Protein phosphorylation is a reversible post-translational modification where a protein kinase adds a phosphate group to a protein, potentially regulating its function, localization and/or activity. Phosphorylation can affect protein–protein interactions (PPIs), abolishing interaction with previous binding partners or enabling new interactions. Extracting phosphorylation information coupled with PPI information from the scientific literature will facilitate the creation of phosphorylation interaction networks of kinases, substrates and interacting partners, toward knowledge discovery of functional outcomes of protein phosphorylation. Increasingly, PPI databases are interested in capturing the phosphorylation state of interacting partners. We have previously developed the eFIP  text mining system, which identifies phosphorylated proteins and phosphorylation-dependent PPIs. In this work, we present several enhancements for the eFIP system: (i) text mining for full-length articles from the PubMed Central open-access collection; (ii) the integration of the RLIMS-P 2.0 system for the extraction of phosphorylation events with kinase, substrate and site information; (iii) the extension of the PPI module with new trigger words/phrases describing interactions and (iv) the addition of the iSimp tool for sentence simplification to aid in the matching of syntactic patterns. We enhance the website functionality to: (i) support searches based on protein roles  or using keywords; (ii) link protein entities to their corresponding UniProt identifiers if mapped and (iii) support visual exploration of phosphorylation interaction networks using Cytoscape. The evaluation of eFIP on full-length articles achieved 92.4% precision, 76.5% recall and 83.7% F-measure on 100 article sections. To demonstrate eFIP for knowledge extraction and discovery, we constructed phosphorylation-dependent interaction networks involving 14-3-3 proteins identified from cancer-related versus diabetes-related articles. Comparison of the phosphorylation interaction network of kinases, phosphoproteins and interactants obtained from eFIP searches, along with enrichment analysis of the protein set, revealed several shared interactions, highlighting common pathways discussed in the context of both diseases.Database URL: http://proteininformationresource.org/efip Bax Thr167 is phosphorylated in response to cytokine treatment, and that Thr167 phosphorylationfacilitates’Bax binding to Pin1. (PMC 2847832)‘These data suggest that The phosphorylation and interaction mentions are emphasized in bold, and the impact of phosphorylation on the interaction is emphasized in italics. From this sentence, eFIP extracts ‘Bax’ as the substrate (phosphorylated protein), ‘Thr167’ as the site of phosphorylation, ‘Pin1’ as the interacting protein and ‘facilitates’ as the impact of phosphorylation on the PPI.Protein phosphorylation is a reversible post-translational modification (PTM) where a phosphate group is added to a protein by a protein kinase. Protein interaction data involving phosphorylated proteins are critical for understanding protein networks and prediction of functional outcomes. Proteins can be phosphorylated on different residues, leading to either activation or down-regulation of their activities, alternative subcellular locations and/or interaction with distinct binding partners. Knowledgebases that curate protein–protein interactions (PPIs) are becoming more interested in capturing context information, such as the phosphorylation state of interactants. In particular, a curation effort is under way in the Protein Ontology (PRO)  to createFIP works by first detecting mentions of phosphorylation and PPIs involving the phosphorylated protein. The types of PPIs captured by eFIP include interactions between two proteins, or interactions between a protein and a protein complex, protein region or protein class. Once the phosphorylation and PPI mentions are detected, the second step is to identify a possible relation between the two events.The work reported in this article was motivated by feedback received during the participation of the eFIP system in the BioCreative-2012 Workshop Track III—Interactive Text Mining . eFIP teAddressing the users’ feedback, the main contributions of this work are: (i) full-scale processing of full-length articles from the PubMed Central open-access (PMC OA) database; (ii) the enhancement of the PPI module to include additional words/phrases for PPIs; (iii) the inclusion in the pipeline of iSimp, a sentence simplifier , 5 to imWe will first describe the eFIP system, then present the results of the system evaluation on the information extracted from PMC OA full-length articles. Finally, we will highlight aspects of the user interface and provide a use case scenario based on phosphoproteins that interact with 14-3-3 protein family members.To the best of our knowledge, there is no other system that extracts the same information as eFIP as a whole. However, various systems address individual tasks.et al.  the eFIP extraction system; (ii) the online interface and (iii) the development of a use case. The first two steps are shown in <Akt, Chk1, Ser280> is extracted by RLIMS-P from the following sentence:Chk1]_substrate is directly [phosphorylated by] [Akt]_kinase at [Ser280]_site, a modification that results in cytoplasmic sequestration’. (PMC 3317191)‘_type_of_PPI of phosphorylated [ELBIM]_interactant with [BAX]_’.interactant (PMC 4029189)‘BSO induces the [The tool is trigger based, meaning that only the sentences containing mentions of specific types of interactions are considered for the extraction of binding proteins. Although previously, the extraction of PPIs was based on the following trigger words: ‘binding’, ‘interaction’, ‘complex’, ‘association’, ‘dissociation’, ‘dimerization’ and their textual variations, in this work, we have enhanced the PPI tool to extract PPIs described with additional trigger words: ‘affinity’, ‘precipitation’, ‘recruitment’, ‘release’ and ‘sequestering’, as well as their textual variations. For more information about this tool, we refer the reader to our previous work A more complex sentence about phosphorylated BIMEL shows how the interaction with MCL1 is disrupted after their phosphorylation, while the interaction with BAX is increased after the phosphorylation:phosphorylation of ELBIM and MCL-1, resulting in their dissociation, and increased the interaction between ELBIM and ’BAX. (PMC 4029189)‘Addition of BSO induced mitochondrial injury-mediated apoptosis via the The above sentence is complex for two reasons: (i) the dissociated proteins are referred by the pronoun ‘their’, which calls for a reference resolution between ‘their’ and ‘BIMEL’ and ‘MCL-1’; and (ii) one phosphorylation event affects two PPI events mentioned in a conjunction . We could design a syntactical pattern that looks for a phosphorylation event impacting two PPI events in a sentence. However, this would not be the best solution, because each unique complex sentence would require the design of a unique syntactical pattern to extract the information from the sentence, and given the variety in writing styles, the number of such patterns could grow exponentially.The goal of the impact module is to find information about the ability of phosphorylated proteins to interact with other proteins. For this, we first need to find, in the same sentence, mentions of a phosphorylation event and mentions of a PPI event involving the phosphorylated protein. The next step is to detect whether the phosphorylation event occurs before the PPI event  and, if so, whether the phosphorylation event is causing any change in the PPI event . A simple temporal relation can be seen in the following sentence:phosphorylation of ELBIM and MCL-1.Addition of BSO induced mitochondrial injury-mediated apoptosis via the phosphorylation of ELBIM and results inMCL-1  their dissociation.The phosphorylation of ELBIM and increasedMCL-1  the interaction between ELBIM and BAX.The For this reason, we decided to concentrate only on a few simple syntactical patterns that yield high precision on simple syntactical constructions, and break complex sentences into multiple simple sentences to improve the recall. When run on the above sentence, our tool, iSimp, generates the following three simple sentences.Using simple patterns, the impact module will extract from the second sentence that the phosphorylation results in dissociation, and from the third sentence that the phosphorylation increases the interaction. iSimp detects not only conjunctions, but also lists, relative clauses, appositives, as well as some situations in which these constructs are nested. This is explained in detail in the works of Peng et al. , 5.Normalization of protein names  was performed using the GenNorm system . This stMEDLINE abstracts and PMC OA articles were indexed locally for quick retrieval using an in-house adaptation of the Lucene indexer . The numAn in-house XML parser was written to split the articles into subsections, as our module for extracting phosphorylation events was written for abstracts . Thus, we wanted to generalize our code from abstracts to other types of sections in an article. We noticed other advantages for splitting an article into subsections, mostly having to do with the confidence and certainty associated with the information extracted from various types of subsections. For example, information extracted from a sentence coming from a Results subsection might be marked with a much higher confidence score than information extracted from a sentence coming from the Introduction section. These confidence scores can then be used to display the information extracted from one article in a ranked list, or to solve potential conflicts across papers. We will pursue assigning confidence scores in the near future.In our work, a subsection is considered to be any heading together with the text occurring right afterwards and before the next heading in the article. For example, the ‘Abstract’ subsection is composed of the text between the Abstract heading and the ‘Introduction’ heading. Likewise, the heading ‘GST pull down assay’ and the text between the ‘GST pull down assay’ and ‘Blot overlay assay’ headings in For the evaluation of full-length documents, we selected 100 subsections from PMC OA articles. Subsections were chosen, one by one, in a random fashion. Some of the subsections were filtered out, such that the remaining 100 subsections spanned articles from 2000 to 2014 and followed to some extent the distribution of eFIP results by subsection. This selection process resulted in 24 Results, 21 Discussion, 18 Other, 17 figure captions, 13 Introduction, 5 Background and 2 Methods and Materials sections. The abstract sections were not considered in this evaluation, because they were evaluated in the previous work.http://research.bioinformatics.udel.edu/eFIPonline/Corpus.zip.The subsections were shown to three biocurators of the PRO database, who are familiar with the output format of the eFIP system. All three biocurators have doctoral degrees in biosciences. The biocurators were not given access to the results of the system nor were they allowed to look at the eFIP results, in order to avoid any potential bias. An initial test set given to all three biocurators revealed that inter-annotator agreement was 100%. Thus, the annotators were given different subsections to annotate, but encouraged to discuss the results among themselves when in doubt. Two biocurators annotated 25 subsections each, and the other annotated 50 subsections. On average, each biocurator spent 2 h for every 10 subsections. A total of 20 h was spent cumulatively for the entire corpus. The gold standard corpus contains information about the phosphorylation events  and the interaction events  found in each subsection, and it can be downloaded from supplementary Table S1. Network views were created using Cytoscape 3.1.1 (http://david.abcc.ncifcrf.gov/) (http://www.r-project.org/) treemap function. Information about the enriched terms, including P values and associated genes, is provided in supplementary Table S2.eFIP was queried via the eFIP web interface using the search/query terms ‘14-3-3 AND cancer’ and ‘14-3-3 AND diabetes’. Results were viewed by interactant, and those results where the interactant was 14-3-3 or one of the 14-3-3 isoforms  were validated by manual inspection of the accompanying text evidence. Redundant results were merged. The list of unique interactions is provided in pe 3.1.1 . KinBasepe 3.1.1 . Functiorf.gov/)  with ‘hiP), recall (R) and F-measure (F). We define these measures here:The accuracy of eFIP, with respect to document-level and information extraction, was evaluated in terms of precision  the statistics on full-length articles processed in eFIP; (ii) the results of the evaluation of eFIP on full-length articles; (iii) a description of the enhanced eFIP web interface and (iv) a use case exploring 14-3-3 proteins and their phosphorylated binding partners in two disease contexts.Approximately 67 084 PMC articles and 220 628 MEDLINE abstracts contain phosphorylation triggers. Of those, 77.6% of all PMC articles and 74.7% of all MEDLINE abstracts were found to contain phosphorylation events using the RLIMS-P system. About 5866 PMC articles (or 19 341 subsections) and 7441 MEDLINE abstracts contained information about PPIs involving phosphorylated proteins, which yielded a total of 23 244 sentences with phosphorylation–PPI information. Using the GenNorm system, we were able to link proteins to 6861 unique UniProtKB entries in PMC articles , as well as 16 195 unique UniProtKB entries in MEDLINE abstracts .A total of 272 annotations were marked as eFIP positive by the biocurators, i.e., containing a phosphorylation–PPI relation of interest to eFIP, with 77 containing temporal relations and 195 containing causal relations. Substrate and interacting protein information was annotated in all 272 tuples. However, only 118 of the annotations contained kinase information, and only 134 contained phosphorylation site information. We have conducted two evaluations, as shown in First, we looked at the number of subsections correctly identified by the eFIP system to contain relevant <kinase, substrate, site, interactant, relation type> tuples. For a curator, presenting relevant articles or subsections might be sufficient to aid in the curation process. Ninety-three of 100 subsections were correctly identified as positive or negative with respect to the gold standard, and 7 subsections were incorrectly identified as positive. This resulted in a precision of 92.7, recall of 100 and F-measure of 96.2 at the subsection level.Second, we looked at the amount of information correctly identified in these subsections. For this, we considered all the unique tuples identified by eFIP and/or marked by the annotators. From a total of 272 unique <kinase, substrate, site, interactant, relation type> tuples marked by the curators, eFIP correctly identified 208 as positive (TP), and missed 64 tuples (FN). Additionally, it incorrectly identified 17 as positive (FP), thus giving us a total of 289 unique tuples for evaluation. This resulted in a precision of 92.4, recall of 76.5 and F-measure of 83.7.Note that we have evaluated eFIP as an end-to-end system. Any error in part-of-speech tagging, parsing or by one of the components  will likely cause an eFIP error. All the false positives (17) are attributed to the impact module, where the directionality was, in fact, the opposite  or non-existent. Writing styles in the full-text articles are different from those in abstracts . We obseAfter the participation of eFIP in the BioCreative-2012 Workshop Track III—Interactive Text Mining , we haveThe Search Page allows for various search criteria. Keywords or phrases can be combined using Boolean operators. This input is used to query PubMed, and resulting documents are then intersected with the results stored in our local database. Users can search for a protein together with its role . Because a protein is known by many names, we allow for the search of all synonyms, delimited by the Boolean operator OR. An example query is ‘Bax OR Bcl2-associated protein OR BCL2L4’. Note that users can search for multiple proteins this way. Alternatively, users can also provide a list of PMIDs (PubMed Identifiers) or PMCIDs , delimited by comma, space or new line.At the top of the Results Page, one can see statistics on the numbers of documents, kinases, substrates, sites and interacting proteins found for the search criteria. The document IDs (Identifiers) can be downloaded if needed. The actual results are displayed in a tabular format below the statistics. Different views of the results are available: by kinase, by substrate, by interactant or by the document ID. The results displayed in the table can be downloaded, and the order of the information in the comma-delimited file will vary depending on the view from which it was downloaded.Clicking on the ‘Text Evidence’ icon will bring the user to the Document Page. Basic information about the document is provided at the top of this page: the title, the authors, the journal, the year of publication and the document IDs. If the document is a full-length article, then the various sections are listed in a drop-down list, so that the user can choose to concentrate only on one subsection, as opposed to the entire document. The kinases, substrates, sites, interactants, subsection types, impact details and sentence numbers are shown in tabular format. Clicking on any of the rows will highlight the actual sentence, below, from which the information was extracted. The sentences are displayed at the bottom of the page, with the relevant information colored the same way as in the table . Whenever possible, proteins are also normalized to UniProt identifiers and displayed below the table of results.Clicking the ‘See Cytoscape View’ link at the top of the page will open a new window, with all the proteins  displayed in a graph. Kinases are shown with green pentagons, and the arrows representing phosphorylation events are drawn in green. Substrates and corresponding sites are shown in red circles. These are connected by gray lines to the corresponding un-phosphorylated proteins, shown in blue circles. Finally, the interacting proteins are shown in orange circles, connected to the phosphorylated protein by straight orange lines in case of an association and dashed orange lines in case of a dissociation. Whenever the impact suggests an increase in the association of the two proteins, a ‘+’ is displayed. Conversely, a ‘−‘ is displayed when the impact suggests a decrease in the association of the two proteins. Hovering over any node in the graph will highlight only the nodes and edges connected with the initial node by a distance of maximum two. The Cytoscape graph can be downloaded as a PNG image or in XGMML format, which is Cytoscape compatible.To validate the results, provide additional annotations and/or leave comments, users need to log in. Once logged in, this feedback can also be saved and retrieved at a later time for further edits. Gene normalizations can also be validated. If an identifier is incorrect, the annotator can link the protein to the correct UniProt ID.To demonstrate the use of the eFIP system, we explored phosphorylation-dependent interactions involving 14-3-3 proteins. 14-3-3 proteins modulate a range of cellular processes, including cell proliferation and programed cell death, through their interactions with Ser/Thr-phosphorylated domains of other proteins . The effsupplementary Table S1). Of the 45 proteins, 15 (33%) were not included in an in-depth manual literature curation of 14-3-3 binding proteins  . For example, based on this sentence: ‘Third, ATM-mediated phosphorylation of COP1 at S387 promotes COP1's binding to 14-3-3σ’ . Four kinases were identified as phosphorylating three of the five phosphoforms. Interestingly, despite the overall small number of interactions, there was considerable overlap with the cancer results. Two of the diabetes-related phosphoforms—BAD Ser-112 and TBC1D4 (AS160) PhosphoX—also appeared in the cancer network  and comparison of interactions across different contexts. Sets of kinases, phosphoproteins and/or interactants obtained from eFIP searches can be further analysed  to gain insight into the biological processes affected by phosphorylation-dependent changes in protein interactions. The information collected about the phospho-interactants and 14-3-3 proteins is being added into the curation pipeline of the PRO . Also, the ability to filter eFIP results based on keywords facilitates comparisons of phospho-dependent PPIs in different contexts, which can reveal hidden biological connections .Various enhancements were performed on a previous version of the eFIP system to improve the user experience and expand the number of results: the inclusion of full-length articles from the PMC OA database; the enhancement of the PPI module to include additional types of PPIs; the inclusion in the pipeline of a sentence simplifier to improve the recall when extracting phosphorylation–PPI relations; the incorporation of an updated version of the RLIMS-P system for phosphorylation event extraction; the creation of a new website to enhance user experience, allowing them to search for specific kinases, substrates, interacting proteins, keywords or lists of document IDs; the incorporation of a graphical view for the network of interacting proteins; the inclusion of gene normalization; an evaluation of the eFIP system on full-length articles and a corpus of annotated data from 100 randomly chosen sections from the full-length documents.phosphorylation of Ser10 residue of histone H3, which subsequently leads to’acetylation of Lys14 residue. (PMC 4120686)‘An instance of PTM cis-crosstalk is the Another type of impact that we are currently investigating is the subcellular localization of the affected protein, as can be seen in the following sentence:‘Protein kinase IKKbeta-catalyzed phosphorylation of IRF5 at Ser462induces its dimerization and ’nuclear translocation in myeloid cells. (PMID 25326418)With the inclusion of full-length articles, we have noticed cases in which two events are mentioned in different sentences; however, a cause-effect relationship between them could be deduced. Around 15% of all possible phosphorylation–PPI relations in full-length articles fall into this category, although, according to our previous work, the frequency is much lower in abstracts. We will investigate the use of multiple sentences to detect the types of impact proposed above.We envision expanding on this work in the future in various ways. First, because the RLIMS-P patterns and rules were recently generalized to other post-translational modifications, including acetylation, ubiquitination and glycosylation, we are planning in the near future to detect the impact of these PTMs on the interactions of affected proteins. We will adapt the rules, if necessary, and process the entire MEDLINE and PMC OA corpora to extract these other types of PTMs. We are also interested in various other consequences of PTMs. One such relation is between PTMs themselves, as can be seen in the following sentence:Around 12% of phosphorylation–PPI relations were marked as hypothetical or potential by the annotators, meaning that the information was not conveyed by the authors with 100% confidence. We plan to study this type of relation and identify ways to assign confidence scores to the information extracted by the eFIP system to avoid intra-paper and inter-paper conflicts. For example, an intra-paper conflict might be between the information extracted from the following two sentences within the same article: (i) ‘We have investigated whether protein A maintains its interaction with protein B, when phosphorylated at site X’ and (ii) ‘When phosphorylated at site X, protein A dissociated from protein B’. The first sentence is stating an experimental setup scenario for which the results are unknown, and, thus, the sentence has some doubt, while the second sentence is stating an experimental finding, with 100% confidence. Confidence scores for these two types of sentences would suggest to eFIP that the information coming from the second sentence should replace the information extracted from the first sentence.Finally, we would like to investigate the creation of Cytoscape networks with information coming from multiple articles, integrating confidence information and highlighting the nodes and edges that are most discussed in the literature.Supplementary data are available at Database Online.


Protein complexes play important roles in biological systems such as gene regulatory networks and metabolic pathways. Most methods for predicting protein complexes try to find protein complexes with size more than three. It, however, is known that protein complexes with smaller sizes occupy a large part of whole complexes for several species. In our previous work, we developed a method with several feature space mappings and the domain composition kernel for prediction of heterodimeric protein complexes, which outperforms existing methods.We propose methods for prediction of heterotrimeric protein complexes by extending techniques in the previous work on the basis of the idea that most heterotrimeric protein complexes are not likely to share the same protein with each other. We make use of the discriminant function in support vector machines (SVMs), and design novel feature space mappings for the second phase. As the second classifier, we examine SVMs and relevance vector machines (RVMs). We perform 10-fold cross-validation computational experiments. The results suggest that our proposed two-phase methods and SVM with the extended features outperform the existing method NWE, which was reported to outperform other existing methods such as MCL, MCODE, DPClus, CMC, COACH, RRW, and PPSampler for prediction of heterotrimeric protein complexes.We propose two-phase prediction methods with the extended features, the domain composition kernel, SVMs and RVMs. The two-phase method with the extended features and the domain composition kernel using SVM as the second classifier is particularly useful for prediction of heterotrimeric protein complexes. Several proteins form a complex and work as a transcription factor, whereas there exist another type of proteins that work as enzymes. Hence, to identify proteins that constitute such transcription factors is useful for uncovering gene regulatory networks and metabolic pathways. Many computational methods have been developed for predicting protein complexes from protein-protein interaction networks ,2. Enriglgorithm , which r) method , which i) method . Bader a) method , which u) method , which sd DPClus , which t) method , which f) method , which g) method . Most meHowever, protein complexes with small sizes occupy a large part of whole known protein complexes. CYC2008 is a comprehensive catalogue of 408 manually curated yeast protein complexes . In the G be an undirected graph with a set V of vertices and a set E of edges, representing the protein-protein interaction network. Here, a vertex represents a protein, an edge  represents an interaction between proteins i Pand jP, and the weight ij wrepresents reliability and strength of the interaction between i Pand jP. In this paper, we use the WI-PHI database [i Pa neighboring protein to j Pif  ∈ E. Then, our proposed methods use the support vector machine (SVM), its discriminant function, and the relevance vector machine (RVM).In this section, we propose prediction methods for heterotrimeric protein complexes. More accurately, we consider the following problem: Given a network of protein-protein interactions weighted by some reliability, determine whether or not three distinct proteins that are connected in the protein-protein interaction network form a protein complex. Let database  as edge N training data {xi, ti} with target i t∈ {-1, 1} are given. For our purpose, xi corresponds to a set of three distinct proteins, i t= 1 corresponds to the case that the set forms a heterotrimeric protein complex. Then, we consider linear models represented by the formWe briefly review the support and relevance vector machines ,17. Suppi ϕdenotes a basis function, M denotes the number of basis functions, i adenotes the coefficient, and b denotes the bias parameter. In the SVM, ϕi(x) is implicitly defined as K with a positive semidefinite kernel function K, M is equal to N, and i aand b are determined by maximizing the margin. New sets x of proteins are classified according to the sign of y(x). We make use of this discriminant function y(x) in our proposed methods.where i γfor each parameter i aand a prior distribution over parameters i aare introduced to obtain a sparse model. For the classification, the model in Eq. (1) is transformed as σ(y(x)), where σ(y) denotes the logistic sigmoid function 1/(1 + -ye), and i aand b are determined by maximizing the marginal log-likelihood with respect to γ.The RVM is a Bayesian sparse kernel technique for classification and regression, and shares some characteristics of the SVM. As well as the SVM, the basis functions of the RVM are given by kernels, which are not required to be positive semidefinite. It, however, is known that training time of the RVM is in general longer than that of the SVM. In the RVM, a hyperparameter iP, jP, and k Pthat are connected in the protein-protein interaction network. Here the fifth mapping in the previous study is eliminated because more neighboring proteins increase the maximum of differences close to the maximum of neighboring weights denoted by (F3). (F1) and (F2) denote the maximum and minimum of the weights of interactions between iP, jP, and kP, respectively. The first feature in the previous study is the weight of the interaction between two proteins. Since there are at least two interactions for three focused proteins and we cannot use all the weights as elements of our feature vector without changes, we take the maximum and minimum of the weights  ∈ E,  ∈ E, or  ∈ E. It is considered that (F3), which is the maximum of the neighboring weights of a heterotrimer, should be lower than the weights of interactions in the heterotrimer. Consider the case that a protein r Pinteracts with two of proteins iP, jP, and kP, where r Pis not any of iP, jP, and k P. (F6) and (F7) denote the maximum and the minimum of the numbers of domains contained in iP, jP, and kP, respectively. The number of domains in a protein complex is expected to be large because domains are considered as mediators of protein-protein interactions.In our previous study, we proposed seven feature space mappings for prediction of heterodimeric protein complexes . These ay (F3). F and (F2)y (F3). F and (F2)d between two proteins i Pand j Pas the condition that i Pconsists of the same domains of j P, and defined equivalence =c between two sets xi and xj that consist of n on the set {1, ⋯, n}. Then, the domain composition kernel c Kwas defined byIn addition to the extended features, we examine the domain composition kernel developed in our previous study . We defiOur proposed methods take two-phase learning approach. The basic idea for designing our methods is that heterotrimeric protein complexes are not likely to share the same protein with other heterotrimeric protein complexes. We estimate model parameters of SVM using training data in the first phase, and predict whether or not the training data and the neighboring sets sharing at least one protein with the training data are heterotrimeric protein complexes, respectively. Then, the second phase predictor makes use of the discriminant values obtained by the first phase predictor. It is expected that the discriminant values for a target set of proteins and its neighboring set do not become large together if heterotrimeric protein complexes do not share the same protein.N sets xi of three distinct proteins with the corresponding label i t∈ {-1, 1}. For each xi, we calculate 7-dimensional feature vector f(1)(xi) using (F1),…,(F7) shown in Table i, j)-th element is 〈f(1)(xi), f(1)(xj)〉 + cαK, where α is a constant and x and connected in the protein-protein interaction network, where we call xi a neighboring set to xj if xi and xj share the same protein and xi is not xj  and y(x) for all xi,Suppose that the training data set comprises S| denotes the number of elements in the set S. Here, we define f p)(2(xi) = 0 (f n)(2(xi) = 0, f a)(2(xi) = 0) if f (2)(xi) using f (1), f s) (2and f a)-th element 〈f (2)(xi), f (2)(xj )〉 + cαK, and we apply some supervised learning method. It should be noted that our methods use only training data to estimate model parameters. For test data x, we calculate 〈f (2)(xi), f (2)(x)〉 + cαK for training data xi, and determine whether or not x is a heterotrimeric protein complex according to the second classifier.where |To evaluate our proposed methods, we performed computational experiments and compared them with the existing method NWE . We usedhttp://wodaklab.org/cyc2008/. We restricted positive and negative examples to sets of three distinct proteins that form a single connected component in the input protein-protein interaction network. Thus, 7 heterotrimers were eliminated, and we used 80 heterotrimers as positive examples. For negative examples, we extracted 32647 sets of three proteins included in protein complexes with size more than three of CYC2008, and we selected uniquely at random 100 examples from the sets because our methods require many neighboring sets of three proteins for an example in the second phase. It is considered that negative examples selected from such sets are more difficult to be classified than those selected from all sets of three proteins except heterotrimers.We prepared datasets using heterotrimeric protein complexes in CYC2008 protein complex catalogue , which cFor NWE, we set some options related with the size of complexes so that NWE output protein complexes with size two or more from the WI-PHI protein-protein interaction network in the same way as , and extFor measuring the performance, we used accuracy, precision, recall, and F-measure defined byTP, FP, and FN mean the number of true positive, false positive, false negative examples, respectively.where We used 'libsvm' (version 3.11)  and 'Spaf (1). α denotes the coefficient of the domain composition kernel cK. We examined α = 0.5 because the case was best for prediction of heterodimeric protein complexes in our previous study [α = 0, and 0.5 were higher than those by NWE, respectively. Furthermore, the accuracy and F-measure by the two-phase method SVM+SVM were higher than those by usual SVM with f (1), respectively. The accuracy and F-measure by SVM+RVM, however, were lower than those by SVM, respectively. It implies that RVMs may be less useful than SVMs for these problems that SVMs can be applied. Thus, the results suggest that our proposed methods SVM+SVM, SVM+RVM, and SVM outperform the existing method NWE. The results also suggest the usefulness of the second phase.We performed 10-fold cross-validation, and took the average of accuracy, precision, recall, and F-measure. Furthermore, we repeated this procedure 10 times for other datasets with randomly selected negative examples, and took the average. Table us study . NWE preWe proposed prediction methods by two-phase learning for heterotrimeric protein complexes. In the methods, we extended the feature space mappings in our previous study for prediction of heterodimeric protein complexes, and made use of the discriminant function for neighboring sets of three proteins. To validate our proposed methods, we performed 10-fold cross-validation computational experiments. The results suggest that our two-phase prediction methods and SVM with the extended features outperform the existing method NWE, which was reported to outperform many other existing methods such as MCL, MCODE, DPClus, CMC, COACH, RRW, and PPSampler, although our methods are limited to prediction of heterotrimeric protein complexes. For further evaluation, we would like to perform computational experiments for other datasets if such data become available.We have some possibility to further improve the prediction accuracy. For instance, we can use sequence information for designing feature space mappings as well as domains contained in proteins. In addition, we can introduce some probabilistic model such as conditional and Markov random fields to neighboring sets of three proteins although in this paper we considered kernels between neighboring sets.The authors declare that they have no competing interests.PR and MH developed and implemented the methods. MH drafted the manuscript. OM and TA participated in the discussions during the development of the methods and helped draft the manuscript. All authors read and approved the final manuscript.


Characterizing the interface residues will help shed light on protein-protein interactions, which are involved in many important biological processes. Many studies focus on characterizing sequence or structure features of protein interfaces, but there are few studies characterizing the dynamics of interfaces. Therefore, we would like to know whether there is any specific dynamics pattern in the protein-protein interaction interfaces. Thermal fluctuation is an important dynamical property for a residue, and could be quickly estimated by local packing density without large computation since studies have showen closely relationship between these two properties. Therefore, we divided surface of an unbound subunit (free protein subunits before they are involved in forming the protein complexes) into several separate regions, and compared their average thermal fluctuations of different regions in order to characterize the dynamics pattern in unbound protein-protein interaction interfaces.We used weighted contact numbers (WCN), a parameter-free method to quantify packing density, to estimate the thermal fluctuations of residues in the interfaces. By analyzing the WCN distributions of interfaces in unbound subunits from 1394 non-homologous protein complexes, we show that the residues in the central regions of interfaces have higher packing density (i.e. more rigid); on the other hand, residues surrounding the central regions have smaller packing density (i.e. more flexible). The distinct distributions of packing density, suggesting distinct thermal fluctuation, reveals specific dynamics pattern in the interface of unbound protein subunits.We found general trend that the unbound protein-protein interaction interfaces consist of rigid residues in the central regions, which are surrounded by flexible residues. This finding suggests that the dynamics might be one of the important features for the formation of protein complexes. Protein-protein interactions are involved in many biological processes such as signal transductions, enzymatic regulations, immunoglobulin recognitions, and allosteric controls -3. A pro1YPI) as an example, it is shown that the residues with larger B-factors  provides a simple, fast means to estimate the B-factors of residues, we used it to quantify thermal fluctuations for the residues in the unbount subunits. We analyzed the WCN distributions of interface residues in unbound subunits collected from a dataset containing 32 non-homologous homodimers , and theith residue is the summation of the square of reciprocal distance between the other neighbor residues in the same chain [The weighted contact number of the me chain :The reciprocal value of WCN has been shown closely related to the thermal fluctuation of a residue ,26, so ww σwere the mean and standard deviation of the WCN based on all the surface residues in an unbound subunit. By the above procedures, and the average normalized WCN was denoted as 〈z〉. For a global analysis, WCN were not normalized for each individual subunits but normalized for the whole residues in a dataset, and this normalized WCN was denoted by zWCN. Therefore, the average normalized WCN was denoted as 〈zWCN〉 for a global analysis. In summary, high WCN residues are more rigid while low WCN residues are more flexible.It should be noticed that based on the equation, the size of protein has influence on WCN. In order to compare WCN among protein subunits with various sizes, we normalized the WCN to its corresponding z-scores: The surface residues of a protein subunit were those residues whose solvent accessible surface area is not zero in the unbound subunit, where the solvent accessible surface areas of residues were calculated by the program dsspcmbi  with a wZ〉N, 〈Z〉C, and 〈Z〉P, where N, C, and P stand for non-interface, central interface and peripheral interface, respectively. If the normalization is global, the average normalized WCN were denoted by 〈zWCN〉N, 〈zWCN〉C, and 〈zWCN〉P.To compare the difference of WCN distributions among different regions on the surface of unbound subunits, we compared their average normalized WCNs, denoted by 〈1IL8). For this NMR structure, we used the first model for our analysis. Three PDB structures 2SDH, 2SSI, 3GAP were obsolete and were substituted with 3SDH, 3SSI, 1G6N respectively. Table S1 shows the list of protein-protein interaction pairs in this homodimer data set.We used the 32 non-homologous  protein homodimers  to show t-test, the unbound subunits that have only one or zero central interface residue were removed.In addiction, we compared the WCN distributions in homodimers with the distributions in heterodimers. There are 793 non-redundant homodimers (listed in Table S2) and 274 heterodimers (listed in Table S3) selected from 3Dcomplex [t-test. Table S4 and S5 respectively list the unbound subunits from obligate and transient complexes.We also applied WCN on obligate and transient complexes. The non-redundant structural dataset manually separated into 115 obligate complexes and 212 transient complexes , whose sThere are five protein complex datasets used in this study and are summarized in Table S6. For the residues in these various datasets, 87% to 89% residues are on the protein surfaces; among these surface residues, 12% to 20% are in the interfaces. The percentages of interface residues on the surfaces are similar for homodimers (18.4%) and heterodimers (19.8%), but are slightly different between obligate complexes (15.7%) and transient complexes (12.4%). Considering the interface residues, about one-third of the residues were in the central regions. The proportions of residues in the central regions of interfaces are similar for homodimers (31.7%) and heterodimers (32.5%), but are different between obligate and transient complexes . Our analysis suggests that the interfaces in obligate complexes tend to be larger than interfaces in transient complexes. Moreover, the central interface regions in obligate complexes tend to be larger than those in transient complexes if their interface sizes are the same.2TSC): central interface region is colored blue and peripheral interface region is colored red. The color of Figure CzWCN, black bars) tend to be larger than that of non-interface residues  as well as the WCN distribution of peripheral interface residues  tend to be smaller than non-interface residues.Figure t-test to evaluate the differences among these normalized WCN distributions, denoted by zWCN, by MATLAB. Both the P-values of C zWCN- N zWCNand P zWCN- N zWCNare smaller than 3.17 × 10-12, and the P-value of C zWCN- P zWCNis even as small as 7.83 × 10-83, indicating that the central interface residues have significantly larger WCN than non-interface residues, and the peripheral residues have significantly smaller WCN than non-interface residues.We performed the Student's z〉C, 〈z〉P and 〈z〉N, where C, P and N represented central, peripheral, and non-interface regions. We found that 〈z〉C is larger than 〈z〉P in all 64 unbound subunits from the 32 homodimers . When compared with 〈z〉N, 〈z〉P is smaller than 〈z〉N in 91% interfaces (58/64), and 〈z〉C is larger than 〈z〉N in 75% (48/64) interfaces . These results showed that the relationship between the average WCNs from different surface subregions holds not only for all the residues across the datasets, but also for the residues in every unbound subunit.Besides the average WCN values of residues for the whole dataset, we calculated the average WCN values of residues in different surface subregions of each individual unbound subunit, denoted by 〈zWCN〉C > 〈zWCN〉N > 〈zWCN〉P, where 〈zWCN〉Y denotes the average normalized WCN in the surface subregion Y  from the 793 homodimers dataset obeyed the inequality 〈t Figure  and 3D.zWCN〉C than the later .For the interface subregions in unbound protein subunits, our analyses showed the distinct distributions of packing density, suggesting distinct thermal fluctuation. These distinct thermal fluctuations reveal specific dynamics pattern in unbound interface: interface regions consist of rigid residues in the central regions, and the peripheral regions consist of flexible residues. This specific dynamical feature appear in various protein complexes including homodimers, heterodimers, obligate complexes, and transient complexes, based on the average packing density of surface subregions with 〈Previous studies have divided an interface into buried region (core region) and exposed region (rim region) upon complexation, and have found that this two regions vary in sequential, structural, and evolutionary features such as amino acid composition, degree of sequence entropy and sequence conservation ,5,7,36, The mechanism for protein-protein association was reviewed that a temporary complex is formed through nonspecific collisions guided mostly by electrostatic interactions, and then this temporary complex reorientates to form its final structure guided mostly by desolvation . The ratThe authors declare that they have no competing interests.JJL analyzed data, performed the statistical analysis.ZLL and TTH collected the datasets, wrote the codes and performed the calculations.JKH and TTH conceived and designed this study.JJL and TTH drafted the manuscript.Figure S1 - A structure of triosephosphate isomerase colored by B-factor and by WCN. Figure S2 - Comparison of the B-factor and WCN profiles of triosephosphate isomerase. Figure S3 - Comparison of the normalized WCN in difference surface subregions for the small homodimer dataset. Table S1 - The list of the protein-protein interaction pairs from 32 homodimers.Table S2 - The list of the protein-protein interaction pairs from 793 homodimers. Table S3 - The list of the protein-protein interaction pairs from 274 heterodimers. Table S4 - The list of the protein-protein interaction pairs from 115 obligate complexes. Table S5 - The list of the protein-protein interaction pairs from 212 transient comoplexes. Table S6 - The protein complexe datasets analyzed in this study.Click here for file


Developing suitable methods for the identification of protein complexes remains an active research area. It is important since it allows better understanding of cellular functions as well as malfunctions and it consequently leads to producing more effective cures for diseases. In this context, various computational approaches were introduced to complement high-throughput experimental methods which typically involve large datasets, are expensive in terms of time and cost, and are usually subject to spurious interactions.In this paper, we propose ProRank+, a method which detects protein complexes in protein interaction networks. The presented approach is mainly based on a ranking algorithm which sorts proteins according to their importance in the interaction network, and a merging procedure which refines the detected complexes in terms of their protein members. ProRank + was compared to several state-of-the-art approaches in order to show its effectiveness. It was able to detect more protein complexes with higher quality scores.The experimental results achieved by ProRank + show its ability to detect protein complexes in protein interaction networks. Eventually, the method could potentially identify previously-undiscovered protein complexes.http://faculty.uaeu.ac.ae/nzaki/Research.htm.The datasets and source codes are freely available for academic purposes at  Merging by Cohesiveness, used to refine the detected protein complexes. Here, complexes are viewed as entities of highly-interconnected members that are well-separated from the rest of the interaction network. The experimental studies and results greatly favor our approach.Proteins often collaborate by forming groups referred to as protein complexes in order to execute various cellular functions . AccordiProRank ,14 is a 1. Pruning: removing unreliable interactions which could negatively affect the detection process using the AdjustCD method ,20, a we3. Protein Similarity Calculating: proteins belonging to the same complex most likely have evolutionary relationships. Consequently, the similarity scores among all the proteins in the network are calculated using pairwise alignment.4. Protein Ranking: a ranking algorithm, analogous to the PageRank algorithm, is used to order proteins based on two criteria: the number of interactions in which they participate and their similarity levels with other proteins.5. Complex Detection: using the spoke model, essential proteins, which do not belong to any of the types defined in step 2, are consecutively considered by their decreasing ranking order and each of them is pulled from the interaction network along with its neighbors to form a protein complex. Here, each protein can belong to one complex only.In addition to the steps mentioned above, the ProRank algorithm discards formed protein complexes of less than three members. Also, it merges two complexes if more than 50% of the neighbors of each protein belonging to the first complex are in the second complex. To show the potential of the approach, we consider the network presented in Figure Although the ProRank method achieved competitive results when compared to other approaches, it could still be improved further. Pruning, filtering, ranking and complex-detection steps constitute strong building blocks of the approach. This is true since unreliable interactions can be removed by the pruning step; noisy proteins can be identified by the filtering phase; essential proteins can be ordered based on their importance in the network by the ranking step; and detected complexes can be formed starting from essential proteins by the spoke model at the complex-detection stage. On the other hand, the similarity calculating step by which the similarities among all the proteins in a given interaction network are computed is computationally very expensive because such networks are typically very large. Since the effect of this step on the final results is relatively minimal  and becaAdded to that, proteins could contribute in multiple cellular functions by being part of several protein complexes . For ins1. The set of generated complexes is filtered to remove duplicates resulting from the complex-overlap notion.Merging by Cohesiveness, is applied in the direction of exploring more variations of the detected complexes. In consistency with the initial considerations of the ProRank method, we rely on the key roles of the essential proteins in the network to establish the merging process. All the detected complexes are matched against each other for possible merging. Two complexes, C1 and C2, whose percentage of overlapping essential proteins is above a merging threshold, merging_threshold, are merged along with their interconnections to form a larger complex C. Then, the process adopts the cohesiveness measure introduced in , the corresponding formulae are given by the following equations ) where tij represents the number of proteins that are found in both predicted complex m and reference complex n.ProRank + was tested on five large-scale protein-protein interaction datasets associated to the well-studied yeast microorganism. Four of the datasets consist of weighted protein interactions, they are: Collins , Krogan elden in  to calcuD, and their experimental settings are:The steps of applying ProRank + on a given dataset, D using the AdjustCD method [1. Pruning: removing unreliable protein interactions from D method ,20. This2. Filtering: identifying bridge, fjord, and shore proteins which could add noise to the network, as defined in .3. Protein Ranking: proteins are ordered using a ranking algorithm, in analogy with the PageRank algorithm.4. Complex Detection: all the essential proteins, i.e. those that are filtered as noise, are seeds based on which detected complexes are formed using the spoke model. Here, a protein can belong to more than one complex.5. Pre-processing: The set of predicted complexes is filtered to remove possible duplicates generated due to the introduced overlap assumption.6. Merging by Cohesiveness: Two detected complexes, whose overlap is above a merging threshold, here 75%, are merged. The subsequent complex is iteratively extended following the presented merging procedure.7. Post-processing: again, the refined set of predicted complexes is filtered to remove possibly replicated copies of the same complexes resulting from the previous merging step.ProRank + was compared to other state-of-the-art methods, applied on the same datasets and evaluated based on the same quality scores. These methods include ProRank  to highlSn), geometric accuracy (Acc) and maximum matching ratio (MMR) for all the considered datasets. However, it could not surpass the clustering-wise positive predictive value (PPV) of ProRank which was the highest for all datasets. This can be justified by the fact that PPV tends to be lower when the overlaps among the detected complexes are substantial. By the PPV formula, a complex-detection algorithm that fully succeeds in detecting the reference complexes has a PPV value less than or equals to 1 since there is a matching predicted complex for every reference complex, in addition to other predicted complexes that partially overlap with reference complexes. On the other hand, a dummy detection algorithm which distributes the proteins into separate sets of single elements has a PPV value equals to 1, which is greater than the PPV of the perfect algorithm that is able to detect all reference complexes. Consequently, PPV values must be carefully analyzed since they may not always reflect the competence of a certain method. Moreover, the geometric accuracy (Acc) is negatively affected by the predicted complexes that do not match any of the reference complexes. This somehow contradicts the initial purpose of developing methods for the detection of protein complexes which mainly consists of finding previously unknown or undiscovered entities. Accordingly, the MMR measure [MMR values achieved by ProRank + are in the favor of the proposed approach. We hereby note that our approach could also be explored using other pruning methods such as the ones introduced in [The experimental results show that ProRank + detected a higher number of protein complexes that matched with the reference set. It is worth-mentioning that the number of clusters predicted by ProRank + is relatively higher than the number of clusters returned by the other methods for Collins, Gavin and BioGRID datasets. Nevertheless, the ratio equivalent to the number of matched complexes over the number of detected clusters falls within the same range of the ratio corresponding to the other methods. Added to that, ProRank + achieved higher clustering-wise sensitivity  and the maximum matching ratio (MMR) scores.Detecting small protein complexes is not a common feature of complex-detection methods. In fact, it is important to identify such complexes in protein interaction networks. For instance, among the 313 protein complexes included in the MIPS catalogue , 104 comWhen tested on various datasets, weighted and unweighted, ProRank + was able to detect more complexes than state-of-the-art methods with higher quality scores. Indeed, the method could be very helpful for biologists if it was tested on Human interactions and proved valuable in detecting known protein complexes of key roles in normal and abnormal cellular functions. Therefore, we applied our method on the Human interactions dataset in the BioGRID repository . The int1. The CCT micro-complex  which pa2. The Ribosomal protein complex  was dete3. The PA700-20S-PA28 complex  was dete4. A recent publication  confirmeThe above experiment affirms the ability of ProRank + to identify significant and key protein complexes from protein interaction data. In addition, such outcomes could potentially contain relevant and previously-undiscovered protein complexes or unidentified protein members of certain complexes.In this paper, we presented ProRank+, an efficient method for detecting protein complexes in protein-protein interaction networks. The detection process is mainly centered on a ranking algorithm that allows the identification of key proteins based on which the corresponding components are formed. It is also tailored by a series of pruning, filtering and merging steps, allowing the refinement of the drawn complexes. Unlike most approaches, the design of our method is not bound by the sole association of protein complexes to dense regions in interaction networks. In addition, ProRank + takes into account possible overlaps among complexes and this is an important assumption that reflects biological facts. In contrast with other methods, the experimental study underlined the competitive ability of ProRank + to identify protein complexes. The performance of our algorithm was tested using weighted and un-weighted datasets, and using Human protein interaction data as well. The results were in favor of the introduced approach.The authors declare that they have no competing interests.EMH and NZ have contributed to the conceptual development of the method. EMH has performed the experimental work and the statistical analysis. Both authors contributed to the manuscript writing. All authors read and approved the final manuscript.


Summary: We present a practical computational pipeline to readily perform data analyses of protein–protein interaction networks by using genetic and functional information mapped onto protein structures. We provide a 3D representation of the available protein structure and its regions  for the selected genetic variants and/or SNPs, and a prediction of the mutants’ impact on the protein as measured by a range of methods. We have mapped in total 2587 genetic disorder-related SNPs from OMIM, 587 873 cancer-related variants from COSMIC, and 1 484 045 SNPs from dbSNP. All result data can be downloaded by the user together with an R-script to compute the enrichment of SNPs/variants in selected structural regions.Availability and Implementation: PinSnps is available as open-access service at http://fraternalilab.kcl.ac.uk/PinSnps/Contact:franca.fraternali@kcl.ac.ukSupplementary information:Supplementary data are available at Bioinformatics online. High-throughput experiments are routinely performed to decipher genetic, metabolic and protein-protein interaction networks (PPINs) and bioinformaticians are compelled to develop efficient and accurate tools to assist decision-making based on available data from multiple sources ; this extracts structure-integrated human PPINs, enriched with information from homologous protein domains with sequence identity higher than 30%. The main strengths and differences to previous approaches lie in (i) the use of homologous structures of human protein sequences in the PPINs to map the studied variants, which more than doubles the available positional 3D information; (ii) the mapping onto predefined protein regions  along with the mapping of functional sites and Post-Translational Modifications (PTMs) . This i UniProt ; (iii) a UniProt .Fig. 1.Supplementary Table S1. The current release includes data of 16 603 proteins, of which 4673 have a resolved structure and 4962 have a homologous structure .The PPIN used in this study has been derived as a non-redundant set of protein interactions from the list of human PPIs given in Supplementary Fig. S4).PinSnps is, to our knowledge, one of the largest collections of variants mapped onto 3D coordinates. SNPs from dbSNP  that represents the fingerprint of the user-selected information. These profiles were generated based on information obtained from sequence alignments, available structural information, human genetic data  and UniProt protein functional site and PTM annotations. PSI-BLAST .Conflict of Interest: none declared.


JET2 is freely available at www.lcqb.upmc.fr/JET2.Protein-protein interactions (PPIs) are essential to all biological processes and they represent increasingly important therapeutic targets. Here, we present a new method for accurately predicting protein-protein interfaces, understanding their properties, origins and binding to multiple partners. Contrary to machine learning approaches, our method combines in a rational and very straightforward way three sequence- and structure-based descriptors of protein residues: evolutionary conservation, physico-chemical properties and local geometry. The implemented strategy yields very precise predictions for a wide range of protein-protein interfaces and discriminates them from small-molecule binding sites. Beyond its predictive power, the approach permits to dissect interaction surfaces and unravel their complexity. We show how the analysis of the predicted patches can foster new strategies for PPIs modulation and interaction surface redesign. The approach is implemented in JET e.g. drugs) could best interfere with protein partners. To answer these questions, a detailed description of the interaction at the atomic level is needed and we present a novel computational approach, JET2, bringing insights on such a description. Beyond its highly precise predictive power, the approach permits to dissect the interaction surfaces and unravel their complexity. It fosters new strategies for protein-protein interactions modulation and interaction surface redesign.Many questions regarding Protein-Protein Interactions (PPI) cannot be answered by just knowing the approximate location of the interaction site at the protein surface but demand an understanding of the geometrical organization of the interacting residues. For instance, one would like to estimate the number of interactions for a protein, identify precisely the borders of each interaction site possibly overlapping other sites, understand the structure and the usage of a moonlighting protein interaction site shared with several partners, identify the anchor points in an interaction site that allow for strong versus weak binding, identify the locations on a protein surface where artificial molecules ( PLOS Computational Biology Methods paper.This is a e.g. small-molecule binding pockets). Numerous studies have described some structural properties of PPIs sites [Proteins regulate biological processes through a complex network of dynamical interactions. Protein-protein interactions (PPIs) are considered as increasingly important therapeutic targets –4. HowevIs sites –13. By aIs sites , 15. TheIs sites –17; theyIs sites  and, theIs sites –23. StarIs sites . An effoIs sites .e.g. drugs) could best interfere with protein partners. To answer these questions, a detailed description of the interaction at the atomic level is needed and any computational tool bringing insights on such a description becomes extremely useful.Many questions regarding PPIs cannot be answered by just knowing the approximate location of the interaction site at the protein surface but demand an understanding of the geometrical organization of the interacting residues. For instance, one would like to estimate the number of interactions for a protein, identify precisely the borders of each interaction site possibly overlapping other sites, understand the structure and the usage of a moonlighting protein interaction site shared with several partners, identify the anchor points in an interaction site that allow for strong versus weak binding, identify the locations on a protein surface where artificial molecules  for protein interface prediction . JET rel2. Our basic goal is to unravel the complexity of protein interaction surfaces by pinpointing characteristics that can define their structures and distinguish their multiple properties. We show that interaction sites can be described by very simple and general rules of organisation of the interacting residues, indicating that evolutionary constraints on this organisation exist and can be revealed.The present work revisits the idea formalized in JET, by defining a protein interface as formed by three structural regions, called seed, extension, and outer layer, that approximate the structural notions of support, core, and rim defined for experimental interfaces  S1 Fig)S1 Fig). 2 is a new tool that takes as input the three-dimensional structure of a single protein and exploits both sequence and structural information, in contrast to JET. The strategy implemented in JET2 does not rely on learning and the method is computationally much simpler than any machine learning approach. We show that the use of a very straightforward geometric descriptor (circular variance) captures with remarkable precision the intrinsic geometry of protein-protein interfaces. Moreover different combinations of structural and sequence descriptors allow the detection of different protein surface regions and to deconstruct protein interfaces. We provide a completely versatile tool that enables the user to tune all parameters depending on the biological question one wants to answer. JET2 is freely available to the community and can be downloaded at www.lcqb.upmc.fr/JET2.JETJET), interface physico-chemical propensities (PC) and circular variances (CV)  see  in the 1(CV) see . The val(CV) see . Interac(CV) see . TJET dionserved . Core reJET, a narrow distribution is observed around 0.84 while values for 1-CV are not higher than 0.58  that are generally highly conserved and would not contribute significantly to the interface  that combine evolutionary information from the sequence (TJET), amino-acid interface propensities (PC) and local geometry of the structure (CV), as follows  are detected and grouped to form cluster seeds, which are then extended using TJET and PC. An outer layer is finally added considering PC and CV. SC1 is intended to detect diverse protein binding sites and thus corresponds to the general case.SC2 uses a combination of TJET and CV for seed detection and extension. This ensures that strong evolutionary signals are captured while avoiding residues too buried inside the protein. The outer layer is defined based on PC and CV, as in SC1. SC2 was designed to specifically detect protein interfaces that are located close to or overlap with small-ligand binding pockets.SC3 disregards evolutionary information and rather employs PC and CV for detecting all three components. The development of SC3 was motivated by the observation that some protein interfaces, e.g. antigen-binding sites from PPDBv4, contain very low conservation signal to none. We expect SC3 to yield consistent predictions for those difficult cases.SC, TJET, PC and/or CV are combined in a very straightforward manner without any specific weight  that evaluates the density of protein around an atom. This simple geometric descriptor captures the structural properties of interacting residues. To properly assess the predictive role of CV, we compared JETJET2 see  to JET/iJET2 see . One sho2/iJET2 dramatically improves the detection of these interfaces compared to JET/iJET  and the shark new antigen receptor PBLA8 (2I25:R) illustrate the power of SC3 in predicting with high precision (PPV = 72% and 60%) antigen-binding sites while iJET failed to correctly detect them (Lowly conserved interacting residues are found in the antigen-binding sites of the 25 antibodies from PPDBv4 . JET2/iJJET/iJET . The scoect them .SC3 detects 52% of the FCG-binding site of FCG receptor (1E4K:L) with a very high precision value of 86%  from PPDBv4 and alaserpin  from Huang (PPV = 24%).In addition to antigen-binding sites, e of 86% . It alsoom Huang . By cont2 greatly improves the detection of these residues for both testing sets . For 80% of these proteins, SC2 enabled to get a more precise definition of the protein interface  of these cases, JET2/iJET2 automated procedure successfully detected the presence of a small-ligand binding pocket and consequently chose SC2 for protein interface prediction  and the Ras-related protein Rab-33B (2G77:L) with precision values of 50% and 64%  the protein interface displays very low evolutionary signal, hence it is correctly detected using SC3 , (2) the protein interface does not contain a significant number of protruding residues and is thus correctly captured by SC1 , (3) the protein interface displays very peculiar features and is not detected by JET2/iJET2 (1AZS:R and 1XQS:L from PPDBv4).Among the 67 analyzed proteins, 13 have a protein interface whose prediction is not improved when using 2 was run on the entire PPDBv4 (except for antibodies) and Huang datasets, it automatically chose SC2 for 126 proteins—90 from PPDBv4 and 36 from Huang. 62 of those proteins have a bound small molecule in their PDB structure . For 67% of the remaining proteins, we could find experimental evidence reported in the literature that the proteins do interact with a small molecule (see When JETi) exploiting the local geometry of the protein surface in combination with evolutionary information often permits to better define protein-binding patches and segregate them from small ligand-binding pockets, (ii) JET2/iJET2 automated clustering procedure is efficient for selecting the most appropriate scoring strategy (SC2).Overall, this analysis revealed that  and SC3 often point to different surface regions  protein interface may be comprised of multiple recognition patches. Janin and co-authors  previous regions . This suustering  of exper2 complete automated clustering procedure . We also observed cases where SC1-2- and SC3-predicted patches were either almost disjoint  or largely overlapping . For 60 proteins—44 from PPDBv4 and 16 from Huang—JET2 predicted a site comprised of multiple patches, i.e. the second round of the clustering procedure added at least 5 residues and the added residues represent more than a quarter of the whole predicted site. 57% of those predictions match experimental interfaces comprised of multiple recognition patches, according to the definition of Janin and co-authors [JETrocedure , by combrocedure . For bot-authors .2 predictions for ferritin (1IES:B) and lumazine synthase (1NQV:A) contain two patches, one detected by SC1 and the other by SC3, that match well one or several experimental recognition patches  with an accuracy value of 87%. The SC3-predicted patch matches the experimental patches 3 and 4 (highly protruding) with an accuracy value of 88%. For tryptophan synthase beta chain 1 (1WDW:R), iJET2 predicted a site composed of two patches issued from SC2 and SC3. The SC2-predicted patch corresponds to the experimental patches 1 and 2 (Acc = 96%) while the SC3 prediction corresponds to patch 3 (Acc = 98%). Interestingly the experimental patches 1 and 2 display different characteristics: the former is highly conserved while the latter comprises a concave conserved region (in red) and lowly conserved highly protruding residues (in green). This example illustrates the power of SC2 to detect interacting residues having different properties.iJET2 predictions are composed of multiple patches, only one predicted patch matches the experimental interface. For 2 out of 3 proteins (66%) from Huang and 11 out of 23 proteins (48%) from PPDBv4, the JET2 predicted patch that would be considered as false positive actually corresponds to an interaction with another partner, based on experimental data available in the literature  origin(s) of an interaction, i.e. the set(s) of interacting residues forming the core(s) of the physical contact between the two partners. It reveals how these multiple origins shape large protein interfaces and, in doing so, it revisits the way recognition patches are defined. Moreover, it enables to characterize the different properties of these recognition patches. Such information cannot be obtained by using purely 3D position-based clustering of experimentally known interaction surfaces.This analysis showed that JET2 to understand the geometry of these patches by analyzing two proteins that interact with multiple partners via the same region of their surface.Some proteins present several binding sites, to different partners, that have a lot of residues in common. In such cases, we expect predicted patches to encompass residues involved in the interaction(s) with one, several or all partners. Consequently, given an experimental complex, residues that would be considered as false positives might actually participate in the interaction with another partner. We used JETα, β and γ . Once activated, the Gα subunit dissociates from the complex and triggers phototransduction signaling cascade. The regulatory protein RGS9 terminates the signal by binding to Gα . By using SC2, iJET2 predicted the Gβγ- and RGS9-binding patches of Gα with high sensitivity (Sens = 58% and 62%) and high precision (PPV = 78% and 54%). The two patches predicted over the two structures share 73% of their residues (Transducin is a heterotrimeric guanine-nucleotide-binding (G) protein composed of three polypeptide chains residues . Among tresidues .E. coli facilitates the reduction of other proteins such as PAPS reductase . Trx is itself reduced by Trx reductase . In addition to its anti-oxidant function, Trx also plays a structural role upon bacteriophage T7 infection by associating with T7 DNA polymerase (PDB code: 1X9M) [2 (SC2). The two predicted patches share 84% of their residues and T7 DNA polymerase also binds to this region (Thioredoxin (Trx) from e: 1X9M) . This abe: 1X9M) . 86% of s region . 60%  but rather low precision (< 60%) and looked for additional structural data in the Protein Data Bank (PDB). Among the 11 identified proteins, we could identify another partner for 6 (55%) of them and match the false positives of JET2 prediction with the corresponding interaction site  for which SC1 predicted a patch that corresponds to the binding site of the snake toxin fasciculin and SC3 predicted well the protein homodimeric interface  cover about 50% and 60% of all protein interfaces from Huang and PPDBv4, with precision of 66% and 43% respectively  is comparable to that of VORFFIP on the Huang dataset , better overall localization (1K5D:L) or more precise definition (1E4K:L) of the experimental protein interfaces than VORFFIP predictions. iJET2 also detects more experimental interacting residues in the multi-patch predictions reported on The VORFFIP method  integrat dataset . On PPDBy values . iJET2 p2 average sensitivity and precision values are 59% and 43%. Consequently, PredUs predicted overall more interacting residues than iJET2 but with lower precision. In addition, there are a significant number of cases where the PredUs calculation failed. For the majority of these cases, iJET2 performed very well: 10/13 predictions have sensitivity values above 50% (up to 100%) and 7/13 predictions have precision values above 50% (up to 82%). In 9 cases over 13, SC3 alone or in combination with SC1 or SC2 yielded the best prediction.The PredUs method , 47 predPPI method [PPI applies meta-threading to identify structurally and functionally related templates and maps the known interfaces of the templates to the query protein using structural alignment. Then, the algorithm combines the information from the templates with four residue-based descriptors  to compute interfacial probability scores for every residues of the query protein, by using support vector machines and a naive Bayes classifier. The method was shown to outperform PredUs when protein models are given as inputs instead of experimentally determined structures [PPI was applied to the proteins from the Huang dataset, for which it showed better performance values than iJET [PPI achieved a sensitivity of 62% and a precision of 62% (see Table 5 in [2 displays a lower sensitivity of 48% (resp. 42%) but a higher precision of 66% (resp. 69%) with 2 (resp. 8) runs out of 10. We used the eFindSitePPI web server [2 ability to predict and dissect multi-patch interaction sites (Sens = 33%) and lumazine synthase  were detected by eFindSitePPI with very high precision of 96% and 94% respectively (2 predictions (PPI due to a too large number of residues in the protein structure.The eFindSiteI method  combinesructures . eFindSihan iJET . On averble 5 in ). iJET2 b server  to predion sites . About oectively . The predictions . The cas2/iJET2 on Huang and PPDBv4 are comparable to those computed for state-of-the-art methods that use machine learning algorithms. An advantage of our method is that it predicts interacting residues that form recognition patches, it gives insights into the origins of these patches and it enables to contrast the different properties of the interacting residues and their role for the molecular recognition specificity. It can be applied to proteins for which a structurally similar protein with experimentally characterized interface(s) cannot be found, to proteins of arbitrary length and to multi-chain complexes.Overall, these analyses showed that the performance values computed for JETThe geometrical support-core-rim model suggested us a novel way to predict protein binding sites. We highlighted a structural feature of these sites that was never exploited for PPI prediction before: the rim mainly consists of protruding residues. We showed that circular variance represents an elegant solution for detecting such residues, especially when the evolutionary signal is very low. Moreover, when combined with conservation levels, CV achieved extremely high precision in the detection of PPI-mediating residues versus nearby residues forming small-molecule binding pockets.A previous analysis of solvent-exposed loops in single-domain proteins suggested that some loops may drive fulfillment/prevention of homodimerization . In our 2. We predicted interfaces for 238 protein complexes forming a wide spectrum of functional and structural classes. The construction of a predicted interface through the generation and the fusion of patches associated to different scoring schemes allows JET2 to identify different origins of the interactions and recover the dissecting patterns of protein-protein recognition sites that can be obtained by residue density analysis of experimental interfaces [We have addressed the issue of the specific and precise automatic detection of protein interaction surfaces with JETterfaces . We expl2 predicts remarkably well the antigen-binding sites of the 25 antibodies from PPDBv4 by exploiting the local geometry of the protein surface (SC3). Interestingly, the evolutionary trace-driven strategy (SC1) captured with high accuracy the interfaces between light and heavy chains . Another patch was predicted by using SC3 and the overlap between the two patches is 50%. We could identify five residues in the SC3-predicted patch that are not involved in the interaction with ICAM-1 but are found in the interface with Efalizumab (PDB code: 3EOA)  is an integrin that plays a crucial role in antigen-specific responses  by recoge: 3EOA) . Efalizu2 to detect multiple interactions involving different partners of a protein. By manually inspecting the predictions and looking for additional experimental data reported in the literature, we could assess JET2 predictive power beyond the interactions present in the studied datasets. It is likely that the increasing availability of experimental structures of protein complexes will further validate JET2 predictions in the future. Let us stress however that many aspects of protein regulation , which are out of the scope of this work, may also influence protein binding specificity.These examples illustrate the predictive potential of JET2 with state-of-the-art methods for predicting protein-protein interfaces based on machine learning algorithms. JET2 is computationally much simpler than any machine learning approach. It combines in a rational way three residue-based descriptors that have straightforward biological interpretability. This strategy allows to reach comparable or even better results than dozens of features used to train unsupervised classifiers. We thereby demonstrate that machine learning is not a necessary algorithmic approach for protein-protein binding site prediction. To have few features instead of dozens is important while exploring the very large landscape of protein interaction sites, since many of these sites might not be classifiable yet, and many others might be overlapping, making their recognition a difficult task for learning algorithms. A clear advantage of JET2 is that it provides a detailed residue-based characterization of the conservation, physico-chemical and geometrical properties of the recognition patches. By characterizing different signals at the surface of a protein, it enlightens the complexity of this surface and more specifically of the surface regions involved in functional interactions. It can be used to discover novel interfaces, which is not possible with methods based on the learning of experimentally known interfaces of structurally similar proteins.We have carefully compared the performance of JET2 performance shows that the first step of the clustering procedure, i.e. seed detection, yields very precise predictions with zero or very few false positive(s) for a large number of proteins . The Levy model [rasa) changes, as computed by NACCESS [support residues are buried in unbound and bound protein ; core residues become buried upon binding to the partner ; rim residues are exposed in unbound and bound protein . The support and the rim resemble protein interior and surface respectively, while the core has a specific amino-acid composition.We describe experimental protein interfaces by using the Support-Core-Rim model information obtained from complex structures  [JET) and amino-acid physico-chemical properties (PC). JET does not need to know the binding partner but only requires information on a single query protein. The tool was designed to (i) detect very different types of interfaces , (ii) provide predictions even with weak signal thereby ensuring broad applicability, (iii) provide robust consensus predictions from iterative runs (iJET). An important characteristics of JET algorithm is that it uses suitable heuristics to detect and extend binding patches. Consequently alternative predictions are produced by different JET runs. Consensus predictions are defined from the likelihood of a residue to belong to the interface predicted in several JET independent runs.JETes (JET) . JET pre2 method requires as input a protein query sequence for which three-dimensional structural coordinates are available in the Protein Data Bank (PDB) [2 determines the conservation level (TJET) of every amino acid in the query sequence. The calculation is performed on a set of homologous sequences . This step, essentially unchanged compared to JET, was extensively described in [The JETnk (PDB) . In a firibed in .2 detects and extends putative binding patches at the surface of the three-dimensional structure of the query protein. For this, residues are scored using a mixture of three descriptors: the evolutionary traces (TJET) computed in step 1, interface propensities (PC) specific to each amino acid and circular variances (CV) computed from the protein 3D structure and representing the density of protein around each residue. JET2 implements three different scoring schemes, i.e. combinations of the three descriptors  highly-scored residues are selected and grouped together based on 3D proximity (< 5Å) to form cluster seeds; (2) seeds are extended by progressively adding highly-scored neighboring residues until a cluster mean score threshold is reached down; (3) an outer layer is added comprised of highly protruding residues. Seeds too small to be considered as putative protein binding sites are filtered out, as described in [2 allows to automatically combine different binding patches predicted using complementary scoring schemes in order to form multi-patch binding sites  either the user let JET2 automatically decide which scoring scheme is appropriate for the studied system, or (2) the user can manually choose a particular scoring scheme. The user can also decide to run only one round of JET2 clustering procedure  or to run the complete procedure where a second round is performed to detect additional patches by using a scoring scheme complementary to the main one (see below). Finally, as described above for JET, JET2 can be run multiple times, in an iterative mode of the program (iJET2), to get more robust predictions.In JETJET, PC and CV, were introduced in JET2 to evaluate single residues in a protein. Conservation levels (TJET) are computed from phylogenetic trees constructed using sequences homologous to the query sequence and sampled by a Gibbs-like approach [N trees are constructed from N representative subsets of sequences. For each position in the query sequence, a tree trace is computed from each tree T: it corresponds to the level n in the tree T where the amino acid at this position appeared and remained conserved thereafter [Three measures, Tapproach . N treesereafter . Let us ereafter , 58.Tree traces are averaged over the N trees to get more statistically significant values, which we denote relative trace significances. The final TJET value of amino acid aj at position j is obtained by accounting for aj’s environment and is expressed as follows [I is the set of residue positions which are neighbors of aj (i.e. with at least one atom distant by less than 5Å to at least one atom of aj) and where dj is the relative trace significance of aj. The weights were fixed at wI = 3 and wj = 4, as in [JET values are scaled between 0 (least conserved residue of the protein) and 1 (most conserved residue of the protein) for the calculation of residue scores. follows :TJET(j)4, as in . TJET vaPhysico-chemical properties (PC) are derived from propensities specific to every amino acid to be located at a protein interface, taken from . The orii is computed as:ni is the number of atoms distant by less than rcÅ from atom i. The CV value of a residue j is then computed as the average of the atomic CVs, over all the atoms of j. A low CV value indicates for a residue that it is located in a protruding region of the protein surface. CV values are scaled between 0 (most protruding residue of the protein) and 1 (least protruding residue of the protein) for the calculation of residue scores.Circular variance (CV) is a measure of the vectorial distribution of a set of neighboring points around a fixed point in 3D space . For a grc directly influences the resolution of the protein surface. Here we chose to use two different values of rc. We set rc = 100Å when using SC3 as the main scoring scheme, to be able to capture the most protruding regions of the protein surface at a global level. Otherwise we set rc = 12Å to obtain a local description of the geometry of the protein surface. Values in the range 10–14Å for rc give similar results.The cutoff distance JET and PC.CV complement (1-CV) was preferred to CV to visualize the results because it permits to better contrast this measure with the two other descriptors,TJET, PC and CV are combined in a very straightforward way in the different scoring schemes, without any specific weight. Each residue score is simply computed as the arithmetic mean of the scaled TJET, PC and/or (1-CV) values  values between 0 and 1 is performed by considering the variability of the values for residues in the query protein. As a consequence, interface predictions are protein-specific.The descriptors T) values . Let us fintfrac(N) = (26.54/N) + 0.03, where N is the number of surface residues [fintfrac(N) is used in JET2 clustering procedure to define two thresholds (fintfrac(N) on the distribution of score values. It is used to select appropriate residues for constructing the clusters. The fintfrac(N)/x, where x values 2 or 4, on the same distribution. It is used to decide when to terminate the cluster seed construction step (x = 4) and the cluster extension step (x = 2). These thresholds are the same as those used in JET and more details on how they are determined and employed can be found in [The expected size of a protein interface is computed as residues . The parresidues  based onresidues . The funresholds . The scofound in .2 algorithm for extending cluster seeds reflects the decreasing gradient of conservation and interface propensities observed in the experimental interfaces from support through core to rim /2 on the distribution of residue scores [JETe to rim . Specifie scores . Followi2 clustering procedure runs in only one iteration and the strategy adopted is quite different from that of the regular extension step (described above). Each cluster is extended toward neighboring residues (distant by < 5Å) that have a high score—combining PC and CV, provided that the inclusion of the considered residue leads to a mean cluster score as high as or greater than before the inclusion  is compared to the expected size of the interface, fintfrac(N). If the protein surface is characterized by evolutionary signals too low to help detect protein interfaces, then the strategy is to look for sites comprised of protruding residues that satisfy expected physico-chemical properties (SC3). Otherwise, SC1 is used to detect conserved seeds. If the search finds no seed with satisfiable size, then the strategy is switched for SC3. Otherwise, if a seed displays a very homogeneous conservation signal, i.e. the ratio of the dispersion to the mean of the conservation score computed over the seed is much smaller (<1/6) than the ratio computed over the whole surface, then a protein-small ligand binding site is suspected and the strategy is to use SC2. Otherwise, the seeds are considered to display good characteristics and SC1 is further employed for their extension and the addition of the outer layer.The user can let JETd system . The imp2 clustering procedure.Alternatively the user can select the scoring scheme of his/her choice. He/She can decide to run only the seed detection step, the seed detection and extension steps, or all three steps of JET2 procedure is run in the automated mode or the manual mode, the user can decide to run only one round of the clustering algorithm  or the complete procedure where a second round of the clustering is performed to detect complementary clusters by using a scoring scheme different from the main one . This is due to the heuristics employed to compute evolutionary traces TJET and to filter out small cluster seeds . To get for JET . In JET22 (iJET2) to evaluate the performance of the tool. We relied on the following quantities: the number of residues correctly predicted as interacting , the number of residues correctly predicted as non-interacting , the number of non-interacting residues incorrectly predicted as interacting  and the number of interacting residues incorrectly predicted as non-interacting . We used the four standard measures of performance: sensitivity Sens = TP/(TP + FN), specificity Spe = TN/(TN + FP), accuracy Acc = (TP + TN)/(TP + FN + TN + FP) and positive predictive value PPV = TP/(TP + FP). We also evaluated the statistical significance of Sens, Spe, PPV and Acc by using scores that account for the expected values of these measures. Expected numbers of true/false positives/negatives are computed as: TPexp = C ⋅ S, TNexp = (1 − C)(N − S), FPexp = C ⋅ (N − S), FNexp = (1 − C) ⋅ S, where C = P/N in the coverage obtained with JET2, P is the number of surface residues predicted by JET2, N is the total number of surface residues and S is the number of residues in the experimental interaction site. Note that the calculation of expected values assumes that C ⋅ N residues have been selected at random as being positives on the structure of the protein under study. This means that expected values depend on the protein studied. The expected sensitivity, specificity, positive predictive value and accuracy are then computed as: Sensexp = C, Speexp = 1 − C, PPVexp = S/N, Accexp = ((1 − C) ⋅ (1 − S/N)) + C ⋅ S/N. Pertinence scores are expressed as: ScSens = Sens − Sensexp, ScSpe = Spe − Speexp, ScPPV = PPV − PPVexp and ScAcc = Acc − Accexp. The R software [We previously showed that consensus predictions obtained by running multiple independent runs of JET were more accurate than predictions from a single run . Consequsoftware  was usedMultiple recognition patches within protein experimental interfaces from the Huang dataset and PPDBv4 were detected by applying hierarchical clustering to interface residues using the average linkage method, as shown in . CalculaPPI webserver [The Multi-VORFFIP webserver  was usedebserver  was usedS1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.S9 Table(PDF)Click here for additional data file.S10 Table(PDF)Click here for additional data file.S11 Table(PDF)Click here for additional data file.S12 Table(PDF)Click here for additional data file.S13 Table(PDF)Click here for additional data file.S14 Table(PDF)Click here for additional data file.S15 Table(PDF)Click here for additional data file.S16 Table(PDF)Click here for additional data file.S17 Table(PDF)Click here for additional data file.S18 Table(PDF)Click here for additional data file.S19 Table(PDF)Click here for additional data file.S1 Fig2 predictive model comprises a seed, and extension and an outer layer. In scoring scheme SC1 taken here as an example, the seed is detected based on conservation levels, the extension is defined from conservation and physico-chemical properties, and the determination of the outer layer accounts for physico-chemical properties and surface geometry. The SCR model [JET predictive model for protein binding sites comprises a cluster seed, detected based on conservation levels or physico-chemical properties, and an extension, detected by using a mixture of conservation and physico-chemical properties. JETCR model  of exper(TIFF)Click here for additional data file.S2 Fig2. Overlaps are computed as: oi,j = #(predi ∩ predj)/#(predi), where predi and predj represent the residues predicted at the interface by iSC and jSC respectively. (a) o1,2 > 50% & o2,1 > 50%, (b) o1,3 < 10% & o3,1 < 10%, (c) o2,3 < 10% & o3,2 < 10%, (d) o1,2 < 10% & o2,1 < 10%, (e) 10%≤o1,3 < 60% & 10%≤o3,1 < 60%, (f) 10%≤o2,3 < 60% & 10%≤o3,2 < 60%, (g) o3,1 > 90% & o1,3 < 80%, (h) o3,2 > 90% & o2,3 < 80%. The proportions of proteins from the Huang dataset (H) and PPDBv4 (P) corresponding to each case are given.The predictions were obtained from a consensus of 2 runs out of 10 of iJET(TIFF)Click here for additional data file.S3 FigA) the homodimers (in green), heterodimers (in blue), transients (in orange) and all proteins (in black) from Huang; (B) the antibodies-antigens (in orange), bound antibodies-antigens (in pink), enzymes-inhibitors (in green), others (in blue) and all proteins (in black) from PPDBv4. Predictions were obtained by a consensus over 10 runs (x-axis).Average sensitivity and precision were computed on ((TIFF)Click here for additional data file.S4 FigSens), positive predictive value (PPV), specificity (Spe) and accuracy (Acc) are plotted for proteins from the Huang dataset (on the left) and PPDBv4 (on the right). Huang: 2 proteins for which PredUs calculation failed were excluded; PPDBv4: the proteins used for training PredUs, the multi-chain proteins and the 11 proteins for which PredUs calculation failed were excluded, resulting in a subset of 80 proteins. The numbers of proteins that could not be treated are indicated on the right (“No Pred”). For iJET2, consensus predictions were obtained from 2 (in light green) and 8 (in forest green) runs out of 10. The clustering procedure was run using all three scoring schemes for each protein and the best patch or combination of patches was retained for performance assessment. For PredUs, predicted patches were defined as formed by residues with positive scores (in firebrick).Average sensitivity ((TIFF)Click here for additional data file.S5 FigPPI web server Click here for additional data file.S6 Fig2 for the antibody are depicted as opaque surfaces and colored according to the scoring scheme used: SC1 in orange, SC3 in green.The antibody’s heavy and light chains are represented as marine and pale cyan cartoons while lysozyme is in pink. The patches predicted by JET(TIFF)Click here for additional data file.S7 Fig2SC3 that are specific to the interaction with Efalizumab are depicted as green opaque surface.LFA-1 is displayed as a grey cartoon. The heavy and light chains of Efalizumab are colored in marine and palecyan and displayed as cartoon and transparent surface. The experimental binding site of LFA-1 natural ligand is depicted as opaque grey surface. The five residues detected by iJET(TIFF)Click here for additional data file.S8 Figfintfrac(N) function.The fraction of the surface covered by the interface (y-axis) is plotted versus the number of surface residues (x-axis) for all 352 proteins from PPDBv4. The red line corresponds to the (TIFF)Click here for additional data file.