The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods. Protein–protein interactions are key to understand the molecular level mechanisms of regulation in the cell. However, there is still a limited understanding of what distinguishes a protein-protein binding site from the rest of the surface. This lack of knowledge is manifested in the relatively low accuracy of computational methods that try to predict protein interfaces. In this work we report a new conceptual insight about protein interfaces. Our results suggest that protein interfaces serve as generic binding sites to any ligand. This also means that in the absence of the known binding partner it is still possible to define protein interfaces by extensive docking studies of randomly selected, unrelated ligands, as they have a strong tendency to bind to the cognate binding site. This insight was leveraged in a new binding interface prediction algorithm that alone outperforms state of the art approaches that often combine a variety of features. Homology-based predictions of interfaces rely on the knowledge of known protein complexes to infer the likely binding sites in similar proteins. These methods can be very powerful[ab initio” protein interface predictions a number of studies have attempted to identify distinctive features of interfaces[Specific protein-protein interactions are essential for maintaining a robust phenotype. A deeper understanding of these interactions would allow the identification of cognate ligands and driv powerful, 7, but nterfaces–14 oftennterfaces, residuenterfaces–18, hydrnterfaces, 20, planterfaces, predictnterfaces, electronterfaces, accessiSome studies found that different subtypes of protein interfaces  have distinct sequence features, which can be exploited to predict some of the interface residues from sequence, 23. ForRelevant to the current study are the works that discuss the possible generality of binding site locations, both for small molecule and protein ligands. In the case of the former, it has been observed as early as in the 1980s that small organic molecules, both substrates and non-substrates tend to bind to similar, energetically favored “sticky” sites irrespective of their relevance to the target. These observations were made by experimental studies that soaked target proteins in organic solvents and examined the crystal or NMR3 structurIt was observed in the late 1990s that protein superfolds  have “supersites”. In other words, despite substantial sequence divergence and the evolved distinct functions, the 10–15 superfolds that dominate about half of the structural fold population of the genomes usually Docking programs have been used successfully to predict partner-specific interface residues such as the Atomic Contact Frequency (ACF) or the Rab initio method for binding site prediction that does not require prior knowledge of the cognate ligand. Binding interfaces are determined by the frequency of a receptor residue interacting with a range of unrelated protein ligands in extensive docking simulations. A conceptual insight brought to light by our work is that protein shapes evolved to allow a surprisingly small number of suitable surface patches for interactions that are apparently sampled by a wide range of possible ligands. Alternatively, it may be that a variety of unique residue patterns that evolved for recognizing a specific cognate protein ligand also present an energetically relatively favorable site for non-cognate proteins.In the current work, we explored the generality of the phenomenon of binding supersites. We report the surprising observation that protein-protein interaction sites serve as generic protein binding sites. Protein ligands, irrespective of their relevance to the receptor protein, tend to bind to the cognate protein interface. This behavior does not depend on the docking program used, the range and type of protein ligand probes employed, or more technical conditions such as the size of the binding sites considered. Based on this new observation we introduce a docking-based, We explored the hypothesis of whether protein-protein interaction sites also serve as generic binding sites for a range of non-cognate ligands, and as such, behave similarly to protein-small-molecule-binding sites, 41, 42.We explored the overall phenomenon by docking 13 different ligand probes, six immunoglobulin folds and seven randomly picked small protein folds on a combined target dataset of 241, 44 protWe further broke down results by complex and database type. Performance on the Docking Benchmark and NOX databaseAll these suggest that the observation about protein binding supersites is a generic feature of proteins, with some fluctuation of specific success rates depending on the choice of test database.We also explored how well the cognate ligands bind to and define the annotated functional site of the receptor proteins in comparison to unrelated ligands.  InterestWe further subdivided our results as a function of different ligand probes and ligand sizes, while also exploring two alternative docking programs, ZDOCK and GRAMM, to examine the role that variations in the scoring functions play in detecting supersites. We found little dependence on the type of probe used with either docking program . The difWe compared the actual interface residues predicted by the two docking programs, ZDOCK and GRAMM. Although the entire set of interface residues predicted by the two docking programs were not identical, for 40% and 79% of the 241 proteins in the data set, the two docking programs predicted more than 10 or more than 5 interface residues in common out of 15, respectively. To put these numbers in a statistical context: the expected number of residues that are common out of 15 residues between any two random draws,—in protein sizes 100, 150, 200, 250 and 300 are: 2.27, 1.58, 1.16, 0.82, and 0.81 residues, respectively. Consequently, the two programs have a strong tendency to locate binding sites similarly. The corresponding p-values of observed common residues between ZDOCK and GRAMM are all significant at any protein size.We explored an additional aspect of the potential impact of the employed energy function. ZDOCK ranks the generated docked poses by their energy score, so we explored if there is a difference in performance between the top-scoring and bottom-scoring docked poses. Indeed, this phenomenon can be observed once we plot the performance of the first and last 200 docked poses . There iWhen considering the possible reasons for the existence of protein binding supersites, besides the general energetic preferences of certain “sticky” areas of the protein, one could also consider receptor-shape-driven causes. For instance, one could speculate that in the case of small proteins it might be a geometrical artifact that only a confined area is suitable to accept interactions. However, the distribution of the size of receptors in the current work has a large range (<100 residues to >700 residues) for which the ability to detect supersites appears to be uniformly high .We further dissected the possible differences in performance between the two docking approaches. First, we compared the performance of these techniques using 2000 models generated by the methods, irrespective of the size of the identified binding interface, with the performance when using only a subset of the docked complexes that have the most common interface sizes; in the current work, formed by 9 residues . Though We used 13 different ligand probes and by default 2000 docked conformations to locate the binding site of a receptor protein. This amounts to 13x2000 = 26,000 docked poses. We gradually reduced the number of docked poses and found that with 13 ligands as few as 200 docked conformations are sufficient to establish the same results as before, with 2000 poses .Another aspect of the binding site exploration is the number and variety of probes employed. Upon plotting the performance of all the 13 probes independently, it is clear that these perform in a relatively tight range and that the observed small differences most likely can be acknowledged to the particular set of test proteins used. As an empirical test, the accuracy using ZDOCK changes from 65.4 when averaged over a subset of 6 randomly selected different probes to 63.2 when averaged over all 13 different probes. We found that randomly selecting 2–3 probes already provides robustly the same performance results as running all 13 probes .It has been shown that docking based methods are less successful to predict the correct binding pose and binding site when targeting uncomplexed receptors, especially the ones that undergo substantial conformational change upon binding to their cognate ligand. In our case we do not restrict our analysis to the cognate ligand and to a few (or one) docked poses with the lowest energetics, as such an approach is likely to be insensitive to small conformational changes. Non-cognate ligands bind with much lower affinity, and we are capturing the relative preference of any ligand to dock to the cognate binding site. We manually identified 95 target proteins in our combined set for which we could locate a PDB structure in an uncomplexed form. The F-scores of "apo" and "holo" forms for this subset of 95 target proteins is include in the Supplementary Information . On thisAn important aspect of this study is to explore if the observed phenomenon is a function of fold types, or something more general. The distribution of protein folds is very uneven, with 12In order to understand some of the differences in performances, we examined the specific superfamily classifications of the proteins represented in the two datasets . In the Slightly different interface definitions can drastically change the number of residues involved in the interface. A recent study suggests that even in the case of nearly identical definitions, the disagreement between different definitions can be substantial, suggesting that a ~0.8 F-score as a practical upper limit for prediction methods. In addiIn this work, we have shown that protein binding supersites exist in proteins, i.e. the protein binding interface provides an energetically-preferred binding site for many alternative, non-cognate proteins as well. There were previous, anecdotal studies that noted that even non-cognate ligand have tendency to accumulate around the cognate site, as it was shown in case of chymotrypsin when docked with a non-native binder, lysosyme. Other rTwo different datasets were employed in this study. A set of 108 protein chains from the Docking Benchmark and anotIn our approach we use a total of 13 ligand probes, none of which are known partners or share any detectable sequence similarity to known ligands for the query proteins in our data set. Six of these ligand probes were immunoglobulin folds  = 1; otherwise, I(Rik) = 0. A Residue Interface Frequency (RIF), Ni was determined for each residue at position i in the receptor protein by summing over all the 2000 docked structures.Two different docking programs, ZDOCK and GRAMi values, and the top 15 ranking residues were considered most likely to be at the interface. The residues were also ranked similarly by using a subset of the 2000 complex structures all of which contained exactly nine residues at the interface (the most frequent interface patch size during the simulations). This subset generally consisted of between 150 and 300 complex structures.The residues were then ranked based on the NThe actual number of interface residues varies with each receptor protein. We considered the number of true positive predictions of interface residues in the top 15 rankings assigned by our method. The performance of the current RIF method was evaluated using a statistical significance test by comparing it with a random prediction. The probability of randomly selecting x interface residues in the top K predicted residues (K = 15 in our case) for a query protein chain with N is the total number of residues sampled during the extensive docking simulation and M actual interface residues is given by the probability mass function of the hypergeometric distribution:An interface prediction is considered significant if P(X = x) < 0.05. The performance is expressed asTheoretically, the hypergeometric distribution can be exposed to some instability when very small numbers of discrete residues are assessed for significance; therefore, performance was also evaluated empirically, by randomly sampling 15 residues from the surface exposed residues sampled during the extensive docking simulation of the query protein 200 times and finding the average number of interface residues, μ, and the standard deviation, σ. A Z-score was then calculated, Z = (N–μ)/ σ, where N is the actual number of interface residues in the top 15 using the RIF method. The prediction was considered significant if Z > 1.97. The % significance evaluated using the hypergeometric distribution and the random sampling method yielded identical results.Receiver operating characteristic curves ROC were calculated by plotting the true positive rate (sensitivity) against the false positive rate (1- specificity). Corresponding Area Under the Curve values were obtained.Functional residues are a small fraction of the total residues, so true negatives far outnumber true positives. Therefore methods that heavily reward true negatives, such as the “specificity” and the “accuracy”, are less appropriate than ones that do not, such as the “F-Score” and apprS1 Fig(TIF)Click here for additional data file.S1 Text(TIF)Click here for additional data file.


Crosslinking Mass Spectrometry generates restraints that can be used to model proteins and protein complexes. Previously, we have developed two methods, to help users achieve better modelling performance from their crosslinking restraints: Jwalk, to estimate solvent accessible distances between crosslinked residues and MNXL, to assess the quality of the models based on these distances.Here, we present the Jwalk and MNXL webservers, which streamline the process of validating monomeric protein models using restraints from crosslinks. We demonstrate this by using the MNXL server to filter models made of varying quality, selecting the most native-like.The webserver and source code are freely available from jwalk.ismb.lon.ac.uk and mnxl.ismb.lon.ac.uk.Bioinformatics online. Crosslinking Mass Spectrometry (XL-MS) is an experimental method that can generate sparse structural information on proteins, complementary to traditional structural techniques. Briefly, an XL-MS experiment consists of (a) crosslinking of your target protein (or protein complex); (b) digesting the crosslinked protein and (c) identifying via MS which residues are crosslinked. This results in restraining information that can then be used to model the protein of interest.Jwalk (Matched and Non-accessible Crosslink Score (MNXL), which we found to outperform more conventional methods when scoring protein monomers . The SASDs of the experimental crosslinks are then scored using the MNXL scoring function .Jwalk and MNXL are written in Python 2.7. For a full description of Jwalk see  were made with MODELLER . The fivWe have created webservers for MNXL and Jwalk, two methods that can be used to validate models using restraints from XL-MS and demonstrated how it can be useful in filtering comparative models built from different templates. These webservers are designed to be user-friendly, in order to make it easier for the novice user to make better use of their crosslinking data. We hope to expand these platforms to incorporate the modelling of protein complexes in the near future.Supplementary DataClick here for additional data file.


Then, to infer substructures within complexes, we apply a graph clustering method ) and a gene ontology (GO) semantic similarity based functional clustering on partially- and fully-connected networks, respectively. Computational results show that the proposed framework achieves fairly good performance of cross validation and independent test in terms of detecting direct contacts between subunits. Functional analyses further demonstrate the rationality of partitioning the subunits into substructures via the MMC algorithm and functional clustering.Understanding the physical arrangement of subunits within protein complexes potentially provides valuable clues about how the subunits work together and how the complexes function. The majority of recent research focuses on identifying protein complexes as a whole and seldom studies the inner structures within complexes. In this study, we propose a computational framework to predict direct contacts and substructures within protein complexes. In this framework, we first train a supervised learning model of l Saccharomyces cerevisiae. CORUM [Homo sapiens. Reactome [Homo sapiens. Interested readers can refer to References [Protein complexes have their individual gene products spatiotemporally arranged in place to form the structures required for specific biological activities . Systemae. CORUM  is a pube. CORUM  providesReactome ,7 providferences ,9 for coTo our knowledge, there are only several studies that investigate the inner structure of complexes. For instance, Gavin et al.  propose To gain knowledge about inner substructures of protein complexes, we systematically designed a two-step computational model. The first step was to predict the physical interactions between subunits within complexes, and the second step was to partition the subunit interaction networks into sub-complexes via graph clustering. In the first step, we needed to cautiously restrict the physical interactions within complexes  rather than between genome-scale individual proteins . Both methods ,12 prediThe second step was to infer sub-complexes from the predicted networks of physical subunit interactions within complexes. Drew et al.  calculatIn this study, we propose a computational framework that combined supervised learning and graph clustering to predict physical subunit interactions and infer substructures within human protein complexes. The direct and indirect interactions of training data were restricted within complexes. A graph clustering method, named maximum modularity clustering (MMC) , was useWe first show the flowchart of the proposed framework for easy grasp of the workflow. As illustrated in In the second phase, we conducted clustering on the predicted subunit interaction networks to identify sub-complexes. For partially-connected networks, we used topological clustering via maximum modularity clustering (MMC) ; and forTo learn the patterns of direct and indirect subunit interaction, we restricted the construction of training data within complexes. We first obtained physical protein–protein interactions from HPRD , BioGridWe further mapped the 57,920 physical PPIs to the co-complexed protein pairs from CORUM  and HPRDIn the proposed framework, the negative class refers to indirect subunit interactions within complexes. Besides the 163 indirect interactions from Reactome , we needions see .Two negative independent test sets containing 3326 and 2349 co-complexed indirect interactions were similarly sampled from CORUM , respectGene ontology (GO) has been reported to be the most discriminative feature to depict protein pairs and predict PPIs . Unfortui in the training set U, we obtained the homolog set of GO terms denoted as G is defined as follows.For each protein The feature vectors for target instance and homolog instance for protein pair g of feature vector for the target instance and homolog instance, respectively. The GO terms GO term g, the value of component g in both feature vector is set to 2; if neither protein possesses the GO term g, the value is set to 0; otherwise the value is set to 1. This simple method of feature representation intuitively represents the distribution of GO terms among two proteins without considering the hierarchical and semantic relationship between GO terms. As compared with the method that incorporates the ancestor GO terms in GO directed acyclic graph (DAG) [For a GO term ph (DAG) , this siph (DAG) . Due to 2-regularized logistic regression method [x and labels y that consist of a set of instance-label pairs 2-regularized logistic regression calculates the optimum weight vector Computational complexity and noise tolerance were two major concerns for us to choose the base classifier. Since a regularization technique can counteract the noise from homolog knowledge transfer and logistic regression performs well in fast training of large-scale data, we selected the well-established ln method  that is n method  as the cIn the test or prediction phase, the decision function As illustrated in V and a function f assigns an edge weight to each vertex pair. The degree of vertex v is defined as V into non-empty subsets Assuming that a graph edge set . The modv from its cluster The modularity increase caused by the refining operator that moves a vertex The coarsening and refining operator iterate greedily until no modularity increase , the semantic similarity between GO term GO semantic similarities could be aggregated to define gene similarity via the methods of maximum, average and best-match average . In this2-regularized logistic regression model is evaluated under three experimental settings, namely combined-instance, homolog-instance and target-instance. The combined-instance setting uses both target instances and homolog instances to evaluate the model; the homolog-instance setting uses homolog instances alone to evaluate whether the model is robust against GO unavailability; and the target-instance setting uses target instances to estimate the baseline performance. We adopted five performance metrics, i.e., receiver operating characteristic (ROC) and area under the curve (AUC) (ROC-AUC), precision (PR), specificity (SE), Matthews correlation coefficient (MCC) and F1 score. Among these metrics, PR, SE and MCC were derived from a confusion matrix M, where its element M via Formula (14). The we calculated the class-specific metrics PRl, SEl and MCCl via Formula (15). The overall MCC is calculated via Formula (16).L denotes the number of labels. For binary classification, L is equal to two. The AUC score was calculated on the basis of the decision values calculated via Formula (5). The F1 score is defined as follows:The lP matchec the actual set of sub-complexes C.To measure the performance of graph clustering, we used a Jaccard index to estimate how well the predicted set of sub-complexes P matches C if Given a threshold As mentioned in the subsection “Negative training and independent test data”, the negative data were sampled from two sources: (1) the co-complexed protein pairs that no path existed between them in human physical PPI networks (No-path); and (2) the co-complexed protein pairs connected via paths whose path lengths all were no less than two (No-less-than-two). The sampling ratio As illustrated in The performance measured via precision, sensitivity and MCC on the positive and negative class is provided in N subunits, there are potentially maximum N × (N − 1)/2 physical interactions or edges, i.e., a complete graph. For a complex that is predicted to possess M physical subunit interactions, we define the connection degree as We used the trained model to further predict the physical subunit interactions within complexes from CORUM . After rFor the complexes whose subunits are predicted to be partially connected, we used the maximum modularity clustering method (MMC)  to identWe further conducted functional GO enrichment analyses of the four sub-complexes within the CEN complex. Top five GO terms were provided for each sub-complex. As illustrated in As illustrated in Further GO enrichment analyses of the sub-complexes within ALL-1 supercomplex are illustrated in To our knowledge, there are only two studies on inferring direct contacts and substructures within complexes ,12. BothThe two methods ,12 do noThe two related studies ,12 dividNevertheless, we still compared the maximum modularity clustering method (MMC)  used by om CORUM  and HPRDver, MCL  at most ver, MCL  and HPRDIf the Jaccard index threshold C method  excelledC method  and was 2-regularized logistic regression model. The computational results of cross validation and independent test show that the proposed framework outperformed the related methods in terms of predicting direct subunit contacts within complexes.A fine-grained resolution of direct subunit contacts and inner substructures within complexes is significant to understanding how complexes work. To the best of our knowledge, there are very few computational studies on predicting substructures within complexes to date. The two related studies ,12 use tTopological clustering of directly-contacted subunits requires sophisticated graph clustering techniques to infer the hierarchical and overlap substructures within complexes. In this study, we employed the maximum modularity clustering method (MMC) to infer sub-complexes from the predicted networks of physical subunit interaction. For the fully-connected complexes, we used GO semantic similarity based functional clustering to infer the inner substructures. As compared to the related studies, this proposed framework demonstrated two major advantages. First, the sampling of direct and indirect subunit interactions was restricted within complexes, so that the trained model was more biologically interpretable. Second, we used MMC method and functional clustering method to infer the hierarchical and overlap substructures within partially-connected and fully-connected complexes, respectively. The predicted direct contacts and substructures within complexes potentially provide valuable clues for future biomedical research.


Protein-protein interactions (PPIs) play important roles in biological functions. Studies of the effects of mutants on protein interactions can provide further understanding of PPIs. Currently, many databases collect experimental mutants to assess protein interactions, but most of these databases are old and have not been updated for several years.To address this issue, we manually curated a kinetic and thermodynamic database of mutant protein interactions (dbMPIKT) that is freely accessible at our website. This database contains 5291 mutants in protein interactions collected from previous databases and the literature published within the last three years. Furthermore, some data analysis, such as mutation number, mutation type, protein pair source and network map construction, can be performed online.http://DeepLearner.ahu.edu.cn/web/dbMPIKT/ for use by all, including both academics and non-academics.Our work can promote the study on PPIs, and novel information can be mined from the new database. Our database is available in The online version of this article (10.1186/s12859-018-2493-7) contains supplementary material, which is available to authorized users. Protein-protein interactions (PPIs) play crucial roles in organisms particularly by mediating the majority of biological functions . MutatioTraditionally, hot spots can be determined using biological experiments, such as alanine scanning mutagenesis and alanine shaving . In geneHowever, experimental methods for hot spot identification are time- consuming and labor-intensive. In addition, it is also difficult to measure all potential binding hot spots in a large number of proteins , 15. TheThis work presents a kinetic and thermodynamic database of mutant protein interactions called dbMPIKT. The database consists of data from previous databases about mutant protein interactions, including BID, SKEMPI and AB-Bind, and data extracted from scientific literature published in recent years. The dbMPIKT contains 5291 nonredundant mutants of experimental kinetic and thermodynamics data upon mutation. Our database will facilitate research on hot spot prediction, drug discovery, and other topics.This database consists of two types of data sources. On data source involves existing databases, i.e., SKEMPI, BID, and AB-Bind; the other data source is curated literature. Our curated literature database collected the mutation data of protein interactions from scientific literature within the past three years . As a result, 1017 protein structures were obtained from 682 citations in PDB, which were mapped to the PDB-Bind database to extract the corresponding thermodynamic data. A total of 99 complex structures from 85 citations containing dissociation constant  information were obtained. All of the literature was manually assessed, and all Kd values of the structures were recorded [Then, the structures of protein complexes were obtained by advanced searches of the PDB database using various query items, i.e., macromolecule type (only contains protein), protein stoichiometry (heterodimer complexes), release date (from1 January 2013 to 31 December 2016) and X-ray resolution   was embeAccording to the network map, some features of PPI network, such as the regularity of PPIs, can be obtained by analyzing the association of PPIs and network structure.Kd), includes the association rate (Kon), and dissociation rate (Koff). Most data are presented in units of nM, M− 1S− 1 and S− 1. Other units can be converted into these units. Moreover, thermodynamic data contain changes in binding free energy (∆G) and differences in binding free energy changes between the mutant and wild-type complex (∆∆G). These values are reported as kcal/mol. PubMed ID is another attribute. This ID is the source of kinetic and thermodynamic data. In addition, you can refer to more details by clicking on each PubMed ID in the table and download literature from NCBI. The last attribute is Method, which presents the experimental measurement method of the affinity of PPIs. There are mainly two methods: SPR (surface plasmon resonance) and ITC  [In this paper, although data entries in dbMPIKT were obtained from different sources, the database contains distinguished attributes. The first feature is the PDB ID, which denotes the ID of the protein-protein complex in the PDB database. This ID is linked to a related PDB website, so users can obtain more information on the complex. The second attribute is mutation information, which consists of original residue, chain identifier, the position of the mutant residue in sequence and the name of mutant residue. The third attribute includes the names of the two interacting proteins, namely protein 1 and protein 2. Additional attributes in- clude kinetic data and thermodynamic data. In general, kinetic data (imetric) . TemperaThe dbMPIKT database collected 5291 mutants with kinetic or thermodynamic data. The data were divided into four sources: SKEMPI, AB-Bind, BID and literature, containing 3046, 815, 256 and 1174 mutants, respectively. The mutants are derived from 233 structures of 245 protein-protein complexes, and only 12 complexes do not have PDB IDs. Some statistical information of dbMPIKT can be found on our website, where the comparison of the four databases with respect to mutation type is presented. The mutations in each database are clustered into three mutation types: single mutants, double mutants and multiple mutants. The data distribution from different sources is presented in Fig. Specifically, the collected thermodynamic and kinetic data are measured by G, change in enthalpy (H), change in entropy (S), and kinetic rate constants. These values are derived from SPR, ITC and alanine mutation scanning (AMS) experiments.The database contains almost all experimental mutants to date. For single mutations, we counted the number of mutations for each type of amino acids. Table Mus musculus, and Bos taurus proteins are included in the database, and human proteins represent the largest group. A protein interaction network was constructed based on protein interaction pairs, which can be used to identify protein functions for specific protein interactions [Bos taurus proteins. BPTI plays an important role in biomedical science given that it can be used to study the conformations and PPIs of globular proteins reduce hemorrhagic complications in clinical practice [In our database, 5291 mutants were obtained from 245 protein-protein complexes, including heterodimer complexes, antigen-antibodies, and enzyme-inhibitors [ractions . Figure practice . Furtherpractice .Fig. 4PaThe dbMPIKT consists of data from four data sources, which all include kinetic or thermodynamic data of mutant protein interactions. However, these data are somewhat different. The SKEMPI database contains the largest number of mutants, and the manually curated database is the second largest source. The BID is the least represented source given that the BID database is not currently operational and data cannot be downloaded directly. Some BID data are extracted from the additional studies in the literature . In addiProtein-protein interactions have been extensively studied, and many researchers have proposed calculation methods for PPI predictions. Among them, disease– related PPIs deserve in-depth study . Our datKd, ∆∆G, ∆G, Koff and Kon, are obtained. In addition, some data can be calculated using other data. For example, ∆∆G, a parameter that can be used to diametrically distinguish hot spots and nonhot spots, can be indirectly obtained using the following equation:The paper proposes to integrate the three previous databases and manually curated data presented in the literature over the last three years. We built a web server to store kinetic and thermodynamic data on mutant protein interactions. More detailed information about mutants and protein-protein interactions can be found on the web server. In our database, kinetic and thermodynamic data of mutants, including The database provides a large hot spot data set that can help improve the applications of hot spots and hot spot predictions.http://DeepLearner.ahu.edu.cn/web/dbMPIKT/. Users can perform advanced searches on the home page to obtain interesting data and browse all data on the browse page.Our free website is available at Additional file 1:Table S1. Literature list for the collected data. (XLSX 60 kb)Additional file 2:Table S2. The collection of protein complexes and their sources. (XLSX 38 kb)Additional file 3:Table S3. Distribution of mutation types. (XLSX 10 kb)Additional file 4:Figure S4. Mutation distribution of amino acid types. (PNG 22 kb)Additional file 5:Figure S5. Protein interaction network map. (PNG 71 kb)


Databases of literature-curated protein-protein interactions (PPIs) are often used to interpret high-throughput interactome mapping studies and estimate error rates. These databases combine interactions across thousands of published studies and experimental techniques. Because the tendency for two proteins to interact depends on the local conditions, this heterogeneity of conditions means that only a subset of database PPIs are interacting during any given experiment. A typical use of these databases as gold standards in interactome mapping projects, however, assumes that PPIs included in the database are indeed interacting under the experimental conditions of the study.Using raw data from 20 co-fractionation experiments and six published interactomes, we demonstrate that this assumption is often false, with up to 55% of purported gold standard interactions showing no evidence of interaction, on average. We identify a subset of CORUM database complexes that do show consistent evidence of interaction in co-fractionation studies, and we use this subset as gold standards to dramatically improve interactome mapping as judged by the number of predicted interactions at a given error rate.We recommend using this CORUM subset as the gold standard set in future co-fractionation studies. More generally, we recommend using the subset of literature-curated PPIs that are specific to the experimental context whenever possible.The online version of this article (10.1186/s12864-018-5139-2) contains supplementary material, which is available to authorized users. Proteins perform the majority of cellular functions necessary for life. Nearly all individual proteins are modular components of larger macromolecular structures, i.e. protein complexes, and the exact role of a protein within a cell is controlled by its interactions with co-complex members. Uncovering which proteins interact, i.e. the interactome, is therefore central to understanding the molecular mechanisms of life.This task is complicated by a combinatorial explosion, however: a proteome containing 20000 proteins has nearly 200 million potential pairwise interactions and many more higher order complexes. High-throughput techniques that analyze thousands of proteins simultaneously with minimal bias offer a solution to this problem . For exaGold standard databases are assembled from different experiments and techniques, each with a unique set of biases. Since protein-protein interactions (PPIs) can be context-specific and transient, single datasets, which are typically generated by a single technique, can disagree with gold standards. This variability partly reflects true biological differences. For example, the majority of in vivo yeast PPIs were observed to depend on environmental and chemical conditions . Some asTherefore, a distinction should be made between the large, curated compilations of interactions across many studies, and the gold standard sets used as a reference for a single dataset. Our own focus has been on interactome mapping using co-fractionation, so here we quantify the proportion of gold standard interactions that fail to display any evidence for interaction in 20 co-fractionation datasets. Using a conservative measure of protein interaction, we find that between 19 and 55% of gold standard PPIs display no evidence of interacting. Across co-fractionation experiments, there is evidence that a subset of literature-curated complexes consistently co-fractionates, suggesting this subset would be a more appropriate gold standard reference set. Indeed, the number of predicted interactions at a given stringency increases dramatically when using this subset as a gold standard set. We recommend using this subset as the gold standard reference in future co-fractionation studies and, more generally, using experiment- and condition-specific gold standards whenever possible.Using the CORUM database of protein complexes , we firsMore broadly, across 20 PCP-SILAC co-fractionation datasets, the majority of random protein pairs do not co-fractionate, as quantified by anti-correlated fractionation profiles, a conservative measure of which protein pairs are non-interacting Fig. . While tWhile the full set of CORUM complexes is a widely used gold standard , 7, 9–11Since some PPI databases include evidence codes, we also analyzed whether the proportion of anti-correlated protein pairs differed as a function of evidence code . Similarly, 61 gold standard complexes are predicted in at least 2/3 Y2H interactomes, while being predicted in at most one co-fractionation or AP-MS interactome (p < 0.001). Only 22 AP-MS-specific complexes are selected in this way (p = 0.49) possibly due to the low CORUM coverage of interactome AP3 [Other high-throughput techniques display consistent over- and under-enrichment of specific gold standard complexes. Figure tome AP3 . TechniqIn addition to being truly non-interacting, the absence of some gold standard complexes from published interactomes Fig.  could reWe note that the context-specific nature of gold standard complexes is not limited to the type of high-throughput experiment . For example, using co-fractionation data where proteins were fractionated using a variety of techniques [p-value thresholds  produced four subsets of CORUM complexes that contain 302, 122, 95, and 80 complexes, respectively . This agrees with an orthogonal analysis of a mouse co-fractionation dataset collected by our group, which analyzed protein co-fractionation across seven tissue types [p < 0.05, permutation test; Fig. p = 7.6 × 10− 8, hypergeometric test; overlapping complexes marked by asterisk * in Fig. p < 10− 2 co-fractionation-specific CORUM subset vs the subset of CORUM with observable proteins in co-fractionation studies (p < 101 subset) . Consistent with the two previous analyses, this enrichment analysis confirms that the co-fractionation-specific subset of CORUM is enriched for housekeeping processes such as translation and transcription , and 23% (34442/149477) show no evidence of interaction (R < 0)  of gold standard protein pairs display at most weak evidence for interaction (0) Figs. , 2. OtheSince CORUM is manually curated from low-throughput experiments, we do not interpret these anti-correlated pairs as errors in the database. Rather, we attribute any discrepancy between our raw data and the databases to the context-specific nature of protein interactions and the fact that databases compile evidence from many different experiments and conditions. Indeed, under certain conditions, 60S ribosomal proteins, which have been extensively studied and shown to interact, display poor evidence of interaction . For databases such as CORUM that list complexes rather than pairwise interactions, gold standard interactions were defined as all protein pairs that are co-members of at least one gold standard complex. Only non-redundant, i.e. unique protein interactions were analyzed.We primarily used CORUM core complexes as an LC database of known protein complexes  . CORUM iSalmonella enterica infection [The majority of co-fractionation data analyzed in this study was collected by our group and constitutes a broad sampling of SILAC-labelled co-fractionation datasets. Data was collected for four independent experiments, each mapping interactome rearrangements to an experimental treatment. Datasets in this study were separated by condition and replicate, such that an experiment with two conditions and three replicates would yield six datasets analyzed here. A total of 20 datasets are included in this study. Three experiments are previously published: two that map the reorganization of the HeLa interactome in response to stimulation with EGF  and Salmnfection , and onenfection . All franfection  which usIn addition to raw co-fractionation data, we analyzed twelve published human protein interactomes: three derived from co-fractionation data published by our lab , three Raw co-fractionation profiles: To evaluate the degree to which gold standard PPIs are supported by co-fractionation data, we calculated the Pearson correlation coefficient and Euclidean distance between each pair of chromatograms in a dataset. For both measures, missing values in the chromatograms were replaced by zeros. When calculating Euclidean distance, all chromatograms were normalized to have a minimum value of 0 and a maximum value of 1. High correlation or low Euclidean distance was taken as evidence that the gold standard interaction was interacting in the sample.Published interactomes: We mapped published pairwise protein-protein interactions to gold standard CORUM complexes. For each published interactome, we tested whether gold standard complexes were enriched for published interactions, meaning they contained significantly more pairwise interactions between complex members than the average rate (hypergeometric test). We took significant enrichment as evidence that the published interactome supported the gold standard complex. To standardize interactomes with each other and CORUM, all isoform tags were removed from protein IDs.http://metazoa.med.utoronto.ca/; [To control for expression and different baits, we also defined the subset of gold standard complexes in each study in which at least one interaction could be predicted. For CF interactomes this was defined as a complex in which at least two co-complex members are present in the raw data  in each subset were 302 (33378), 122 (10953), 95 (6326) and 80 (4861), respectively.In this study, we used subsets of the gold standard complexes that are consistently supported by co-fractionation interactomes. For these co-fractionation subsets, we used all CORUM complexes that were significantly enriched for interactions (hypergeometric test) in CF4, CF5, and CF6. Significance was assessed at a range of To control for the effects of simply reducing the size of the gold standards, we generated random subsets of gold standard PPIs with the same size as the selected subsets. Of the full 46413 unique. PPIs in the core CORUM complexes, we randomly sampled 33378, 10953, 6326, and 4861 PPIs.without replacement. Random sampling was repeated 100 times for each of the subset sizes, and interactomes were predicted using each random subset with the PrInCE software package.For this study, interactomes were predicted using the PrInCE software (Predicting Interactomes via Co-Elution), a software package developed by our lab for the analysis of co-fractionation datasets . PrInCE Additional file 1:Figure S1. Restricting gold standard PPIs to those supported by two or more publications does not eliminate uncorrelated protein pairs, as measured by Pearson correlation R < 0. Each point is one dataset. Horizontal lines show medians. Red: all non-gold standard protein pairs. Black: non-redundant gold standard pairs. “All pairs” and “BioGrid” correspond to Fig. Figure S2. Co-fractionation datasets are more consistent with database PPIs with “co-fractionation-like” evidence codes, e.g. “density sedimentation” and “molecular sieving”. Top: Percent fraction of anti-correlated pairs across co-fractionation datasets, sorted by average percent. Evidence codes are given on the x-axis. Bottom: Average Pearson correlation co-efficient. This analysis is similar to Fig. Table S1. Some CORUM complexes are predicted by a single high-throughput technique, as measured by average complex coverage. Complex coverage = number of pairwise interactions in a published interactome / total pairwise connections within a complex. Parentheses show number of complexes averaged. CF-specific complexes correspond to numbers 1–80 in Fig. Figure S3. 60S ribosome co-fractionates via sucrose fractionation (A) but not via heparin dual ion exchange (B). Pearson R. Plots show replicates. Missing (black) are protein pairs where neither protein was detected. (DOCX 561 kb)Additional file 2:Table S4. Subset of CORUM core complexes that consistently co-fractionate (Feb 2017 CORUM release). Complexes were chosen if they were significantly enriched for pairwise interactions in three published co-fractionation interactomes . Enrichment was calculated with a hypergeometric test, and significance was evaluated at four thresholds: p < 1, p < 1e-2, p < 1e-6, and p < 1e-10. All data aside from columns “p < 1”, “p < 1e-2”, “p < 1e-6”, and “p < 1e-10” are taken from the CORUM core data file. (XLSX 202 kb)Additional file 3:Table S2. Gene Ontology (GO) enrichment of proteins in co-fractionation-specific subset of CORUM. Hypergeometric test. “Hits” are proteins from the co-fractionation-specific subset (p < 10–2) and “background”/“universal” are all proteins from CORUM complexes that were detected, but not necessarily co-fractionating (p < 1) (see Methods for CORUM subset definition). (XLSX 43 kb)Additional file 4Table S3. Third co-fractionation dataset. (CSV 782 kb)


Protein complex identification from protein-protein interaction (PPI) networks is crucial for understanding cellular organization principles and functional mechanisms. In recent decades, numerous computational methods have been proposed to identify protein complexes. However, most of the current state-of-the-art studies still have some challenges to resolve, including their high false-positives rates, incapability of identifying overlapping complexes, lack of consideration for the inherent organization within protein complexes, and absence of some biological attachment proteins.p-value. Furthermore, EWCA could identify many more protein complexes with statistical significance. Additionally, EWCA could have better balance accuracy and efficiency than some state-of-the-art methods with high accuracy.In this paper, to overcome these limitations, we present a protein complex identification method based on an edge weight method and core-attachment structure (EWCA) which consists of a complex core and some sparse attachment proteins. First, we propose a new weighting method to assess the reliability of interactions. Second, we identify protein complex cores by using the structural similarity between a seed and its direct neighbors. Third, we introduce a new method to detect attachment proteins that is able to distinguish and identify peripheral proteins and overlapping proteins. Finally, we bind attachment proteins to their corresponding complex cores to form protein complexes and discard redundant protein complexes. The experimental results indicate that EWCA outperforms existing state-of-the-art methods in terms of both accuracy and https://github.com/RongquanWang/EWCA.In summary, EWCA has better performance for protein complex identification by a comprehensive comparison with twelve algorithms in terms of different evaluation metrics. The datasets and software are freely available for academic research at  A significant task of system biology is to explore cellular function and organization by studying the PPI networks. Most of the functional processes within a cell are executed by protein complexes . TherefoSo far, some biologically experimental methods have been proposed to detect protein complexes from the PPI networks. However, these methods require high level of expensive cost and time-consuming. Thus, many efficient alternative computational methods are proposed to identify protein complexes in PPI networks. Moreover, a number of high-quality and large-scale PPI networks provide possible for computational methods to identify protein complexes. Generally, a PPI network can be modeled as an undirected graph , where vertices represent proteins and edges represent interactions between proteins. Various state-of-the-art computational methods have been developed to identify protein complexes in the last few years. According to the use of information in identifying process, these computational methods are classified into two categories. One category only uses the topological information of PPI networks to identify protein complexes, and we call them topology-based methods. The other category is to combine the biological and topological information to identify protein complexes, such as IPC-BSS , GMFTP  and DPC A large amount of topology-based methods have been proposed to identify protein complexes by employing different topological structures. For instance, CFinder  and CMC As we all known, a clique is a complete subgraph and its all vertices are connected to each other. Some researchers believe that cliques or k-cliques are protein complexes. For example, CFinder  is basedyeast protein complexes which are provided by Pu et al. in [Consequently, some new computational methods have been proposed to identify overlapping protein complexes. For example, DPClus  is a seet al. in , 21% comt al. in  such as t al. in  and ProRt al. in . All of Furthermore, some researchers find that many protein complexes have modularity structure, which means these protein complexes are densely connected within themselves but sparsely connected with the rest of the PPI networks , 26–28. Recently, some research results have shown that the characteristics of detected protein complexes indicate that protein complexes generally have a core-attachment structure –34. GaviUp to now, several methods based on core-attachment structure have been explored for identifying protein complexes, such as COACH , Core 1 and Ma eIn this paper, we try to overcome these limitations and employ a protein complex internal structure to identify biologically and accurately meaningful protein complexes. Inspired by some reserachers’s , 36–38 eGppi=, where Vppi represents as the set of vertices corresponding to proteins and Eppi stands for the set of edges which represent the interactions between proteins from Vppi. A PPI network is undirected and may be unweighted or weighted, with weight on an edge representing the confidence score  for an interaction. For a vertex v, N(v) stands for the set of all vertex v’neighbors.Generally, a PPI network can be typically modeled as an undirected graph v and u is defined by Eq.  . Hence wd by Eq. : 1\docuN(v) and N(u) stand for the set of neighbor nodes of nodes v and u, respectively. N(v)∩N(u) is the set of all common neighbors between nodes v and u, and is denoted by CN. |N(v)∩N(u)| stands for the number of all common neighbors of v and u. |N(v)∪N(u)| represents the union set of all distinct neighbors of v and u. Obviously, the more common neighbors two proteins share, the higher similarity between two adjacent nodes. Here, to better quantify the connectivity between two adjacent nodes v and u, then we define a new high-order common neighbor (HOCN) similarity measure based on the Jaccard’s coefficient between node v and node u, and we introduce HOCN as follows. The main idea is to estimate each edge according to the common neighbors of the common neighbors of the two adjacent nodes. HOCN is defined as Eq. . All common neighbors support (CNS) the edge  are calculated by Eq.  is determined by Eq.  between protein d by Eq. . Finallyd by Eq. .e1 between b and d. According to Eq. ∗JCS + e1 between proteins d and b is To assess the reliability of protein interactions process, we give an example as shown in Fig. g to Eq. , the comentclass1pt{minimaHOCN to calculate the weight of each pair of edge  so that EWCA improves the quality of the identified protein complexes. Obviously, HOCN considers more widely about the connectivity of the entire neighborhood of two adjacent nodes and may well determine whether two interactional proteins belong to the same protein complex. If HOCN is the weight of edge . Otherwise, edge  is considered unreliable and it has to be discarded. The more details pseudo-codes of this phase is shown in Algorithm 1.Here, we use According to the latest research , 47–50, v∈Vppi,N(v) stands for the set of all direct neighbors. Thus, the structural neighborhood of v is defined by Eq. (As mentioned in “d by Eq. : 4\docuSN(v) contains the node v and its immediate neighbors.where SS can be calculated by using the number of common neighbors which are normalized by the geometric mean of the neighbourhood size of vertex v and w. Therefore, the structural similarity SS between two neighbor proteins v and u is defined by Eq. =SS. Obviously, the value of structural similarity is between . Core(PCv) consists of seed vertex v as the center and neighbors that should have high significance structural similarity with seed v. In addition, some biological experiments analyses, such as three-dimensional structure and yeast two-hybrid, have showed that the core proteins (vertices) in the same complex core are likely to be in direct physical contact with each other [u∈N(v), if the value of structural similarity between it and seed v is larger than a prefixed threshold , we select protein u as a core protein. The detail of this prefixed threshold selection will be introduced in Core(PCv) of an identified complex PCv is defined as the subgraph which is made of all the core proteins and their corresponding edges.Based on these statements, we mine a subgraph in the neighborhood graph ch other , 52. TheIf the subgraph is small dense and reliable, its core proteins within the same protein complex core have relatively more interactions among themselves.The core proteins in the same complex core are likely to be directly physical contact with each other.The core proteins in the same complex core should have relatively more common neighbors than other non-core proteins.According to some relevant analysis results , 36, 51,Core(PCv). It should satisfy the following three conditions. The size of the preliminary complex core is larger than 2 and consists of core proteins, where all its core proteins directly connect with each other.The core proteins of a complex core should have more reliable and heavier weights among themselves.A complex core should have higher functional similarity.The core proteins of a protein complex core could be shared with multiple protein complexes.According to these possible conditions and our studies, we take account of a preliminary complex core, named v∈Vppi is a seed to mine protein complex cores, and we compute SS between v and each adjacent vertex w, when SS is larger than or equal to a user-defined threshold (ss); then we take w as a core vertex to the preliminary complex core Core(PCv). Moreover, vertex w should be included into Core(PCv), because they are connected and share a similar structure. Each preliminary complex core Core(PCv) consists of seed vertex v and core vertices, and the value of SS between seed vertex v and its direct neighbors is larger than or equal to a previously set threshold ss. Finally, we discard some redundancy preliminary complex cores and only retain preliminary complex cores whose size is greater than or equal to 2. The pseudo-code of this phase is shown in algorithm 2.More specifically, we consider that each vertex EWCA is used to detect the protein complex cores in the previous section. Next, we should identify the attachment proteins for each complex core to form the protein complex. The research of Gavin et al.  shows thBased on the concepts of attachment proteins, attachment proteins contain could be grouped into two categories. The first category is peripheral proteins, and its main feature is that they only belong to one protein complex. In other words, they closely connect to the protein complex and belong to the most favored protein complexes. The second category is overlapping proteins, which, in contrast, belong to multiple protein complexes. According to our previous CALM algorithm statistics, the number of overlapping proteins in the known protein complexes  shows thPCv. Here, we use a given a preliminary complex core Core(PCv)= and a candidate attachment subset CAP to construct the identified complex PCv. We need to complete two tasks: one is to set up a subset CAP⊆Vppi in which each protein p∈CAP is a candidate attachment protein for the identified protein complex PCv and the other one is to decide which category each protein in CAP belongs to.Next, let us assume an identified complex, written as p satisfies these conditions, it is selected as a candidate attachment protein, where protein p belongs to the neighbourhood of the preliminary complex core Core(PCv) and CAP. Next, we will discuss how to specifically identify the two categories. First of all, we consider a protein belong to that an overlapping protein should satisfy the following: Overlapping proteins interact directly and closely with the corresponding complex cores.The weighted out-connectivity of the complex core of the overlapping protein is greater than the weighted in-connectivity of the complex core.Overlapping proteins weakly interact with the corresponding complex core relative to the internal interactions within the corresponding complex core.Overlapping proteins are not unique to a protein complex; instead, they may be present in more than one complex.At first, for attachment proteins, we give two basic conditions: (1) attachment proteins should directly interact with the corresponding complex cores. (2) attachment proteins should connect with at least two or more core proteins with its complex core. If a protein p of an identified complex PCv be an overlapping protein in a candidate attachment set CAP, that is, p∈Overlapping(PCv): p with respect to Core(PCv) is greater than or equal to the weighted in-interactions of p with respect to Core(PCv), given by: The weighted out-connectivity of p with respect to Core(PCv) is at least half of the average weighted in-interactions of all core vertices in Core(PCv), given by: The weighted in-interactions of According to these conditions, we let a candidate attachment protein dweight) is the total weight interactions of p with core proteins in Core(PCv), given by weightavg(Core(PCv)) is the average of the weighted interactions of all core proteins within the complex core Core(PCv), calculated by Vcore| is the number of proteins in the Core(PCv) and Core(PCv). If a protein satisfies these conditions, we suppose that it belongs to protein complex PCv at the same time and make it an overlapping protein.Here, CAP, we next obtain a candidate peripheral protein subset, CP(PCv), which is a difference set, given by CAP−Overlapping(PCv). We consider that a peripheral protein should satisfy the following: Peripheral proteins are not overlapping proteins.The weighted in-connectivity of the complex core of the peripheral proteins is greater than the weighted out-connectivity of the complex core.Peripheral proteins closely interact with corresponding complex core relative to the interaction of other non-member proteins with the corresponding complex core.Peripheral proteins only belong to a protein complex.Second, when we have obtained all overlapping proteins from candidate attachment set p of an identified complex PCv be a peripheral protein in a candidate peripheral protein subset CP(PCv), that is, p∈Periphery(PCv): p with respect to Core(PCv) is greater than the weighted out-connectivity of p with respect to Core(PCv) and is written by: weightin)>weightout).The weighted in-interactions of p with respect to Core(PCv) is greater than the average weight of interactions of all all candidate peripheral proteins with Core(PCv) and is given by: The weighted in-interactions of Considering these criteria, we let a candidate attachment protein CP(PCv) with Core(PCv).Here, Core(PCv), that is: Combining the peripheral proteins and overlapping proteins, we form the final set of attachment proteins of protein complex core The more detailed pseudo-codes of this phase is shown in Algorithm 3.PCv), i.e., After we have obtained the set of identified preliminary complex cores and the set of identified candidate attachment protein, we combine a preliminary complex core and its attachment proteins and form the final set of unique complex ) between the identified protein complexes and reference complexes, which is presented as follows [The identified complexes  follows , 65, 67:NA is defined to measure the similarity between identified complexes and reference complexes, and it quantizes the closeness between them. NA is, the more possible two complexes closer are. If NA≥t, then the pi is considered to be matched with rj, where t is a predefined threshold. In this paper, we also set t = 0.2, which is consistent with previous studies [Here, the neighborhood affinity  studies , 65.NA has been defined, we will give the definition of recall, precision and F-measure. We assume that P and R are the set of identified complexes and real reference complexes, respectively. Nmr is the number of reference complexes that match at least an identified complex, i.e. Nmr=|{r|r∈R,∃p∈P, NA≥t}|. Nmp is the number of correct identification complexes that match at least a real protein complex, i.e., Nmp=|{p|p∈P,∃r∈R, NA≥t}|. Recall and precision are defined as follows [After the neighborhood affinity  follows : 9\docuand In general, a larger protein complex has the higher recall, while a smaller protein complex has higher precision. Therefore, the F-measure is defined as the harmonic mean of recall and precision, which The corresponding formulas are shown as follows : 11\docR and the set of identified complexes P, are given the |R|×|P| matrix T is constructed, where each element max{Tij} is the largest number of proteins in common between the ith reference complex and the jth identified complex. The coverage rate is defined as: The coverage rate is use for assessing how many proteins in the reference complexes could be covered by the identified complexes , 71. In Ni is the number of proteins in the ith standard complex.where NA . The MMThe above three kinds of metrics are independent and can work together to evaluate the performance of protein complex identification methods .p-value formulated as Eq. . Here we chose these parameters that can maximize the value of F-measure, because it could fully balance the performance of all methods. Moreover, the comparison results between EWCA and other methods are shown in Tables To demonstate the effectiveness of EWCA in identifying protein complexes, we compare EWCA with twelve existing state-of-the-art protein complex identification algorithms including MCL, CFinder, Core, DPClus, COACH, SPICi, ClusterONE, PEWCC, GMFTP, CMC, ProRank+ and DPC. To be fair for each compared method, we follow the strategy used in , 13, theWhat’s more, EWCA achieves almost the highest F-measure and MMR is also the highest through four combinations of the two PPI datasets and the two reference complexes. Please note that we have removed identified complexes with having two or less proteins, and we do not any supply biological data  in EWCA method and other compared methods. The bold values is the best result in comparison with other methods. In fact, F-measure is the harmonic mean of recall and precision. Obviously, the higher F-measure is better.Table Table In summary, EWCA achieves the better performance on two PPI network, which is competitive or superior to the existing protein complexes identification methods. Especially, EWCA achieves a consistently better F-measure and MMR than the other twelve methods. Tables p-value  [p-value over all possible gene ontology term to represent its functional homogeneity. Besides analyzing the protein complexes identified by EWCA, we also calculate the p-value of protein complexes identified by CMC, PEWCC, GMFTP, COACH, ProRank+ and DPC whose size are greater than or equal to 3, respectively. Selecting the above methods to compare with EWCA is because all of them obtained better performances in two test PPI networks as shown in Tables Since the reference complexes are incomplete, to further validate the effectiveness of EWCA method, we investigate the biological significance of our identified protein complexes. Each identified complex is associated with a d in Eq. ) for genmFinder)  which ismFinder) . Here, fp-value test for CMC, PEWCC, GMFTP, COACH, ProRank+, DPC and EWCA are presented in Table p-value falls within different value ranges are calculated for each algorithm. Most of previous algorithms only take account of the proportion of identified complexes. However, the p-value of protein complexes identified has close relationship with their size [The results of eir size . Therefop-value, those identified protein complexes by EWCA has a higher possibility to be identified as real protein complexes through laboratory experiments in the future.On the DIP dataset, the proportion of significant protein protein complexes identified by EWCA is 90.15 percent, which is about 4 percentage point lower than the best method ProRank+. Meanwhile, the number of identified protein complexes by EWCA is also maximum. Similarly, the number of identified protein complexes by CMC and GMFTP in BioGRID dataset is 1113, 2167, respectively. The number of identified protein complexes by PEWCC and DPC in BioGRID dataset is 676 and 622, respectively. Generally, the smaller the number of identified protein complexes is, the higher the proportion of significant complexes is. In fact, the number of identified protein complexes by CMC, GMFTP and PEWCC is much smaller than EWCA. However, they have the percentage of significant protein complexes is relatively lower than EWCA method. All in all, EWCA has more practical and biological significant than other methods in terms of the number of identified protein complexes and the proportion of identified complexes. According to their p-values provide by EWCA method with different datasets are presented in Table p-values  of protein complexes, Cluster frequency and Gene Ontology term. The third column of Table p-value of identified complexes in Table To further reveal the biological significance of identified complexes, five identified protein complexes with very low p-value less than E-19 are listed in Table Furthermore, we discover many identified protein complexes with cluster frequency of 100%. Here, let’s take 5 examples with ss) and study its effect to identifying protein complexes. For yeast, protein complexes are identified from the two yeast PPI datasets including DIP and BioGRID in Table In this experiment, we introduce an user-defined parameter structural similarity  Core(TM) i7-4790 CPU @ 3.60GHz computer with 12.0 GB memory. For simplicity, we run all the programs with their default parameter. Meanwhile, all reported run times are clock times for running protein complexes identification methods. Furthermore, because the accuracy of protein complexes identification methods is most important. Therefore, we only select these comparison methods with having high accuracy according to Tables m edges and n vertices, EWCA first executes Algorithm 1. For each edge, EWCA computes the weight of the edge. For one vertex, EWCA visits its direct neighbors. Here, we use an adjacency list which is a data structure where each vertex has a list which includes all its neighbor vertices. The cost of neighborhood query is proportional to the number of neighbors, that is, the degree of query vertex. Therefore, the total cost is O(deg(v1)+deg(v2)+deg(vi)+...+deg(vn)), where deg(vi),i=1,2,i,...,n is the degree of vertex vi. If we sum all the vertex degrees in G, we count each edge exactly twice: O(2∗m). Meanwhile, each edge has two vertices. Thus the computation complexity of Algorithm 1 is O(4∗m). Secondly, EWCA executes Algorithm 2, for each vertex, EWCA visits all its neighbors and it is same with Algorithm 1. Thus, the computation complexity of Algorithm 2 is O(2∗m). Thirdly, we executes Algorithm 3. We assume that EWCA obtains that the number of preliminary complex cores is |N(PCC)| according to Algorithm 2. The value of |N(PCC)| must be lower than n. Let us assume that the average degree is k in a given PPI network. Furthermore, the real PPI networks generally have sparser degree distributions and follow a power-law degree distribution [k is generally quite small constant. For each preliminary complex core, during the expansion of a preliminary complex core, we assume that the size of the preliminary complex core pcci is |n(pcci)|. Next, we should obtain a candidate attachment proteins subset |Neighbor(pcci)| from the neighbor of the preliminary complex core pcc. The time complexity of this process is O(|n(pcci)|∗k). After we have a candidate attachment proteins subset |Neighbor(pcci)|, we judge whether each candidate vertex p should be added to the pcc by some conditions given in the attachment protein detection section. The time complexity of this process is O(|Neighbor(pcci)|∗k). As a result, the time complexity of Algorithm 3 is O(|N(PCC)|). In summary, the time complexity of EWCA is We present an analysis of the computation complexity of the algorithm EWCA. Given a graph with ribution . Thus, kIn this paper, for the parameters selection of PEWCC, COACH and ProRank+, we use the default value according to suggestions by their authors. Similarly, because EWCA only has a structural similarity parameter, in order to ensure a fairness, we also use the default 0.4 to obtain experimental results. We run EWCA and previous clustering algorithms which have a higher degree of accuracy according to Tables As we all known, the reliability of existing PPIs has a great effect on the accuracy of protein complex identification methods. According to the literatures , 46, we The density-based methods or the core-attachment structure based methods , 15, 16 Furthermore, EWCA has fewer parameters and provides some definitions to distinguish and identify local overlapping proteins and peripheral proteins.Finally, although Wang et al.  considerCompared to earlier protein complex identification methods, EWCA possesses several advantages that are enumerated below. In this paper, we have proposed a new method to identify protein complexes by identifying complex cores and attachment proteins. Our main contributions are as follows: (1) we define a new high-order topological similarity measure to weight each edge. (2) we further extend the protein complex cores identification methods by using the concept of structural similarity; and (3) we propose a new method to distinguish and identify local overlapping and peripheral proteins. Through the comparative analysis with other methods, the experimental results indicate that the performance of EWCA is more effective and accurate. Furthermore, each method has unique characteristics, and selecting a clustering method suitable for your purpose is important. Additionally, EWCA can balance various assessment measures, which means that EWCA provides more insight for future biological studies.We may be able to conceive these further research directions: The available PPI data are full of noise caused by high false-positive and false-negative rates . To over


Arabidopsis thaliana remain unclear. To provide a starting point for functional annotation of these plant-derived peptides, we performed a large-scale prediction of peptide binding sites on protein surfaces using coarse-grained peptide docking. The docked models were subjected to further atomistic refinement and binding energy calculations. A total of 530 peptide-protein pairs were successfully docked. In cases where a peptide encoded by a TAR is predicted to bind at a known ligand or cofactor-binding site within the protein, it can be assumed that the peptide modulates the ligand or cofactor-binding. Moreover, we predict that several peptides bind at protein-protein interfaces, which could therefore regulate the formation of the respective complexes. Protein-peptide binding analysis further revealed that peptides employ both their backbone and side chain atoms when binding to the protein, forming predominantly hydrophobic interactions and hydrogen bonds. In this study, we have generated novel predictions on the potential protein-peptide interactions in A. thaliana, which will help in further experimental validation.Several recent studies indicate that small Open Reading Frames (sORFs) embedded within multiple eukaryotic non-coding RNAs can be translated into bioactive peptides of up to 100 amino acids in size. However, the functional roles of the 607 Stress Induced Peptides (SIPs) previously identified from 189 Transcriptionally Active Regions (TARs) in  For instance, the phytohormone auxin was shown to be involved in bidirectional polar transport across tissues, controlling plant growth-related processes ,2. Furth protein ,5. Most  protein . Some pe protein –12. Seveogenesis –15.Botrytis cinerea) and abiotic stress (Paraquat) in Arabidopsis thaliana. These TARs could be translated into Stress-Induced Peptides (SIPs), which can be specifically categorized depending on the applied stress condition into Botrytis cinerea Induced Peptides (BIPs) and Oxidative Stress Induced Peptides (OSIPs), catalogued in a database ARA-PEPs [In our previous work, we have identified several Transcriptionally Active Regions (TARs) induced upon the application of biotic  [A. thaliana proteins using PepSite2 [We generated a peptide fragment library consisting of 23,113 k-mers ranging in size from 4 to 10 amino acids, using sequences of SIPs ,17 and fcsb.org) . 996 strPepSite2 ,36. ScrePepSite2  and signPepSite2 .We used the default settings of BLASTP2.2.28+ algorithm  with E-vab initio docking protocol pepATTRACT [We shortlisted 576 protein-peptide pairs from PepSite2 output to build atomistic models and perform docking studies, using the protein-peptide coarse-grained pATTRACT . For eacpATTRACT . The bacpATTRACT .In the current study, the rigid body docking models were ranked by ATTRACT score, and the top-ranked 100 structures were subjected to atomistic refinement using the flexible interface refinement method iATTRACT. We used the distance restraint based local docking protocol of pepATTRACT to restrict the sampling during rigid body sampling stage and flexible refinement stage towards the PepSite2 predicted interface residues. The placement of peptide and protein was optimized during iATTRACT refinement. At this stage, the interface region of the peptide and the protein were treated as fully flexible, while simultaneously optimizing the center of mass position and orientation of the peptide.pdb4amber from Amber16 [pdb4amber were retained in the system. Detected protein gaps were treated by addition of N-methyl group (NME) to the carbon of the backbone amide group of the C-terminal, and acetyl group (ACE) to the backbone nitrogen of the N-terminal amino acid, using PyMol Molecular Graphics System, Version v1.7.4.4, Schrodinger, LLC [We used  Amber16  to make ger, LLC . CappingteLeap from Amber16. Counter-charged ions (Na+ or Cl-) were added to the non-neutral systems, and each protein-peptide complex was surrounded by a rectangular box of explicit TIP3P water spanning 10 Å from the system. Force field ff14SB was used for parametrization of proteins and peptides, and tip3p for parametrization of water. Joung/Cheatham parameters were employed for monovalent ions in the chosen water type.Parametrization of the systems was done using sander from Amber16. First 1,000 steps of each cycle were performed by steepest descent method, while conjugated gradient was used for the remaining steps. In first three cycles, the constraint was applied to 1. the entire protein, 2. heavy protein:peptide atoms, and 3. backbone atoms, using force constant 100 kcal mol-1 Å-2. Constraint on backbone atoms was reduced to 50 kcal mol-1 Å-2 in the fourth cycle, and no constraints were applied in the fifth.Systems were optimized in 25,000 steps divided in five cycles, using pmemd from Amber16 package. In the first 300 ps, the canonical NVT ensemble was simulated, with constraint applied to atoms in the protein:peptide complex using the force constant 25 kcal mol-1 Å-2. Temperature was increasing from 0 to 300 K during the first 250 ps. In the last 200 ps of equilibration, isothermal-isobaric ensemble NpT was simulated, with temperature held constant at 300 K and pressure at 1.0 bar, with no constraints applied to the system. Throughout equilibration, the SHAKE algorithm was used to apply constraints on bonds containing hydrogen atoms, and time step of 2 fs was used. The cutoff distance for non-bonded interactions was set to 15 Å, and the neighbor list was updated each 20 steps.After optimization, each system was equilibrated during the initial 500 ps, using https://github.com/ParmEd/ParmEd). Constraint on bonds that contain hydrogen atoms was applied using LINCS algorithm, the time step was 2 fs, and the coordinates were written each picosecond. The temperature and pressure were kept at 300 K and 1.0 bar using modified Berendsen thermostat for temperature, and Parrinello-Rahman barostat for pressure coupling. Particle mesh Ewald method was used for electrostatic interactions, the cutoff distance for non-bonded interactions was 12 Å, and the neighbor list was updated each 20 steps. Periodic boundary conditions were applied throughout equilibration and production phase.Production phase was done as a 4.5 ns continuation of the 500 ps long equilibration, using Gromacs 5 software –54. ConvThe obtained trajectories were visualized by Visual Molecular Dynamics VMD program , and tooMolecular Mechanics energies with Generalized Born and Surface Area continuum solvation (MM/GBSA) method was used to calculate the Gibbs energy of protein:peptide binding in the 104 top docking models. The binding energy is calculated as the following average: omitted , as is iMMPBSA.py.MPI was used here to calculate ΔGbind for protein:peptide systems by MM/GBSA method, using a single trajectory of the complex. The topology files of dry complexes, as well as ligand (peptide) and receptor (protein), were prepared with Amber ante-MMPBSA.py. The Gibbs energy terms in -3. During the MM/GBSA calculations, per-residue binding energy decomposition was also performed in order to get insight into contributions of specific protein and peptide residues to binding.Amber The output files of the MM/GBSA per-residue energy decomposition were used to analyze the characteristics of protein:peptide binding. In each of the 104 systems, the residue with the largest contribution to binding, either in stabilizing or destabilizing manner, was detected. The threshold was then set to 40% of its binding energy contribution value, and all residues that contributed more than the threshold in a given system were taken for the analysis, with taking into account whether amino acid belongs to protein or peptide. The number of appearances of individual amino acid was then calculated, as well as average binding contribution of different amino acids, separately for proteins and peptides, using Python.We scanned SIPs for hydrophobicity using the grand average of hydropathy (GRAVY) number, which is a measure of the hydrophobicity/hydrophilicity of a protein based on Kyte and Doolittle equation. The hydropathy values range from -2 to +2 for most proteins, with the positively ranked proteins being more hydrophobic.www.ebi.ac.uk/pdbe/docs/sifts/index.html). The list of proteins was used to query REACTOME_Pathways and GO_BiologicalProcess ontology and the type of evidence set was All_experimental. Pathways with p-values ≤ 0.05 were displayed, the minimum GO tree interval was set as 3 and the maximum level was set as 8, the GO term/pathway selection was set as a threshold of 4% of genes per pathway and the kappa score was set as 0.4.Gene ontology (GO) analysis was performed using the ClueGo Cytoscape plugin . Lists oA and B, was calculated according to the Euclidean distance equation We manually inspected the top 10 models for each docked protein-peptide pair predicted by pepATTRACT using molecular visualization softwares UCSF Chimera  and PyMOProtein-peptide bindings were characterized using BINding ANAlyzer (BINANA) , HBPLUS Botrytis cinerea, which could be translated into 607 SIPs [A. thaliana proteins from the PDB repository, using PepSite2. 12,540,140 protein-peptide pairs were screened, and multiple testing correction was performed using Benjamini-Hochberg correction method [p-values with FDR set as 0.25. We have used a fairly high FDR of 25% because our study is an initial exploratory step to identify potential protein-peptide bindings and using a rather relaxed FDR would help us to not leave out any interesting candidates. We additionally screened for short peptide motif matches on A. thaliana proteins using BLASTP with default E-value ≤ 10 and found 302 matches with sequence identity ranging from 20% to 100%. The reasoning for finding peptides that show sequence similarity with a protein chain is that, an entire or a part of a SIP may mimic a specific binding motif on the protein, or resemble a loop from a large structured protein, a disordered region in protein termini or interfaces between defined domains and modulate the protein’s activity [https://www.biw.kuleuven.be/CSB/ARA-PEPs/SIP_PDB_interactions.php)  . In our ons.php) . Our resSpecific inhibitors may mimic portions of protein interfaces and can bind to a peptide binding pocket located at the interface between two monomers. In our study we found that 15 peptides bind at the interface between subunits of protein complexes around 6.0 Å apart, indicating the ability to modulate complex’s activity . Among tWhile we observe that both pepATTRACT-local and pepATTRACT-blind produce similar results for 56% of the docked pairs, the other 44% of the pairs were not docked at the same site of the protein using the two methods . These rIn addition, we investigated if protein pockets could bind multiple peptides, or in other words, whether a peptide prefers to bind to one specific pocket on a protein. To test this, we generated a randomly shuffled list of peptides while keeping the list of PDB structures intact, followed by scanning for binding sites using PepSite2. Among the protein structures that did bind a peptide, in 95.15% of the cases, peptides bound to the same protein pocket while in 4.85% cases peptides bound to a different pocket on the same protein . It is pA. thaliana protein chains from the initial screening to Uniprot IDs using annotations from SIFTS database [A. thaliana proteins with significant scores revealed that they may be categorized into 5 main groups viz. defense response, cellular response to organic cyclic compound, organonitrogen compound metabolic process, regulation of stomatal movement and cellular response to chemical stimulus  and 5,10-methenyltetrahydrofolate (MTHF), out of which the first one is necessary for catalytic activity. According to UniProt database , amino aA. thaliana may exert their function through protein-peptide interactions, by binding on protein surfaces. We found 30 peptides that may bind at known ligand/cofactor binding sites on proteins. The identification of ligand/cofactor binding sites in protein structures can aid in determination of peptide ligand types and experimental validation of the function of the receptor [Steroids, peptides and other small bioactive compounds mainly regulate cellular communication in eukaryotes, including plants. Over the last decade, an increasing number of secreted peptides have been shown to influence a variety of developmental processes in plants, such as meristem size, root growth, stomatal differentiation, and organ abscission . sORFs treceptor . A peptireceptor . Currentreceptor .PeptiDB dataset comprising of 103 protein-peptide complexes, 19 peptides bind as β-strands, which use far more H-bonds on average, while 18 peptides were bound as α-helices, which form less H-bonds with proteins and contain more nonpolar atoms at the interface [The characterization of protein-peptide interactions can be used to evaluate the binding affinity of the model. One of the major factors determining the binding of a peptide to a protein is the size of the pocket . In our The pepATTRACT-local docking method has advantages over other protein-peptide docking methods. First, pepATTRACT-local docking outperforms blind docking whose performance is similar with other local docking methods . Second,A. thaliana genome may encode thousands of small proteins that could function as peptide signals and more than 600 plasma membrane-bound receptor-type proteins that could act as receptors for peptide ligands [The  ligands . Several ligands . Apart fS1 Figp-values using Benjamini-Hochberg correction method vs. raw p-values for the 576 docked models. (B) Histogram showing distribution of the number of close contacts across the 104 top models.(A) Plot of adjusted (TIF)Click here for additional data file.S2 Fig(A) Comparison of pepATTRACT-local and blind docking protocols (B) Effect of random shuffling on the binding of peptides to pockets(TIF)Click here for additional data file.S3 Fig(A) Average contributions to the binding energy for each amino acid type, for peptide and protein amino acids separately, and (B) for peptide amino acids at different locations within the peptides. (C) Individual data points for all amino acids, from which the averages were made, with red lines representing the average values. The represented data includes only amino acids whose binding contribution is at least 40% of the maximal contribution value within the respective system.(TIF)Click here for additional data file.S4 FigThe peptide binding pocket is highlighted in yellow.(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Tablep-values and ΔGbind values of the models.The table includes the PepSite2 (PDF)Click here for additional data file.S3 TableFor fields that are indicated as monomers in Protein stoichiometry, the other chain in the structure is either a characterized peptide or the monomers may biologically aggregate to form dimers.(PDF)Click here for additional data file.S4 TableEmpty fields in the ligand column indicate only catalytic sites and no known ligand is known to bind at the respective sites.(PDF)Click here for additional data file.