting proteins contains a high-affinity motif pair; non-interacting proteins do not contain such motif pairs; and motif pairs with supporting evidence, such as from domain fusion, should be more likely to have high affinity. There may be multiple assignments to the affinity parameters that explain the data well; our method tends to select sparser explanations, where fewer motif pairs have high affinity, thereby incorporating a natural bias towards simplicity. A simple example of this phase is illustrated in Figure M on protein A binds to protein B'. In a naïve approach, we can simply take the most likely set of binding sites for the estimated set of affinity parameters. However, in some cases, there may be multiple models that are equally consistent with our observed interaction pattern, but that give rise to different binding predictions. In the second phase of InSite, we therefore assess the confidence in each binding prediction by 'disallowing' the A-B binding at the predicted motif M, re-estimating the affinities, and computing the overall score of the resulting model (its ability to explain the observed interactions). The reduction in score relative to our original model is an estimate of our confidence in the prediction. This phase serves two purposes: it increases the robustness of our predictions to noise, and also reduces the confidence in cases where there is an alternative explanation of the interaction using a different motif. For example, in Figure d on P4 binds to P1' has higher confidence, because d is the only motif that can explain the interaction. Conversely, the prediction that 'motif d on P3 binds to P1' has lower confidence, because the motif pair a,c can provide an alternative explanation to the interaction. The prediction that 'motif e on P5 binds to P2' also has high confidence; although interaction via binding at b,c would explain the interaction, making b,c a high-affinity motif pair would contradict the fact that P2 and P3 do not interact.A set of estimated affinities provides us with a way of predicting, for each pair of proteins, which motif pair is most likely to have produced the binding. In the second phase, we use this ability to produce specific hypotheses of the form 'Motif et al. [et al. [We provide a formal foundation for this type of intuitive argument within an automated procedure Figure , based oet al.  and Rile [et al. , formali [et al. ,25, and  [et al. ,26,27, wOur entire model is trained using the expectation maximization (EM) algorithm in a unified way  to maximize the overall probability of the observed protein-protein interactions. This type of training differs significantly from most previous methods that aggregate multiple assays to produce a unified estimate of protein-protein interactions. These methods ,28 generM on protein A binds to protein B'. In this phase, InSite modifies the model so as to enforce that binding between A and B does not occur at motif M. We then compute the loss in the likelihood of the data, and use it as our estimate of the confidence in the binding hypothesis.These estimated affinities allow us to predict, for each pair of proteins, which motif pair is most likely to have produced the binding. In the second phase, we use these predictions, augmented with a procedure aimed at estimating the confidence in each such prediction, to produce specific hypotheses of the form 'Motif et al. [et al. [et al. [A interacts with protein B via the motif M on A, a mutation at motif M may have a significant effect on the interaction. If the interaction is critical in some pathway, this mutation may result in a deleterious phenotype, which may lead to disease [M that contain a mutation in the Online Mendelian Inheritance in Man (OMIM) human disease database [M leads to the disease by disrupting the binding of the protein pair. A literature search validated many of these disease-related predictions, whereas others are unknown but provide plausible hypotheses. Therefore, our predictions provide us with significant insights into the underlying mechanism of the disease processes, which may help future study and drug design.As an initial validation of the InSite method, we first show that it provides high-quality predictions of direct physical binding for held-out protein interactions that were not used in training. These integrated predictions, which utilize both binding sites and multiple types of protein-protein interaction data, provide high precision and higher coverage than previous methods. As the primary validation of our approach, we compare the specific binding site predictions made by InSite to the co-crystallized protein pairs in the Protein Data Bank (PDB) , whose set al.  and 80.0 [et al.  and of G [et al.  are veri disease . We appldatabase  or identdatabase . We thenWe have made our predictions and our code publicly available for download . Our algSaccharomyces cerevisiae and human. For S. cerevisiae, we compiled 4,200 reliable protein-protein interactions as our gold standard and 108,924 observations of pairwise protein-protein interactions from high-throughput yeast two-hybrid assays of Ito et al. [et al. [et al. [et al. [et al. [et al. [We applied InSite to data from both  [et al. . Altoget [et al. , and eli [et al.  and from [et al. . In the  [et al.  and the  [et al.  that ideet al., is to train on all Pfam domains (providing a larger training set) and to evaluate the predictions only on the more reliable Pfam-A domains. For each motif set, we used evidence from domain fusion and whether two motifs share a common GO category as noisy indicators for motif-motif interactions [The InSite method can be applied to any set of sequence motifs. Different sets offer different trade-offs in terms of coverage of binding sites; we can estimate this coverage by comparing residues covered by a particular set of motifs to residues found to be binding sites in some interaction in PDB. One option is Prosite motifs , where wractions ,40.We experimented with different data sets and different motif sets. In each case, we trained our algorithm on these data; then, for each interacting protein pair, we compute the binding confidences for all their motifs, and generate a set of binding site predictions, which we rank in order of the computed confidence.et al. and Krogan et al., which are derived from tandem affinity purification (TAP) followed by mass spectrometry (MS) and quantify the propensity of proteins to be in the same complex. Using standard ten-fold cross-validation, we divided our gold interactions and high-throughput interactions into ten sets; for each of ten trials, we hid one set and trained on the remaining nine sets together with our gold non-interactions. We then computed the probability of physical interaction for each protein pair in the hidden set, and ranked them according to their predicted interaction probabilities. We defined a predicted interaction to be true only if it appears in our gold interactions, and false if it appears only in the high-throughput interactions; we then counted the number of true and false predictions in the top pairs, for different thresholds. Although this evaluation may miss some true physical interactions that appear in the high-throughput data set but not in our gold set, it provides an unbiased estimate of our ability to identify direct physical interactions. We separately performed this procedure by ranking the interactions according to the scores computed by Gavin et al. and by Krogan et al. We also compared our model with a method that combines all evidence on protein-protein interactions in a naïve Bayes model where motifs are not used.The actual protein-protein interactions are mostly unobserved in our probabilistic model. However, we can compute the probability of interaction between two proteins based on our learned model, which integrates evidence on protein-protein interactions and motif-motif interactions as well as the motif composition of the proteins. As a preliminary validation, we first evaluated if InSite is able to identify direct physical interactions. We compare our results to those obtained by using the confidence scores computed by Gavin et al.'s and Krogan et al.'s scores, our model covers more positive interactions because it integrates multiple assays. However, even if we restrict it only to pairs appearing in a single assay, such as Gavin et al.'s or Krogan et al.'s, InSite  complex, which is responsible for all mRNA synthesis in eukaryotes. Its three-dimensional structure is solved at 2.8 Å resolution , so thatet al., and trained on Pfam domains and evaluated PDB binding sites on the more reliable Pfam-A domains; we also tried to both train and evaluate on Pfam-A domains but the result was worse in comparison to training on all Pfam domains (data not shown).We performed this type of binding site evaluation for all of the co-crystallized protein pairs in PDB that also appeared in our set of gold interactions. While the PDB data are scarce, they provide the ultimate evaluation of our predictions. We applied our method separately in two regimens. In the first, we trained on Prosite motifs and evaluated on those motifs that cover less than half of the protein length ; we pruned the motif set in this way because short motifs provide us with more information about the binding site location. In the second regimen, we followed the protocol of Riley Overall, the PDB co-crystallized structures contain 96 potential binding sites covered by Prosite motifs, of which 50 (52.1%) are verified as actually binding, and the remaining 46 are verified to be non-binding. Similarly, PDB contained 317 possible bindings between a Pfam-A domain and a protein, of which 167 (52.7%) are verified in PDB. We ranked all possible bindings according to their predicted binding confidences. With Prosite motifs Figure , the areet al. [et al. [et al. [et al. use linear programming to find the confidence scores to a most parsimonious set of motif pairs that explains the protein-protein interactions. Lee et al. use the expected number of motif-motif interactions for a pair of Pfam-A domain types across four species, and integrate them with GO annotation and domain fusion to generate a final ranking on pairs of motif types. Note that all these methods generate confidence scores on pairs of motif types, regardless of what protein pairs they occur on. To use these predictions for the task of estimating specific binding regions, we define the confidence that motif M on protein A binds to protein B as the maximum confidence score between motif type M and all the motif types that appear on protein B. For Guimaraes et al. and Lee et al., only the confidence scores between Pfam-A domains are available so we only compared their results with our Pfam-A predictions. We re-implemented DPEA and compared the results with both our Prosite and Pfam-A predictions. As we can see, in both Prosite and Pfam evaluations  comes second . InSite is able to reduce the error rate (1 - AUC) by 16.2% compared with Lee et al. For Pfam, the AUC values are 0.619 and 0.620 for Riley et al. and Guimaraes et al., respectively. For Prosite, the AUC value for Riley et al. is 0.601. Compared to these two methods, InSite achieves a significant error reduction of 43.7% and 19.8% for Pfam and Prosite, respectively.We compared our results to those obtained by the DPEA method of Riley et al.  the pars [et al. , and an  [et al. . DPEA cos Figure , the AUCet al., 80.0% (Pfam-A) for Guimaraes et al., and 80.0% (Pfam-A) and 58.9% (Prosite) for Riley et al. Note that, in the case of Pfam-A, Riley et al. predicted all top 24 pairs correctly because they are derived from the binding of PF00227 (Proteasome) with itself. This motif pair has the highest score and it appears in 24 binding events, all of which are correctly verified by PDB. The lack of granularity  in Riley et al. helped in those top predictions, but hurt it in the remaining predictions, thus resulting in overall lower performance.If we consider the top 50 predictions made by Insite, 33 (66.0%) are correct for Prosite and 45 (90.0%) are correct for Pfam-A. In comparison, only 52.1% and 52.7% are expected to be correct using random predictions for Prosite and Pfam-A, respectively. The enrichment of known binding sites in our top predictions indicates that InSite is able to distinguish actual binding sites from non-binding sites. In comparison, the proportion of top 50 predictions verified are 82.0% (Pfam-A) for Lee More generally, a pair of motif types may have multiple occurrences over different protein pairs . The previous methods -20 assigA occurs on a motif M that is predicted to be the binding site to a protein B, and B is involved in pathways related to the disease, it is likely that the mutation disrupts the binding and thus leads to the disease. We ran InSite with two different experimental setups: one using only reliable protein-protein interactions, and the other using both reliable and high-throughput protein-protein interactions. Table While a systematic validation is not possible in human, due to the very low coverage of known protein-protein interactions or binding sites, we performed an anecdotal evaluation that focuses on interactions of particular interest for human disease. Many genetic diseases in human have been mapped to a single amino-acid mutation and cataloged in the OMIM database . The exaet al. [Some of our predictions are directly validated in the literature. One of the top ten predictions involves vitamin K-dependent protein C precursor PROC, which is predicted to bind to vitamin K-dependent protein S precursor PROS1. There are four regions on PROC, a Gla domain, an EGF-like domain 1, an EGF-like domain 2, and a serine proteases domain. Prosite has ten motifs on the protein, covering these four regions. InSite predicted two of the motifs (PS01187 and PS50026), which correspond to EGF-like domain 1, to be the binding site for PROS. Ohlin et al.  showed tet al. , matchinet al. [Another of our highest-confidence binding site predictions is 'the BH3 motif on BAX binds to BCL2L1' Figure . BCL2 haet al.  showed tet al. ; thus, tet al. . If we eSome of our predictions Table  are not We also listed all top predictions are that are confirmed to be wrong Table . In one et al. [et al., the one most likely to be a binding site according to the InSite predictions is the SH2 domain of FYN in the SRC family  for training, thereby greatly increasing the size of the training set and avoiding possible biases in it. InSite also easily accommodates other types of indirect evidence, such as co-expression, GO annotation, and domain fusion, on both protein-protein interactions and motif-motif interactions. This type of integration may be useful in other settings as well. We note that the evidence model, although an important component in our approach, is not the main factor in its performance. Indeed, if we remove the indirect evidence like co-expression, GO annotation, and domain fusion from our model, the AUC value decreases by only 0.033 and 0.019 for Pfam and Prosite, respectively . Therefore, our result using protein-protein interactions alone is still significantly better than the methods of Guimaraes There are several limitations to the ability of our approach to identify correct binding sites. Not all motifs mediate protein interactions through direct binding. Some motifs help shape the structure of proteins. Mutations in the motifs would alter the structure of the protein and disrupt binding at some other places. Other motifs are signatures that are markers for longer domains. It is the longer domain, and not the signature motif, that serves as the actual binding site. InSite will not be able to distinguish these cases. One approach would be to classify motifs into either structural or binding motifs by using partially supervised learning with labeled binding sites from PDB or prior biological knowledge. A motif may appear multiple times in a protein, but InSite is unable to distinguish between them, and, therefore, cannot predict which copy is the actual binding site. Most importantly, some binding sites may not be covered by any motif in our set of conserved motifs , and thus our current model has no way to predict interactions involving them. Clearly, we can apply InSite to a larger set of motifs, for example, eMotifs ,66, but There has been steady growth in the past few years in the suite of methods that successfully utilize large amounts of available data and sophisticated machine learning methods to solve problems in structural biology for which experimental methods are difficult and time-consuming. These tasks include protein structure prediction , RNA strInSite makes targeted, testable predictions for specific binding regions in an interacting protein pair. As we have shown, these predictions can be used to generate hypotheses regarding the mechanism by which certain mutations in a protein can disrupt interactions, and give rise to phenotypic changes, including human disease such as cancer. We put all predictions with cancer annotations or OMIM mutations online, allowing for a more comprehensive analysis by experts and follow-on wet-lab experiments. We have also made the InSite software publicly available via the web to allow this tool to be used by researchers. Due to the universal mechanisms underlying biochemical interactions, the tool can be applied to any organism, and even to protein-protein interaction data generated from multiple organisms.S. cerevisiae protein-protein interactions from MIPS [We constructed 'observed interaction' variables for each of the assays, as follows. For the yeast two-hybrid datasets of ,10, thesrom MIPS  and DIP rom MIPS , downloarom MIPS  and remoWe computed expression correlation using a compendium of time series data obtained in different environmental conditions -79. The In one run, we used sequence motifs from the Prosite database  excludinWe construct a 'domain fusion' variable for each pair of Prosite motifs or Pfam domains. Its value is 1 if the two motifs ever co-occur on the same protein in any species. Its value is 0 otherwise. Note that we use the term 'domain fusion' here, although it can also refer to motifs. We also looked at whether the two motifs appear together in any biological process category based on the mapping table from Pfam to GO . If theyet al. [We used a high confidence yeast two-hybrid assay  and HPRDet al.  and an aet al.  with itset al.  and remoOur probabilistic model has three components. The first  formalizes the binding model described above: for each protein pair in our model, and each pair of motifs on the two proteins, we have a variable indicating whether binding took place at this motif pair. The prior probability that a specific motif pair binds is the affinity of the corresponding motif types. The overall interaction of the proteins is a disjunction of these binding events, and of an additional 'spurious binding' variable that accounts both for noise in some interaction data sets and for binding outside of motifs in our database. The second component of our model  addresses the problem that very few protein interactions are known with certainty. Yeast two-hybrid assays can be noisy ,25, withPi, Pj is described by an entity Tij. A pair of motifs in two proteins can potentially 'bind' and induce an interaction between the corresponding proteins. We encode this assumption by introducing a variable Tij.Bab for each pair of motifs a in Pi and b in Pj, which represents whether the pair of motif occurrences actually binds. The probability that they bind depends on the 'affinity' between the motifs. Therefore, we define:More formally, each interacting or non-interacting pair of proteins andθab is the affinity between motifs a and b. Note that this affinity is a feature of the motif pair and does not depend on the proteins in which they appear. We place a Dirichlet prior distribution over the value of θab, which is the same for θ across all motif pairs. We must also account for interactions that are not explained by our set of motifs, such as the binding between amino acids not included in our motif set. Thus, we add a 'spurious binding' variable Tij.S. The probability that spurious binding occurs is given by:where m is proportional to the average  number of amino acids not covered by any motif in the two proteins. It represents the fact that the more amino acids we have outside the motif set, the more likely the interaction is induced by something other than binding between motifs. Two proteins interact if and only if some form of binding occurs, whether by a motif pair or by spurious binding. Thus, we define a variable Tij.I, which represents whether protein Pi interacts with protein Pj, to be a deterministic OR of all the binding variables Tij.S and Tij.Bab. We note that Riley et al. did not include a spurious interaction variable in their model, but rather used 0.001, regardless of the protein length, as the probability of interaction when there is no motif pair between two proteins.where Tij.O to be the interactions observed in the experimental assays and indirect evidence like co-expression and GO distance, which are noisy sensors for the actual interaction variable Tij.I. Note that an actual interaction variable may have several observation variables if the pair appears in multiple assays. For those assays with binary observations, Tij.On is a binary variable and the probability it is 'true' depends on Tij.I and the type of assay. Therefore, we can account for the different false positive and false negative rates in different assays. For Gavin et al., we assume the confidence score Tij.Og to be Gaussian distributions, whose mean and variance depends on the Tij.I. For Krogan et al., we assume the confidence score Tij.Ok has a uniform distribution if Tij.I is false (non-interacting) and has an exponential distribution if Tij.I is true (interacting). For co-expression, we assume the Pearson correlation coefficient Tij.Oe to be Gaussian distributions, whose mean and variance depends on the Tij.I. For GO distance, we assume its value Tij.Oo to be an exponential distribution when Tij.I is false and a mixture of Gaussian and uniform distribution when Tij.I is true (interacting). In the case of human confidence score Tij.Ow from Ewing et al. [Tij.I. Note that each parametric form was selected by examining the empirical distribution and assessing what model would fit it well.To account for the fact that our experimental assays are not direct and reliable measurements of physical protein-protein interactions, we define the observation variables g et al. , we use Rab to describe a pair of motif a and motif b. We introduce a variable Rab.Eg to represent whether they share the same GO biological process category and another variable Rab.Ef for whether they appear together in a domain fusion event. Both variables are probabilistically dependent on the binding variable Tij.Bab and serve as its noisy sensors. Note that Rab is the same regardless of which protein pair Tij it appears in. We use different models for domain fusion and GO distance to account for their different correlation with the actual motif-motif interactions. Note that parameters of the evidence models for protein-protein interactions and motif-motif interactions are all learned from the data. Some of the learned values are illustrated in Figure S2 in Additional data file 2.We use An instantiation of our probabilistic model is illustrated in Figure S2 in Additional data file 2 and the conditional probabilities involved are summarized below:α, β are the hyper-parameters in the Dirichlet distribution, 8.68 is the maximum value of the GO distance, n enumerates the different type of yeast two-hybrid assays, Og is Gavin et al.'s assay, Ok is Krogan et al.'s assay, Oe is co-expression, Oo is GO distance, Ow is Ewing's confidence score, Eg is shared GO motif function, Ef is domain fusion, U is uniform distribution, and I is indicator function. The observation parameter vector η is the union of ηn, μ, σ, w, λ, ρ, σ.where θ, probability of spurious binding θs, and the parameters for the observation models η, which maximize the probability over observed evidence on protein-protein interactions T.O, the partial assignment to the actual interactions T.I, and the observed evidence on motif-motif interactions R.E. Our algorithm uses an iterative procedure based on the EM algorithm to find the local maximum. In the E-step, we compute the conditional probabilities for the binding variables T.B., T.S., and the actual interaction variables T.I, given θ, θs, η, T.O, R.E, and use those as the soft assignments to the variables. Define:The model defines a joint probability over the entire set of attributes, which is the product of all local conditional probability models shown above. Our learning objective is to find affinities between motifs to be the binding probability given the evidence on motif-motif interactions, where:By Bayes' rule, we have:whereθ, θs, η. This step can be executed efficiently in closed form, using standard methods, for the parameters θ, η. To estimate θs, we need to decompose it into m variables and apply EM to this approximate form . We repeat the E-step and M-step until the change of likelihood is less than a threshold. Since, in the next phase, we force each motif-protein pair to be non-binding and compare the change of likelihood Liaj, we have to makes sure the threshold used here for convergence is at least a magnitude smaller than Liaj, so the noise would not overwhelm the signal. Here we set the threshold to be 0.01 in terms of change of log-likelihood. Note that Riley et al. used the expected likelihood to test convergence, which does not optimize the joint likelihood and may not always increase over the EM steps.In the M-step, we compute relevant expected sufficient statistics using the computed soft marginal probabilities as soft assignments. We use maximum likelihood estimation to re-estimate the parameters α, β of the Dirichlet distribution, we used two-fold cross-validation on the PDB data set. In this regimen, we select the hyper-parameters so as to optimize performance on one PDB fold, and evaluate performance on the other fold; thus, no data in the test set were used to estimate any of the parameters or hyper-parameters in the model.To estimate the two hyper-parameters, a on protein i is the binding site to protein j, we force a not to bind with any motif on protein j . We rerun our algorithm with the above constraint and use the change in likelihood as the confidence score of our prediction, which we denote to be Liaj. A high score indicates that forcing a not to be the binding site induces a big change in likelihood and is unfavorable. A low score suggests the binding site is dispensable from the model with competing hypotheses that can explain the observed interactions, and thus the prediction is questionable. Unlike the motif affinities θab learned from the previous step, here our confidence score Liaj depends on both proteins i and j and is different for different proteins.Since we explicitly model the binding events between a pair of motifs and between amino acid pairs outside the motif set, it gives us a way to compute the confidence that a motif on a protein binds to another protein. Here the intuition is that if a motif is non-binding, it is dispensable from the model. We first run our model until convergence. To predict whether motif η for the evidence models are initialized based on empirical counts.If a motif pair does not appear between any pair of interacting proteins, we set its affinity to be 0, an assignment guaranteed to maximize the joint likelihood; this helps simplify our model structure. We set the initial affinity for the remaining motif pairs based on the frequency with which they appear between interacting protein pairs . The obsS. cerevisiae proteins. We define two residues to be in contact if the closest distance between their two respective heavy atoms is less than 5 Å. This definition is similar to that of [We extracted all structures from PDB that have at least two co-crystallized chains, and whose chains are nearly identical to  that of . A motifTo relate our predictions to mutations that cause human genetic diseases, we extracted the allelic variants from OMIM , which dTo relate our predictions to mutations in cancer, we extracted more than 1,000 somatic mutations found in 274 megabases of DNA corresponding to the coding exons of 518 protein kinase genes in 210 diverse human cancers . We focuAUC, area under the ROC curve; EM, expectation maximization; GO, Gene Ontology; HPRD, Human Protein Reference Database; InSite, Interaction Site; MS, mass spectrometry; OMIM, Online Mendelian Inheritance in Man; PDB, Protein Data Bank; Pol II, RNA polymerase II; ROC, receiver operating characteristic; TAP, tandem affinity purification.All authors read and approved the final manuscript.The following additional data are available with the online version of this paper. Additional data file Figure S1 shows motif coverage of protein sequences compared with coverage of the protein-protein interaction binding sites. Figure S2 is an illustration of the Bayesian network used in the first phase of InSite. Figure S3 is a schematic illustration of our EM-based learning algorithm. Figure S4 is an illustration of the Bayesian network used in the second phase of InSite. Figure S5 shows the number of motifs and their lengths on each protein. Figure S6 shows the number of occurrences for each pair of motif types. Figure S7 evaluates motif-protein binding site predictions with or without indirect evidence relative to solved PDB structures. Figure S8 shows the statistics for each protein-protein interaction dataset.Click here for fileSupplementary material on the EM algorithm for the spurious binding variable and captions for supplementary figuresClick here for file


Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome.http://ecid.bioinfo.cnio.es/.We have studied the possibility of constructing a classifier in order to combine the output of the several protein interaction prediction methods. The AODE (Averaged One-Dependence Estimators) machine learning algorithm is a suitable choice in this case and it provides better results than the individual prediction methods, and it has better performances than other tested alternative methods in this experimental set up. To illustrate the potential use of this new AODE-based Predictor of Protein InterActions (APPIA), when analyzing high-throughput experimental data, we show how it helps to filter the results of published High-Throughput proteomic studies, ranking in a significant way functionally related pairs. Availability: All the predictions of the individual methods and of the combined APPIA predictor, together with the used datasets of functional associations are available at We propose a strategy that integrates the main current computational techniques used to predict functional associations into a unified classifier system, specifically focusing on the evaluation of poorly characterized protein pairs. We selected the AODE classifier as the appropriate tool to perform this task. AODE is particularly useful to extract valuable information from large unbalanced and heterogeneous data sets. The combination of the information provided by five prediction interaction prediction methods with some simple sequence features in APPIA is useful in establishing reliability values and helpful to prioritize functional interactions that can be further experimentally characterized. A number of computational methods have been developed to predict functional associations in complete genomes. In particular, three such methods have frequently been tested in silico two-hybrid  for improving the prediction of protein interactions in E. coli functional associations.In the study presented here, we propose the application of a machine learning methodology to improve the prediction of functional interactions. Our approach is based on the combination of the results of various prediction methods independently developed in this area. We propose the use of a recently published machine-learning algorithm known as “Averaged One-Dependence Estimators” Finally, we have used our predictor to refine collections of functional protein associations, including those obtained by a high-throughput experimental approach We explored several algorithms and various training sets with different positive/negative ratios in order to reach a good compromise between the actual underlying class unbalance . This helped us determine whether the methods tested were suitable to address the incapacity of most algorithms to handle highly unbalanced sets  are shown . While RE. coli after removing those cases used in the training set. In We considered it convenient to compare the accuracy of the positive predictions made by the original methods and the AODE-based classifier (APPIA), since each method has different applicability and is potentially able to detect different type of interactions. We compared the performance of the individual methods with the one of their combination using APPIA by examining the accuracy of the ‘n’ first predictions from an extended test set, that includes the whole set of predictions for When the comparison was done over a larger set of predicted pairs  and additional attributes such as protein length and size of protein family used in APPIA.Moreover, we found that some individual methods are very inaccurate (that is less than 10%), such as I2H, MT and PP. By contrast, GC produced a much higher proportion of correct predictions and indeed, GC was about 8 times more accurate than PP (the best of the three poorest methods). GF provided very few predictions due to its dependence on the occurrence of a particular event (gene fusion). The relatively low frequency of gene fusion events limits the ability of GF to predict most interactions.It is important to notice, that our comprehensive definition of functional associations is focused on the most informative types of associations see . In our In conclusion, APPIA fusion of the various prediction methods outperformed the individual computational methods by combining and complementing them with additional information such as ranking attributes and biological characteristics . The number of external confirmations is small (a common fact when interaction sources are compared), even though our dataset includes another set of protein complexes from a similar high throughput pull down experiment . Therefore, the coverage and the capacity to discover unknown associations of APPIA should be higher, while its capacity for detecting well-characterized protein associations will be necessarily lower.STRING In order to test these ideas, we used APPIA to extract those STRING entries with higher scores from a set of 240,885 protein pairs above the minimal STRING confidence value of 0.15. In order to obtain a view of the ability of both approaches for detecting unknown functional associations all the experimentally validated pairs were removed. We compare the STRING and APPIA scores for the set of 121,042 associations present in both datasets, representing 50.25% of the ones in STRING .The results obtained clearly show that both definitions of functional links only partially overlap and that APPIA can complement STRING predictions with a more comprehensive definition of functional association between protein pairs.E. coli Interaction Database, Results from each of the five prediction methods and from their combination in APPIA are integrated in the EcID server  and the ‘Prediction Mode’ focused on providing predictions for less well-characterized proteins. The APPIA scores presented here are used to generate a prediction confidence criteria for the functional associations displayed in the ‘Prediction Mode’. This allows the system to provide a valuable set of putative protein relationships for otherwise poorly characterized proteins. Moreover, it fulfils our original purpose of gaining further insight into less well-characterized proteins rather than simply ranking well-known protein associations. The experimental server is freely accessible at We have presented here a new classifier to predict functional protein associations based on the AODE algorithm. We considered the AODE and BayesNet classification algorithms as the best candidates to address our problem, the prediction of protein functional interactions by combining results of five heterogeneous prediction methods, protein size and number of orthologues of each one of the two proteins. We additionally include as input information the ranking position of the potential interactor in the sorted list of scores of each one of the five methods. These algorithms are particularly suitable for problems containing missing data, as is the case here where it is not always possible to obtain predictions with the five methods for the same protein pairs. AODE and BayesNet have a better capacity to infer the states of unknown variables using prior probabilities and existing evidence. This is an important feature for a classifier thought to be part of a periodically updated pipeline, as is the case of protein/gene databases. Additionally, AODE provides quantitative probability estimates that can be used as a measure of reliability associated to each predicted pair.This classifier based on the AODE algorithm (APPIA) takes advantage of other computational methods based on the detection of different evolutionary signals. All the methods and information used as the input for the classifier are derived from the analyses of information provided by genomic sequencing experiments. Therefore, APPIA is intended to provide insights into poorly characterized functional associations, rather than highlighting well-known ones. In this sense, our approach differs from other popular and successful approaches, like STRING, because it is independent of the experimental information available for a considered protein pair.E. coli since it is well characterized at the molecular level, with functions experimentally assigned to approximately 57% of its genes  and with homology-based assignments for an additional 25%. In addition, bacteria are a good workbench due to the quantity of genomes available and the relatively simple architecture of their proteins, both factors that fundamentally boost the quantity and quality of the predictions obtained by genome-based prediction methods. Moreover, at least one of the methods (genomic context) relies on the principle of genome organization that can only be strictly applied to bacterial genomes (conservation of genomic intergene proximity).We focused on We showed that a recently proposed machine-learning algorithm AODE is well suited to detect and rank functional protein associations being particularly valuable for scenarios characterized by a large proportion of missing values. Additionally AODE can be easily and efficiently re-trained, making it a suitable technique for its incorporation into periodically updated resources.In order to assess the performance of this algorithm, we show the results of the preparatory comparative analysis carried out with alternative machine learning algorithms. To benchmark the predictions obtained by different computational methods we used both “Cost Curves” and ROC analyses. The results of these analyses in a training-independent test set confirmed the suitability of AODE to address this type of problem. Indeed, the AODE-based classifier clearly outperforms all the alternative algorithms evaluated in this test, except the BayesNet classifier that obtains just slightly worse results.We show that the AODE-based classifier outperforms the original individual methods that it incorporates and that it can be effectively combined with other data sources  to improve their results. Particularly noteworthy is the combination of our classifier with the results of a previously published large-scale pull down experiment, from which we are able to score the original experimental data according to their functional significance. This result highlights the value of this kind of approach to remove the considerable number of false positives that is still one of the main drawbacks of high-throughput experimental approaches.In conclusion, our results show that improvement in the prediction of the functional association between proteins can be produced by the integration of computational methods. Furthermore, they emphasize that integrative approaches can be useful to gain insights into proteome characterization by selectively detecting and scoring reliable subsets of functional protein interactions.There are several difficulties in building a predictor of functional protein interactions due to the implicit nature of the biological data. These difficulties include the intrinsic uncertainty in the data, the extreme imbalance between the number of positive and negative instances (less than 1% of positive class) and the large percentage of values that are missing in relation to several features  is represented by 19 numerical attributes derived from various sources. We can distinguish three groups of attributes: i) the scores from each of the 5 methods to predict protein interactions (see below); ii) the protein-centred predictions rank for the protein pair, calculated as the position of the protein pair in the sorted list of predictions from each method for each of the two proteins (see below); and iii) 2 pairs of protein features that are highly related to the performance of these methods, the sequence length and the number of orthologues detected for each protein in the pair (see below). In all cases, the values missing for any of the 19 attributes were considered as ‘undetermined’ values and they were not replaced with flags because in our situation the absence of information cannot be considered as usable information. Although using flags provides similar results see  we prefeThe computational prediction methods used are based on different sources of evidence suggesting an interaction between a pair of proteins . The inputs for the classifier presented here includes the scores for each of the following 5 prediction methods:E. coli protein in each of the compiled genomes.Phylogenetic Profiles (PP), a method to examine the presence or absence of genes in related species E. coli were at a genomic distance of closer than 300 bp in the corresponding chromosome, according to the original publication.Gene Context (GC) considers the conservation of the gene neighborhoods in different species http://cgg.ebi.ac.uk/services/allfuse/.Gene Fusion (GF) searches for non-overlapping similarity matches within the same protein of pairs of proteins Mirror Tree (MT) studies the similarity of phylogenetic trees In silico Two-Hybrid (I2H) quantifies the degree of co-variation between pairs of residues in the two proteins E. coli and the corresponding genome were selected as putative orthologues .All the methods  were applied to a set of 118 complete prokaryotic genomes see . For theE. coli protein.I2H and MT require pairs of multiple sequence alignments for their application. We generated the corresponding multiple sequence alignment for each set of orthologues with more than 15 sequences using MUSCLE It is important to note that all the methods are used in their original formats. We preferred to keep these simpler formats and to enrich the input with the related attributes. This way, the training algorithm itself can be optimized for these characteristics.Several of the methods are expected to have some protein-related biases , while each input pair is considered to be independent of any other in our algorithms. To cope with this situation, we calculated the ranking position of the corresponding protein pair in the sorted prediction lists for each of the two proteins in every method. Thus, for each protein we introduced the values of the smallest and the highest ranking of the pair in the five prediction methods . These methods provide a large number of low scoring results. Therefore we also removed the entries where none of the 10 rankings were less than or equal to 100, only for PP, MT and I2H rankings. This step reduces the noise coming from uninformative pairs that would never be predicted by the input methodsFinally, we included 4 attributes that represent two very basic protein features that are intrinsically related with the performance of one or more of the prediction methods. These features are the number of orthologues detected and the sequence length for each protein in the pair.A particularly controversial issue when making predictions of protein interaction is the definition of a functional association. In our case, we have chosen an inclusive definition of functional association that is consistent with the different prediction methods included. While some of these methods are expected to focus on physical interactions , others are better suited to predict biochemical pathways (GC) or they have a less well-defined scope (PP). Indeed, our definitions of positive and negative classes are an expansion of the datasets used in our recently proposed new protein interaction method The set of functional associations for E. coli proteins was extracted from several external databases (see below) and this set tries to capture the complex nature of the domain. It contains 89,401 different protein pairs (homodimers were not considered) from the following sources:Proteins involved in the same biochemical pathway. These functional associations are extracted from the KEGG14 Regulator-regulated gene associations. We extracted the transcription regulatory data contained in EcoCyc and established functional links between each transcriptional regulator and its corresponding regulated genes. This set contains 1,686 relationships.Set of co-regulated genes. Based on the same type of information as the previous set, we established 58,275 functional associations among those proteins that are regulated by the same regulator.E. coli proteins Interactions directly extracted from the literature using text-mining techniques. For this set we use the set of protein name interactions obtained from iHOP for E. coli proteins annotated in: DIP (http://dip.doe-mbi.ucla.edu/); BIND (http://www.bind.ca/Action); and IntAct (http://www.ebi.ac.uk/intact/), respectively.Set of physical interactions derived from low-throughput experiments. We used collections of 401, 58 and 2684 physical interactions for Collection of protein complexes extracted from EcoCyc. These complexes are based on manual curation of the scientific literature and they represent a high quality set of very well known complexes. We established a functional link for each pair of proteins that are part of the same complex. This resulted in a set containing 950 protein associations.Protein complexes extracted from Butland et al. This definition of positives is intended to be a comprehensive representation of the functional associations among proteins. As a consequence of the different amount of information available for each type of functional associations, both training and test analyses are going to be more informative about the predictive capabilities of different methods for the main contributors to this set of positives, i.e. co-regulated genes and metabolic associations.We generated the negative set from the non-positive pairs among the proteins contained in the positive set (homodimers were not considered). Thus, we tried to reduce the uncertainty in the negative information by considering only proteins with information available regarding protein function. From the remaining pairs, we removed those pairs for which the prediction methods generated no value, because they are uninformative for our classifier. This process yielded 2,575,779 negative protein pairs. It is important to note that this set may still contain some uncharacterized functional associations.Building training and test sets must deal with the problem of the imbalance between classes . Thus, the training and test datasets were built using 20% positive and 80% negative instances as a compromise between representing the underlying distribution and providing a more balanced detail of both classes without affecting classification performance.The training set was composed of 2/3rds of the positive instances (making up the 20% explained above), while the test set included the remaining 1/3 of the positive protein pairs. The training and test sets were completed with some of the instances from the negative class and these were exactly 4 times the number of positive instances, to reach 80% of negative instances in each set as indicated previously. Hence, all the positive instances available were employed in the training or test set. By contrast, many negative instances were discarded. Hence, according to the aforementioned criteria, the test set was half the size of the corresponding training set.We use AODE algorithm for the classification AODE is inspired by the notion of n-dependence estimators Higher-dependence estimators typically have a weaker bias but a higher variance than NB. AODE avoids training time computation and reduces variance by overall averaging of a limited class of one-dependence estimators, since the more effective ones also typically have a very high computational complexity for training time AODE handles missing values by using only the known values of each instance when it calculates the product of probabilities. This idea is suitable for our domain, because it neither fills in unknown attribute values with the mean , nor does it ignore the instance completely like some other algorithms. Filling missing values with the mean or majority value  of binary classifiers over the full range of possible class distributions and misclassification costs. In a simpler interpretation, Cost Curve plots represent the cost probability, equivalent Accordingly, the corresponding cost curve of a classifier is made up of different straight lines, with the extremes at both sides of the Y-axis and corresponding to several pairs <ratio FP, ratio TP> obtained for the different classification thresholds. The line segments that are not dominated by any other  make up the whole cost curve.This representation usually contains the curve corresponding to the trivial classifier  and total number of predictions considered in the test set extracted from our gold standard of functional associations (see (0.15 MB TIF)Click here for additional data file.Table S1Performance of different classifiers for the Test Set. This table shows performance related descriptors for a number of different classifiers. The descriptors included are: Area Under the ROC Curve (AUC), Mathews Correlation Coefficient , True Positives (TP), True Negatives (TN), False Positives (FP) and False Negatives (FN). The table is divided in three regimes. The first one (yellow background) represents the incremental inclusion of features in AODE classifiers. New features are included from the most to the least discriminative (MCC score for these features) ones: Methods , Length (protein sequence lengths) and Nseqs (number of sequences). Rankings in the list of scores for each method are finally included (as they are derived from the corresponding methods) to build the presented APPIA classifier. The second regime (white background) shows the performance for the AODE using all the features and with flags instead of missing values. Finally, the third regime (green background) shows the performance of the other seven different classifying algorithms used in the preliminary test. MCC = (TP×TN−FP×FN)/SQRT((TP + FN)×(TP + FP)×(TN + FP)×(TN + FN)).(0.02 MB PDF)Click here for additional data file.Table S2Attributes statistics. This table shows some statistical measures of each attribute used in the classification process. The range of the column values represents the minimum and maximum value reached for this attribute in all the examples. In each case, the mean and the standard deviation are calculated without taking into account the instances with an unknown value. Total number of instances: 2,665,180. It should be noted that the high percentage of unknown values is important in many attributes.(0.06 MB PDF)Click here for additional data file.Table S3List of fully sequenced genomes used. This is the set of 118 prokaryotic genomes to which the computational prediction method has been applied.(0.04 MB PDF)Click here for additional data file.Text S1AODE detailed description. A detailed description of the AODE algorithm.(0.07 MB PDF)Click here for additional data file.


This problem can be solved if a dependency exists between sequence similarity and the conservation of the proteins’ functions.Although a great deal of progress is being made in the development of fast and reliable experimental techniques to extract genome-wide networks of protein-protein and protein-DNA interactions, the sequencing of new genomes proceeds at an even faster rate. That is why there is a considerable need for reliable methods of H. sapiens, D. melanogaster, C. elegans, A. thaliana, and S. cerevisiae and evaluated the quality of these predictions.In this paper, we introduce a novel probabilistic method for prediction of protein-protein interactions using a new empirical probabilistic formula describing the loss of interactions between homologous proteins during the course of evolution. This formula describes an evolutional process quite similar to the process of the Earth’s population growth. In addition, our method favors predictions confirmed by several interacting pairs over predictions coming from a single interacting pair. Our approach is useful in working with “noisy” data such as those coming from high-throughput experiments. We have generated predictions for five “model” organisms:  In-silico methods are widely used to transfer knowledge about protein-protein interaction networks within the organism or between different model organisms, and several general approaches to this task exist. For well-studied model organisms, it is common to use different sources of information which, in addition to sequence homology, include Gene Ontology annotations, localization databases, and other sources to predict and validate protein-protein interactions , which is the probabilistic measure of predicted interaction A’ – B’ to be true, based on the knowledge that a known interacting protein pair Ai – Bi exists in the training set, where A’ is homologous to Ai and B’ is homologous to Bi.The first problem is finding an optimal mathematical expression for individual prediction scores si. Until now, most of the considered prediction scores defined the combined score as equal to the maximum of individual scores , which is a rule for construction of a final score based on several individual scores sres e.g. .The third problem is the definition of “characteristic protein family size,” which defines a number of “individual scores” that should be taken into account when one calculates the final score for the predicted A’ – B’ pair. This problem exists in cases of certain combined scores where it is necessary to avoid good final scores that have resulted from many bad individual predictions.A number of existing methods already allow the prediction of protein-protein interactions using sequence similarity. They include domain-based methods , Bayesiai is based on an empirically derived probabilistic formula instead of ad hoc assumptions, and 2) the combined score is taken as the combination of individual scores. Therefore, in general, we score the predictions confirmed independently by several known interactions higher than the predictions based on a single known interaction. The cumulative effect for predicting protein-protein interactions was independently used in Our method is based on novel approaches to all of the problems previously mentioned: 1) our individual score sInterparalogs or generalized interparalogs. If interacting proteins A and B have paralogs A′ and B′ which also interact, we will call the pairs A – B and A′ – B′ interparalogs. There could be several interparalog pairs within the organism, which can be called generalized interparalogs.Interologs. If two interacting proteins A and B have orthologs A′ and B′ that also interact, according to Generalized interologs. Using the definition from We use protein sequence similarity as the measure of homology between two proteins. The similarity between two protein sequences is taken from the similarity score calculated by the BLAST  program using the standard BLOSUM62 substitution matrix in the manner as described in .We use only the pairs of orthologous proteins consisting of reciprocal best matching homologs in both organisms, i.e. not more than one ortholog of a specific protein per each organism.To predict interactions on the basis of protein sequence similarities, the following assumptions have been made:http://www.ariadnegenomics.com/). The databases for Homo sapiens, baker’s yeast Saccharomyces cerevisiae, the nematode worm Caenorhabditis elegans, the plant Arabidopsis thaliana, and the fruit fly Drosophila melanogaster were constructed by combining the data from published high-throughput experiments, publicly available interaction databases such as BIND and EntrezGene with the literature data extracted using MedScan technology from organism-specific PubMed abstracts and full-text articles. For more details about the construction of these databases, please refer to the PathwayStudio manual: http://www.ariadnegenomics.com/products/pathway.htmlThe protein interaction data for all species were obtained from the ResNet databases of Biological Association Network available from Ariadne Genomics , taken from nlab.org  for comphttp://interolog.gersteinlab.org  when the system changes the rate of growth.The dependency between the average “functional distance”  studies . Here, QSimilar data for all five studied organisms are shown in i and Bi, one can define the probability pi  of the interaction between their respective paralogs A′ and B′ as a product of the probabilities piA′A and pBiB′.  predictions. In this case, the dependence of functional distance upon sequence dissimilarity of two proteins from different organisms can be described in a similar way with the assumption that all pre-defined orthologs have identical functions and the limitation that only proteins which have orthologs in both organisms are used for the calculation of functional distance. These conditions allow us to calculate the functional distance between protein Ax from organism X and Ay′ from organism Y, where Ay is the only true ortholog of Ax and Ay is a paralog of Ay′. Their functional distance should be equal to the probability to not observe an interaction between proteins Ay′ and By in organism Y, given that the protein Bx, which is the only true ortholog of By from organism X interacts with a protein Ax′ paralogous to the protein Ax. The value for functional distance averaged over all possible B’s can then be plotted as a function of the sequence dissimilarity between Ax and Ay′, which is equal to the 1– sim, where sim is amino acid sequence similarity. After the same normalization procedure used for interparalogs, the resulting dependency for pairwise comparisons has the form shown in The exact estimation of all pairwise fitting parameters is impossible for some organism pairs due to the small amount of data. It can be shown, however, that the main coefficient T for all well-studied organism pairs varies from 0.4 to 0.6, as compared to the range  found for interparalogs. To better estimate the parameters, we created a dataset of averaged over all organism pairs and determined all coefficients for this dataset, which are:Individual scores must be added into one “combined” score for every predicted interaction. The “combined” score must take into account the fact that there could be several interactions in the training sets, which lead to the same prediction in the target organism. In previous work, the combined scores were typically defined as trivial functions such as minimum, maximum, sum, or average, depending on the nature of the individual score . Howeverpi) is the probability that the target proteins do not interact in every prediction method. We will refer to the score calculated using this formula as the “M-score.”Because the individual score can be interpreted as an independent probability, the final interaction probability can be expressed as:The M-score depends on the total number of individual predictions that are taken into account for the To evaluate the quality of our prediction method, we have chosen one article, which describes tasks and methods similar to ours, and has an online supplementary data with the predictions . We haveWe have looked at the percentage of true positives for the top 1,000 predictions. Our values were compared with those obtained by the method, described in . The scoive set” .For the best-studied networks (human and yeast), the negative sets are readily available. We used the Receiver Operating Characteristics (ROC) curve that plots a true positive rate as a function of false positive rate and is normally used to evaluate the accuracy of a classification score. The accuracy of methods is measured by the area under the ROC curve (One can see that the M-score (8) provides a significantly better true positive rate than the joint similarity J-score (9) for the prediction of interparalogs. For example, 35% of the top 1,000 yeast interparalog predictions scored with the M-score can be confirmed by the “golden positive set”, while the same number of top predictions scored with the J-score can be confirmed in only 20% of all cases. The same values for the human network are 20% and 10% for M-score and J-score, respectively. data not shown). Using these estimated M-score cutoffs . Thus, it appears that the prediction based on the interactions from multiple organisms has no advantages over the prediction based on an interacting pair from only one organism. We can conclude this because the protein function is conserved less between different genomes than it is for paralogs. Alternatively, it may mean that the knowledge about interolog interactions from several organisms is highly redundant and does not increase the statistical power of the prediction. We discuss this observation in more detail in the next section.A similar procedure can be used to evaluate interolog predictions. In this case, it is possible to combine M-scores from different organism pairs in the same manner as it is done for interparalogs from one organism using the “J-score”—i, A′)*) was replaced by the pi  value from “modified J-score”, where  is monotonous. Since it is necessary to have the probabilistic individual score in order to combine them into M-score, the performance of techniques a) and b) should be considered together. In case of yeast interparalogs the performance of “M-score” and “unrestricted M-score” are the same, since there are no large protein families (>40) in yeast and protein family size restriction does not change anything. Hence, for the yeast interparalogs the only difference is between “J-score” and “M-score” (The proposed scoring system includes three different techniques: (a) calculation of individual score, (b) combining the individual scores and (c) finding of protein family size. In order to evaluate an extent of contribution for each technique we have studied different score schemas for yeast and human interparalogs. The following scores were comparedA′ value  on similM-score” , and theThe relative contribution of different techniques changes dramatically in case of predictions for human proteins. The restriction on protein family size improves the quality of predictions significantly. A percentage of true positives for top 1000 “J-score”, “unrestricted M-score” and “M-score” predictions are 10%; 12% and 20% respectively. The same values for top 2000 are 9%; 10% and 18%. For top 5000 the numbers are 8%; 9% and 13% . This suWe have developed a novel scoring system that improves the reliability of predictions about protein physical interactions using the information about known interactions of their homologs. The method takes into account not only the sequence similarity between homologs but also the number of known interactions for different homolog pairs. On average, it assigns better scores to interactions predicted on the basis of several “hits,” as compared to single-hit predictions. We show that this approach allows more reliable prediction of interactions using paralogous proteins.Individual scores used in our algorithm are based on empirical estimates of the likelihood that a pair of homologous proteins with a specific sequence similarity shares a common interaction partner. We show that the correlation between this probability and a sequence similarity is approximated best by the formula developed to describe the self-similar growth of a population with a finite reproduction lifespan. The formula can be used, albeit with different parameters for prediction of both interologs and interparalogs. We found, however, that our method performs better for interparalog predictions while, for interolog predictions, its performance is comparable with the joint similarity score. The formula was developed as a best fit for human population growth by S.P. Kapitza. The curve is supposed to fit the explosive growth in the beginning that changes to significant slowdown after critical time T. In the case of protein functional divergence, this phenomenon means that proteins start losing their functional similarity more slowly after their sequences diverge beyond critical similarity. One can speculate that the far-diverged proteins still must share common interactions in order to continue being functional. However, a closer look at The analysis of parameter T, corresponding to the critical point in the functional distance, suggests the difference between functional divergence of interologs and interparalogs. This critical point corresponds to the point in time in which population growth begins to slow down following the explosive phase. While the characteristic value of T for interparalogs lies between 0.2 and 0.3 and corresponds to the sequence similarity 0.7–0.8, these numbers for interologs are 0.4–0.6. This observation suggests that two paralogs begin to lose functional similarity as measured by the number of their common interaction partners when their sequence similarity drops below 70–80%. The interologs, on the other hand, remain functionally similar until their sequence similarity remains above 40–60%. This conclusion is intuitively clear: unless a paralog acquires the new function relatively quickly during the sequence divergence, it will probably be lost from the genome due to the loss-of-function mutation. Interologs, however, are constantly under evolutionary pressure to maintain the function during a rather long time of divergence.We have found that the quality of the interolog prediction does not benefit from combining knowledge about interactions from multiple organisms. Because our own data suggest that interologs remain functionally similar during a longer divergence period, the most likely explanation is that information gathered from multiple organisms is redundant; i.e. it is enough to know an orthologous interaction pair in one organism in order to predict an interolog with high confidence. Therefore, the prediction of physical interaction could be done by selecting only one best predictive organism. Our data also confirm the observation made in  that the


Many integral membrane proteins, like their non-membrane counterparts, form either transient or permanent multi-subunit complexes in order to carry out their biochemical function. Computational methods that provide structural details of these interactions are needed since, despite their importance, relatively few structures of membrane protein complexes are available.We present a method for predicting which residues are in protein-protein binding sites within the transmembrane regions of membrane proteins. The method uses a Random Forest classifier trained on residue type distributions and evolutionary conservation for individual surface residues, followed by spatial averaging of the residue scores. The prediction accuracy achieved for membrane proteins is comparable to that for non-membrane proteins. Also, like previous results for non-membrane proteins, the accuracy is significantly higher for residues distant from the binding site boundary. Furthermore, a predictor trained on non-membrane proteins was found to yield poor accuracy on membrane proteins, as expected from the different distribution of surface residue types between the two classes of proteins. Thus, although the same procedure can be used to predict binding sites in membrane and non-membrane proteins, separate predictors trained on each class of proteins are required. Finally, the contribution of each residue property to the overall prediction accuracy is analyzed and prediction examples are discussed.Given a membrane protein structure and a multiple alignment of related sequences, the presented method gives a prioritized list of which surface residues participate in intramembrane protein-protein interactions. The method has potential applications in guiding the experimental verification of membrane protein interactions, structure-based drug discovery, and also in constraining the search space for computational methods, such as protein docking or threading, that predict membrane protein complex structures. Integral membrane proteins constitute a significant fraction of all proteins in sequenced organisms and also are targets of slightly more than half of all current drugs ,2. SimilComputational methods can address these challenges by providing predictions of which residues on the protein surface participate in protein-protein interactions. These predictions can be subsequently verified by, for example, mutagenesis experiments. The predictions can also be used as constraints for predicting the structure of the protein complex by, for example, protein-protein docking. Existing computational methods for predicting protein-protein binding sites can be broadly classified into those that utilize only 1D sequence information and those that require some information about the 3D protein structure. Sequence-only methods -8 have tHere we consider the problem of predicting protein-protein binding sites within the intramembrane region of integral membrane proteins. The previous studies mentioned above were limited to non-membrane proteins, for which considerably more experimental structures are available. Nonetheless, we find that there are currently a sufficient number of structures for training and validating a predictor that achieves accuracy comparable to our previous results for non-membrane proteins . There aA diverse set of alpha-helical membrane protein complex structures was first compiled for training and testing the prediction method. Monomers as well as multimeric complexes were included. The initial set of PDB entries for alpha-helical membrane proteins were taken from the PDBTM database ,22. A noNext, a set of non-redundant proteins, each of which contacts at least one other protein in a complex, was extracted from these structures. Because the individual proteins are taken from structures of protein complexes, their protein-protein binding sites are known. This set of protein structures was then used to train the prediction method and to assess its accuracy. The same procedure was also used to build a set of beta barrel membrane protein complex structures as well as a non-redundant set of proteins taken from these complexes with known protein-protein binding sites. Finally, the alpha-helical and beta barrel sets were combined to make the membrane protein benchmark set.The final set of membrane protein complexes contained 64 alpha-helical multimeric protein complexes comprised of 149 unique subunits, 17 alpha-helical monomeric complexes, 14 beta barrel homomultimeric complexes, and 23 beta barrel monomers. The details of this benchmark set are provided as additional file z| ≤ 15 Å, in which the z-axis is perpendicular to the plane of the membrane predicted by PDBTM and the origin is in the center of the membrane. In other words, the membrane core was assumed to be 30 Å thick, which is in agreement with the approximate values from PDBTM predictions and experimental results on lipid bilayers [Only surface residues, with relative solvent accessible surface area (SASA) ≥ 0.2, that are also within the hydrophobic core of the membrane are considered and so included in the training data. The relative SASA is calculated by dividing the residue SASA by the value for the same residue type in an extended conformation surrounded by glycine residues. Residues in the membrane core have z-coordinates with |bilayers .-2, removing redundant sequences at the 90% sequence identity level using the CD-HIT program [Random Forest predictions were made for each individual residue based on its properties. The training data for each residue consisted of frequencies of each of the 20 standard residues in a multiple sequence alignment of similar sequences and the evolutionary rate. The sequence alignments were created by searching for similar protein sequences in the NCBI nr database with BLAST  at an E- program , and gen program . Only pr program . BecauseMost machine learning classifier methods, include Random Forests, perform better on balanced input data that has a comparable number of positive and negative examples. Because of this, negative (non-binding site) examples were randomly chosen from the negative data such that there were an equal number of positive and negative examples in the training data. After training the Random Forest classifier on a balanced subset of the training data, predictions were made for all data in the  test set. The input data contained a total of 2391 positive examples for binding site residues.A Random Forest binary classifier was trained on the labeled residue data and used to predict whether or not each intramembrane surface residue is in a protein-protein binding site. The Random Forest method  was chosThe overall prediction performance was evaluated by 10-fold cross-validation in which the data was randomly divided into 10 approximately equal size sets and predictions were made for each set in turn using a Random Forest trained on the data in the remaining 9 sets. The data was divided so that all residue data for a particular protein was contained entirely within one set. This insures that the predictions are made for a distinct set of proteins from those used to train the Random Forest classifier so that one obtains an accurate estimate of the prediction performance for novel data.Briefly, a Random Forest is a set of decision trees in which the input data for each tree is randomized in two ways, by using a random subset of the total variables and by using a bootstrap sample of the data. The two main parameters in the method are the total number of trees and the number of variables per tree. Because the Random Forest generalization error converges to an asymptotic value as more trees are added, increasing the number of trees does not generally lead to worse overfitting . For theThe open source Random Forest implementation in the R  package As in our previous studies ,32, the The likelihoods in the numerator and denominator were calculated using Gaussian kernel density estimation of the scores in each respective class. A high value of R for a residue indicates that it is confidently predicted to be in a binding site, a low value indicates that it is confidently predicted to be outside of binding sites, and an intermediate value indicates an ambiguous prediction. The R values are useful for prioritizing the predictions before undertaking time-consuming and costly experimental validation.Throughout this section we consider only the intramembrane portion of membrane protein complexes since the general properties of the solvated portions of the complexes, specifically both binding site and non-binding site surfaces, are expected to have similar properties to those of cytosolic proteins. The membrane core was defined to extend 15 Å in both directions perpendicular to the central membrane plane predicted by the TMDET method and available from the PDBTM database. The TMDET method accounts for both the protein backbone geometry and hydrophobicity in order to predict the extent and orientation of the membrane relative to the protein complex. TMDET uses the structure of the complex, and so incorporates the geometrical constraints that all transmembrane segments are delimited by two common membrane boundaries. This is an advantage over sequence-only prediction methods, when experimental structures are available.Protein-protein binding sites on cytosolic proteins have different distributions of residue types on average than those on the exposed protein surface. Specifically protein-protein binding sites are enriched in large hydrophobic and uncharged polar residues and are depleted of charged residues ,33. Thisp < 2.2 × 10-16, Wilcoxon rank sum test).A different trend in residue frequencies is expected for the intramembrane portion of membrane proteins because their surfaces are contacting the hydrocarbon tails of the lipid molecules comprising the membrane so that hydrophobic residues are energetically favorable on the exposed protein surface. Statistical tests using the benchmark set revealed that intramembrane protein-protein binding sites have higher frequencies of phenylalanine, tryptophan, and tyrosine residues and lower frequencies of valine residues than the remaining intramembrane protein surface . In addition, residues occurring within protein-protein binding sites in membrane proteins have lower evolutionary rates, or equivalently higher conservation, than residues on the remaining intramembrane surface . For a given surface residue, the average score, rmax. The score of the central residue has a weight of 1 while a residue at the cutoff distance would have the minimum weight, wmin. Thus the scores for residues closest to the central one make a larger contribution to the average score Savg than those further away. The best values for the two adjustable parameters, which resulted in the highest AUC, were chosen by a grid search. The optimal values were found to be rmax = 18 Å and wmin = 0.1.in which the summations are over all residues within the cutoff distance, The overall prediction accuracy of the method was assessed by the area under the Receiver Operating Characteristic (ROC) curve for cross-validation results. The ROC curve is a plot of the sensitivity, or true positive rate, versus (1 - specificity), or false positive rate, and displays the tradeoff between these two quantities as the prediction score cutoff is varied. AUC can vary between 0.0 and 1.0. A value of 1.0 indicates perfect accuracy whereas a value near 0.5 indicates poor prediction performance.The ROC curve for the cross-validation prediction results is shown in Figure i.e. binding site residues are more often predicted as non-binding site residues and vice versa. One explanation of the anticorrelation is that whereas hydrophobic residues are more prevalent in non-membrane protein binding sites they are instead more prevalent on the lipid-exposed non-binding site surfaces of membrane proteins. Likewise, hydrophilic residues are more prevalent on the solvent-exposed non-binding site surface of non-membrane proteins whereas they are more prevalent in the protein-protein binding sites of membrane proteins. This result confirms the expectation that the different frequencies of surface residue types for membrane and non-membrane proteins, resulting from the different physiochemical environments of proteins in each class, implies that separate predictors trained on the same class of proteins (membrane or non-membrane) are required in order to achieve good prediction accuracy.It is tempting to use the larger quantity of protein-protein binding site data for non-membrane proteins in order to train a predictor for membrane proteins. This was directly tested by training the same prediction method described above on data from a non-redundant set of 4296 non-membrane proteins, sharing less than 30% sequence identity, and making predictions for the membrane protein benchmark set data. The AUC for the prediction was only 0.36. This AUC value is actually less than the random expected value of 0.5 because the prediction results are anticorrelated, Also we found in our previous study  that cenα separation distance of 8 Å belong to the same class (binding site residue or non-binding site residue). Thus core residues can be either inside or outside of the binding sites but are not near the binding site boundaries. The AUC for the core residues alone was 0.86, which is considerably higher than when residues near the binding site boundaries are included. This is consistent with the results of the earlier study, although that study only examined core residues within protein-protein binding sites. However, unlike that study, there was no significant difference in the evolutionary conservation between the core and peripheral (non-core) binding site residues. This implies that the last factor (#3), mentioned above, that contributes to improved prediction performance for core residues in cytosolic proteins does not contribute for membrane proteins. However, the remaining two factors (#1 and #2) probably also contribute to the improved accuracy for core residues in membrane proteins.Here we define a core residue as one for which all other residues within a CBecause Random Forests use a bootstrap sample to train each classification tree, the remaining unused, or so-called out-of-bag, data can be used to obtain an estimate of each individual variable's contribution to the overall prediction accuracy . This isComparison of Figure Although the quantity of training data for membrane proteins is considerably less than for non-membrane proteins, the fact that the importance of the column residue frequencies exhibit the same dependence on their frequencies of occurrence suggests that this trend is not due to a lack of sufficient data. Rather, the simplest interpretation of these trends is that the overall abundance of each residue type, which determines how prevalent the residue type is in the training data, generally dominates any differences in residue frequencies between each class (binding site and non-binding site residues). For example, even though the statistical tests showed that tyrosine residues are more prevalent in binding sites whereas leucine residues are not, the column frequencies of leucine residues are more important than those of tyrosine residues because the training data contains significantly more leucine residues, thus giving them a larger contribution to the overall prediction accuracy.We next briefly examine two examples in which the protein-protein binding site predictions aid in identifying or confirming the correct biologically relevant complex from X-ray structures. Again, cross-validation predictions, in which the predictor was trained on data for dissimilar protein complexes, were used in order to provide a realistic assessment of the prediction performance.Nitrosomonas europaea . This Nntry 3B9W,36. As cntry 3B9W for the 3B4R[e.g. small molecule binding sites, that are too small to be protein binding sites, and so may improve prediction accuracy.Figure 3B4R). Althou3B4R, would rThe protein-protein binding site prediction method for membrane proteins described in this study was found to yield accuracy that was comparable to that for non-membrane proteins. Although there are considerably fewer experimental structures of membrane proteins than non-membrane proteins, because the predictions are made for individual surface residues there is a sufficient quantity of independent examples for training a Random Forest classifier that gives accurate results. Also, as expected from the different occurrence frequencies of surface residue types in membrane and non-membrane proteins, a predictor trained on non-membrane proteins gave poor accuracy when applied to membrane proteins. Thus separate predictors for membrane and non-membrane proteins are needed. In addition, a prediction procedure that is different than the ones used in previous studies was found to give better accuracy. Random Forest predictions were first made for individual surface residues and then the resulting scores of nearby residues were averaged in order to arrive at the final prediction score. Predictions could not be made for some proteins due to an insufficient number of related protein sequences needed for the multiple sequence alignment, however this is expected to improve with the rapidly growing number of available protein sequences.The prediction method presented here is expected to have applications in guiding experimental investigations of membrane protein interactions and also in the prediction of protein complex structures using computational methods such as docking or threading. In addition to these applications, several future areas of investigation are possible. First, because the method relies only on residue-level information, it is expected to give accurate results for homology models, which are generally correct for regions with well-defined secondary structure but often have errors in loops or side chain conformations. A study of the prediction accuracy for homology models of varying quality would help quantify what accuracy can be expected. Second, because the method relies on a multiple sequence alignment of similar sequences, the choice of included sequences can affect the final prediction accuracy. The implicit assumption that the proteins with sequences in the multiple alignment have the same protein-protein binding site, may be incorrect, particularly if distantly related sequences are included. It would be useful to have a method for selecting the optimal set of sequences to include in the alignment. Finally, contiguous binding patches could be calculated from the individual residue predictions. This would then give a lower bound on the number of independent binding sites on the protein surface.AJB designed and performed the study, analyzed the results, and wrote the manuscript.Benchmark set of membrane protein complexes. This zip archive contains two files. One is a tab-separated table file with information on the PDB structures of membrane protein complexes used in this study. The other is a PDF file that provides a detailed description of the table format.Click here for file