Protein complexes are dynamic. A new analysis of two quantitative proteomic datasets reveals cell type-specific changes in the stoichiometry of complexes, which often involve paralog switching.www.dx.doi.org/10.1186/s13059-016-0912-5Please see related Research article:  The function of a protein is often tied to its interactions, and many proteins function as components of large multiprotein complexes. Multiprotein complexes also will connect to each other in a cell to carry out coordinated biological functions. Every cell has a network of protein interactions, where these connections within and between proteins and complexes yield insights into cellular states. Large-scale studies have been conducted to define human protein interaction networks through the analysis of thousands of affinity purifications in multiple cell types. Two recent studies by Huttlin and colleagues, and Hein et al., have reported human interactomes constructed using data from HEK293T cells , and HeLThere is certainly value in these types of analyses, in which, for example, new interactions of disease-related proteins can be found and characterized . HoweverAnalyses of interaction networks are multidisciplinary efforts. In most of the papers mentioned above, computational biologists, biochemists, cell biologists and proteomics scientists were needed to perform the studies. These studies are often classified as ‘systems biology’, but this classification sometimes obscures the diverse range of skills needed to undertake these studies. For example, in 2013 a group at the European Molecular Biology Laboratory (EMBL) collaborated to analyze human nuclear pore complexes . This inGenome Biology [Several members of the same team that studied cell type-specific nuclear pores asked this question in a new study published in  Biology . First,  Biology  and the  Biology . These o Biology , 10 are Over half of the 182 protein complexes analyzed were variable. More specifically, 102 of the complexes analyzed were variable, and 80 were stable. Stable complexes included the ribosome, the proteasome, mitochondrial protein complexes, and the exosome. However, some variability was seen in the ribosome, consistent with emerging evidence regarding the functional importance of specialized ribosomes . By contThese observations raise questions concerning how these variable complexes are regulated. Certainly, detailed and focused studies on each of the complexes are warranted in the future, but here the authors searched for general principles. They focused on the induced pluripotency dataset in mouse because gene expression data were available. Fewer than half of the cases of variant changes were likely attributable to transcriptional regulation, where protein and transcript abundance changed in the same direction at the same point in time. Almost two-thirds of the cases appear to be regulation at the level of translation or protein turnover. An analysis of structures from the Protein Data Bank suggested that stable interactions have structural properties different to those of variable interactions. Specifically, the authors suggest that variable interfaces are less hydrophobic than stable interfaces and might be more accessible to regulatory events such as phosphorylation.Further analysis of the variable complexes revealed frequent paralog switching, where paralogs are genes produced through gene duplication in a genome and in these variable complexes one paralog would be replaced by another under certain circumstances. In the reprogramming dataset , the autProteomics and genomic data existed for a limited number of the paralog switches. The authors analyzed these data to gain insight into the potential regulation of these switches. In most cases, changes in protein and transcript abundance correlated for one of the two paralogs, but, in the case of the SWI/SNF (BAF) complex, protein and transcript changes correlated for both paralogs. This suggests that there are probably several mechanisms for controlling such paralog switches.Genome Biology study [There is no single human interactome. There are many. How they are different and regulated is crucial for their understanding. Protein interaction networks are dynamic and context dependent. The differences in networks between cellular states are probably determined by key regulatory mechanisms for controlling these states. An excellent example reported in the recent gy study , and in gy study . While tgy study  used two


The PTM database is a compendium of public PTM data, coupled with user-uploaded experimental data. ProteomeScout provides analysis tools for experimental datasets, including summary views and subset selection, which can identify relationships within subsets of data by testing for statistically significant enrichment of protein annotations. Protein annotations are incorporated in the ProteomeScout database from external resources and include terms such as Gene Ontology annotations, domains, secondary structure and non-synonymous polymorphisms. These annotations are available in the database download, in the analysis tools and in the protein viewer. The protein viewer allows for the simultaneous visualization of annotations in an interactive web graphic, which can be exported in Scalable Vector Graphics (SVG) format. Finally, quantitative data measurements associated with public experiments are also easily viewable within protein records, allowing researchers to see how PTMs change across different contexts. ProteomeScout should prove useful for protein researchers and should benefit the proteomics community by providing a stable repository for PTM experiments.ProteomeScout ( ProteomeScout is a database of protein post-translational modifications, protein annotations and a web resource for analysis of PTMs and proteins. ProteomeScout is unique in the world of post-translational modification (PTM) databases—it is the only database that encompasses PTM compendia and allows for the community deposition of PTM experiments, and it is the only web service that has built-in subset selection and enrichment for exploring and analyzing PTM experiments. ProteomeScout is also unique in the world of protein resources—it is a resource that entails a flexible, track-based viewer of protein annotations. The purposeful design of these features was inspired by the historical progression of resources for other types of molecules, including: the UCSC Genome Browser  and the Growth in PTM identification is just one part of the rapid expansion in PTM-centric research; the other part involves the quantitative profiling of PTM changes across conditions. New experimental capabilities have enabled a number of studies, including high-throughput profiling of tyrosine phosphorylation changes in response to receptor tyrosine kinase activation ,13, methstatus quo for PTM data involves publication of isolated, static supplementary tables. This means that there is no single standard format, no central repository and, due to the volatility of protein databases  Batch Search or (iii) Annotate Sites. When a protein accession is passed to ProteomeScout through these methods, the appropriate external database is queried for the taxonomy and protein sequence associated with that accession. If the sequence from that species already exists in ProteomeScout, then the accession is added. If the protein sequence does not already exist, then all of the following protein annotations (Table The protein loading process begins when accessions are loaded through one of the following three interfaces: (i) ns Table  are addeProteins specified by UniProt accession numbers are obtained from the UniProtKB batch query web service. Proteins specified using other accession numbers are, if possible, fetched from NCBI Entrez  using thhttp://www.uniprot.org/docs/ptmlist) and the ExPASy FindMod tool (http://web.expasy.org/findmod/findmod_masses.html) (Upload Dataset —ProteomeScout ensures that the annotated residue and the species of the designated protein accession match the ‘Target Residue’ and ‘Taxonomic Range’ of the modification.PTM ontologies are established from the UniProt ptmlist (es.html) . Rules fWhen proteins can be cross-referenced or are queried with a UniProt accession, pre-generated domain predictions from the Pfam Web service are fetched . In the Annotations of ‘Biological Process’, ‘Molecular Function’ and ‘Cellular Compartment’ are obtained for each protein record by querying the EBI QuickGO  service,http://scansite3.mit.edu, using the proteinScan tool. The stringency value is set to ‘low’ to retrieve all available predictions. The motifClass is chosen based on the sequence's taxonomic lineage. Any sequence with taxonomic node ’mammalia’ is queried with the ‘MAMMALIAN’ motifClass, while sequences with taxonomic nodes ‘Sacchromycotina’ or ‘Saccharomyces’ is queried with the ‘YEAST’ motifClass. Any protein not satisfying these taxonomic criteria are excluded from Scansite predictions.Scansite predictions are retrieved from Scansite 3.0 , availabProtein records obtained from NCBI are parsed for region annotations containing the ‘variant’ region name field. Protein records obtained from UniProtKB are parsed to extract ‘Natural Variant’ annotations. Variants representing single amino acid changes from either of these sources are stored in ProteomeScout, along with associated dbSNP  accessioAccession numbers provided in a user query are cross-referenced to obtain accession numbers for UniProt/SwissProt , NCBI 2, GenBankThe complete taxonomic ranges for all species are parsed from the NCBI or UniProtKB record. In the event that there is a host organism  the taxonomic lineage for the host organism is obtained from the Entrez taxonomy web interface using the Biopython Entrez API .https://www.assembla.com/spaces/proteomescout.ProteomeScout is written in the Python programming language using the Pyramid Web Application Development Framework. Interactive user interface elements are implemented in HTML, CSS and JavaScript, using the JQuery, JQuery UI and D3 libraries. All proteomic data is stored in a MySQL relational database, which is accessed through Python using the SQLAlchemy Object Relational Mapping API. The web application is deployed on an Ubuntu Linux Server (v12.04) using the Apache Web Server with modwsgi. ProteomeScout uses the Python Celery parallel computing toolkit to run distributed data import, export and statistical testing over multiple threads and machines. ProteomeScout uses JavaScript and the D3 library for visualization of data in the protein viewer, experiment summary/explorer and the database statistics pages. Venn diagrams are generated with Ben Frederickson's D3-based Venn Diagram tool. ProteomeScout's documentation, ticketing system and Mercurial repository are hosted freely by Assembla as an open-source project; available at Data submission is done interactively on the web site. The first step in dataset loading is data file preparation. Users should use a text or Excel editor to create a tab-separated data file containing the appropriate headers that designate the columns of interest . In the second step, a registered user uploads this file to ProteomeScout. The remaining steps will walk users through verifying the column assignments, checking a subset for potential problems and setting the metadata . If published, a PMID (PubMed identifier) can be used to automatically retrieve title, authors and other journal information. Finally, after the dataset load is completed, owners will receive an email notification and link to their dataset's error log. Errors are described in the log and an easy process exists for downloading the subset of the data file with errors and then reloading the corrections. Example screen shots and files are provided on the help documentation under the title ‘Loading a Dataset’.Upload Dataset, Annotate Sites) or result in the email communication of results . Protein Viewer, Statistics and Dataset Download do not require user login. There are no restrictions for registering for an account, except a valid email address.ProteomeScout code and graphics are licensed under a Creative Commons Share-Alike with Attribution 4.0 International license. The ProteomeScout database download additionally requires ‘non-commercial’ use, since a majority of the compendia require that restriction. Registration is required for the resources that involve assignment of ownership . Users can use the entire dataset as a background or specify a subset of the data to use as a background. Feature enrichment is performed over all GO terms, Scansite predictions, Pfam predictions and kinase activation loop predictions using the Fisher's exact test. Fisher tests are evaluated using the NumPy and SciPy Python packages . Motif eEvaluate Subsets tool. Users can download annotated versions of their data using the Export Experiment tool, cluster using MCAM, import their clustering annotations, and perform enrichment analysis across all clusters and all cluster sets. Feature selection and evaluation can then be performed using the enrichment results, which ProteomeScout sends by email. The MCAM Mercurial repository is also hosted by Assembla and linked to the ProteomeScout Assembla page.Support for MCAM, a method of ensemble clustering with feature selection , has beeCompare Datasets tool allows users to compare the modified residues identified in their experiments with other experiments and compendia. We consider a modified residue to be shared between two experiments if three conditions hold. First, the modified residue is assigned to the same protein. Second, the modified residue is at the same position in the protein. Third, the modified residue is annotated with the same modification type.The Report Ambiguity tool. We also provide a tool to assign all ambiguous peptides to ‘default’ proteins using the Assign Defaults feature. ‘Default’ proteins are chosen as the protein with the most reported modifications in ProteomeScout. This tool allows users to create a new dataset representing assignments of peptides to ‘canonical’ protein records, which is particularly useful for evaluating the novelty of modified residues using the Compare Datasets tool.The majority of high-throughput measurements of PTMs involves mass spectrometry-based identification of protein fragments. Consequently, there is often ambiguity regarding which protein or proteins, a peptide has originated from. The ProteomeScout web interface highlights when a peptide ambiguously maps to an assigned protein sequence by searching for alternate matching proteins in the ProteomeScout and UniProtKB databases. Ambiguously assigned peptides can easily be reassigned to alternate sequences via a drop-down menu on the Kinase activation loop predictions are based on finding the amino acid sequence, spanning less than 35 amino acids, between the N-terminal ‘D(F/P/L/Y)G’ and C-terminal ‘(A/S/P)(P/I/L/W)(E/D)’ flanking sequences. Identification of the degenerate flanking sequences was defined in the PTMScout publication .The ProteomeScout database is built around two central data types: datasets and proteins. Experimental datasets encompass sets of modified peptides, which are associated with a specific protein record and may be associated with quantitative data. Protein records are associated with annotations Table . ProteomStatistics page (linked from the home page). These statistics indicate that the majority of documented PTMs come from human, mouse and rat proteins. The top five most annotated modification types are: phosphorylation of serine, threonine and tyrosines; and acetylation and ubiquitination of lysines. ProteomeScout data is available for download, in bulk or as species—and modification-specific files. These flat-text files, which contain coverage of PTMs from multiple compendia and public experiments—with related protein annotations—will be especially useful for those groups exploring proteome-wide phenomena of PTMs.With an emphasis on community-driven deposition, the coverage of PTMs within ProteomeScout will continue to expand. ProteomeScout currently includes six major curated sources of PTMs, which are updated frequently, . Data associated with public experiments becomes visible on individual protein pages . This feature could be utilized for a variety of reasons. For example, for a set of clinical SNPs, one could explore where these occur relative to secondary structure elements, known modifications, or protein domains, which has the potential to improve the prediction of whether a SNP affects protein function or regulation.s Figure  is a ProDataset Comparison and Ambiguity Reporting, are discussed in the ‘Materials and Methods’ section.There are several powerful analysis tools available for datasets, which include at-a-glance summaries of constituting protein annotations, subset creation with enrichment testing and comparison with other datasets, including identifying novel modifications. These tools were developed to assist researchers in discovering important biological information within datasets. Here, we will focus on analysis tools that allow users to explore the composition of proteins and peptides in their datasets and test the statistical significance in subsets of their datasets. Other analysis tools, such as Data Summary panel of Figure Evaluate Subsets feature. The Evaluate Subsets page will then list the subset of proteins and peptides, and their annotations, as well as testing for enrichment of annotations in this subset, relative to the full dataset.The Subset Selection tool, available from Evaluate Subsets, that allows users to create simple, or compound, logical selection criteria for creating a subset. The subset is then tested for enrichment in protein and peptide annotations, with enrichment thresholds controlled by the user, including the selection of appropriate multiple hypothesis correction methods . Subsetery Rate . That saEvaluate Subsets and so we have included a set of examples for demonstration in Table Should the user-interface for dataset subset selection be insufficient, users can export the dataset and assign their own ‘clusters’ for reimport and selection. This export/annotate/import feature can be used to interface with our MATLAB-based ensemble clustering software, MCAM , which wProtein Viewer  reserving the right as administrators to remove questionable data. Additionally, researchers could use the number of different experiments documenting a PTM as a proxy for the confidence associated with a PTM's identification. The number of experimental annotations are available in the database download, as well as on the website. We believe, despite the unique challenges of a community-driven resource of PTMs, that the advantages far outweigh the complications.Fortunately, there are many wonderful curated sources of PTMs, such as PhosphoSitePlus , dbPTM  and UniPThere are features in a number of PTM-centric resources that future versions of ProteomeScout will ideally incorporate, or link directly to. For example, UniprotKB  and PhosProtein Viewer, with track-based views of protein annotations, with the ability to zoom and export an editable graphics file, is different than other protein resources, even protein-viewer centric resources. EBI's DASty service (In addition to the PTM-centric aspects of ProteomeScout, the more global protein-centric tools of ProteomeScout will be useful for a broad range of researchers. Specifically, the  service  dynamicaIt is the growth from PTM- to protein-centric features that led us to rename our original resource from PTMScout to ProteomeScout. All the functionality of PTMScout  has been


The identification of compound-protein interactions plays key roles in the drug development toward discovery of new drug leads and new therapeutic protein targets. There is therefore a strong incentive to develop new efficient methods for predicting compound-protein interactions on a genome-wide scale. In this paper we develop a novel chemogenomic method to make a scalable prediction of compound-protein interactions from heterogeneous biological data using minwise hashing. The proposed method mainly consists of two steps: 1) construction of new compact fingerprints for compound-protein pairs by an improved minwise hashing algorithm, and 2) application of a sparsity-induced classifier to the compact fingerprints. We test the proposed method on its ability to make a large-scale prediction of compound-protein interactions from compound substructure fingerprints and protein domain fingerprints, and show superior performance of the proposed method compared with the previous chemogenomic methods in terms of prediction accuracy, computational efficiency, and interpretability of the predictive model. All the previously developed methods are not computationally feasible for the full dataset consisting of about 200 millions of compound-protein pairs. The proposed method is expected to be useful for virtual screening of a huge number of compounds against many protein targets. The identification of compound-protein interactions is an important part in the drug development toward discovery of new drug leads and new therapeutic protein targets. The completion of the human genome sequencing project has made it possible for us to analyze the genomic space of possible proteins coded in the human genome. At the same time, many efforts have also been devoted to the constitution of molecular databanks to explore the entire chemical space of possible compounds including synthesized molecules or natural molecules extracted from animals, plants, or microorganisms. However, there is little knowledge about the interactions between compounds and proteins. For example, the US PubChem database stores more than 30 million chemical compounds, but the number of compounds with information on their target proteins is very limited . In thatin silico chemogenomic approaches have been developed to predict compound-protein interactions or drug-target interactions, assuming that similar compounds are likely to interact with similar proteins. The state-of-the-art in the chemogenomic approach is to built the chemogenomic space of compound-protein pairs as the tensor product of the chemical space of compounds and the genomic space of proteins, and analyze compound-protein pairs by machine learning classifiers such as support vector machine (SVM)  to . Let i T= i1t, ⋯, iℓ tbe a resultant string projected from iS. The projection is defined as the minimum element of the random permutation of the given set,Minwise hashing is a random projection of sets such that the expected Hamming distance of obtained symbol strings is proportional to the Jaccard similarity . We pickFor example, if i S=  is transformed to iS) = , and the final product is ik t= 1. The collision probability, which is a probability that two sets i Sand j Sare projected to the same elements ik tand jkt(ik = tjkt), is described asi tand j tis identical to ℓ).Therefore, the expected Hamming distance between b-bit minwise hashing  →   be an inverse mapping for permutation k π:  → . Let h-1 :  → ∗ be an inverse mapping for additional hashing h :  → . Note that h-1 is, basically, a one-to-many mapping N <<M.We propose to keep inverse mappings h-1 to each element in the compact fingerprint to recover values hashed by additional hashing h. Since h-1 is a one-to-many mapping, several values are recovered. Then, inverse mapping π-1 is applied to each value in order to recover an element in the original fingerprint. Finally, we compute an average of the weights learned by linear SVMs, which provides the recovered weight vector for the original fingerprint. Figure First, we apply inverse mapping We tested MH-L1SVM and MH-L2SVM (newly proposed methods) on their abilities to predict compound-protein interactions from compound substructure fingerprints and protein domain fingerprints, and compared the performance with L1SVM and L2SVM (previous methods ) in termC = 10-5, 10-4, ..., 105 and chosen as the one to achieve the best AUC score.In the pair-wise cross-validation we perform the following procedure: 1) We randomly split compound-protein pairs in the gold standard set into five subsets of roughly equal sizes, and take each subset in turn as a test set. 2) We train a predictive model on the remaining four subsets. 3) we compute the prediction scores for compound-protein pairs in the test set. 4) Finally, we evaluate the prediction accuracy over the five folds. The pair-wise cross-validation assumes the situation where we want to detect missing interactions between known ligand compounds and known target proteins with information about interaction partners. In the block-wise cross-validation we perform the following procedure: 1) We randomly split compounds and proteins in the gold standard set into five compound subsets and five protein subsets, and take each compound subset and each protein subset in turn as test sets. 2) We train a predictive model on compound-target pairs in the remaining compound subsets and four protein subsets. 3) We compute the prediction scores for compound-protein pairs involving test compound set and test protein set. 4) Finally, we evaluate the prediction accuracy over the five folds. The block-wise cross-validation assumes the situation where we want to detect new interactions for newly arriving ligand candidate compounds and target candidate proteins with no information about interaction partners. In the both cases, we evaluated the performance by the area under the ROC curve (AUC) and execution time. The cross-validations were performed by varying the hyper-parameter l and the size of hashing values N in the minwise hashing process of MH-L1SVM and MH-L2SVM on the performance. We tried five different lengths of string ℓ = 5, 10, 15, 30, 50. The size of additional hashing values N is varied from 22 to 232. Figure ℓ = 10 and the size of additional hashing value N = 216, and the AUC score was comparable to that for the original fingerprint.We investigated the effects of the length of strings ℓ is varied from 5 to 50 and the size of additional hashing value is fixed to N = 216. The AUC scores of MH-L1SVM and MH-L2SVM with the length of string ℓ = 10 and the size of additional hashing N = 216 were comparable to those of L1SVM and L2SVM. In addition, MH-L1SVM and MH-L2SVM achieved certain speedup compared with L1SVM and L2SVM.Figure The same trends of these results in the pair-wise cross-validation were observed in the case of the block-wise cross-validation as well. The corresponding results for the block-wise cross-validation are shown in Figures We evaluated the performance for the full data consisting of 216,121,626 compound-protein pairs, where the best parameter values for each method in the cross-validation experiments in the previous subsection were used. We examined the effect of the ratio of positive compound-protein pairs against negative compound-protein pairs on the performance. Note that the number of negative examples is much larger than that of positive examples in our dataset. We varied the number of negative examples in the cross-validation from the same number of positive examples to the number of all possible negative examples.Figure Table The same trends for these results in the pair-wise cross-validation were observed in the block-wise cross-validation as well . The originality of the proposed method lies in the scalable prediction of compound-protein interactions, in the computational efficiency, and in the interpretability of the predictive model. It should be pointed out that all previous methods were not computationally feasible for the full data. The proposed method is expected to be useful for virtual screening of a large number of compounds against many protein targets.https://sites.google.com/site/interactminhash/.The proposed method can be used, as soon as compounds and proteins are represented by binary descriptors . However, a limitation of the proposed method is that the performance depends on the definitions of chemical substructures of compounds and functional domains of proteins. The use of other descriptors  could improve the generalization properties of the method. Datasets, all results and softwares are available at None declared.YT implemented the algorithm of the methods, made all analyses, and drafted the manuscript. YY prepared the datasets, and drafted the manuscript.


Protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein-protein interactions (PPIs), making it possible to predict protein complexes from protein-protein interaction networks. On the other hand, the rapidly growing biomedical literature provides a significantly large and readily available source of interaction data, which can be integrated into the protein network for better complex detection performance.We present an approach of integrating PPI datasets with the PPI data from biomedical literature for protein complex detection. The approach applies a sophisticated natural language processing system, PPIExtractor, to extract PPI data from biomedical literature. These data are then integrated into the PPI datasets for complex detection.The experimental results of the state-of-the-art complex detection method, ClusterONE, on five yeast PPI datasets verify our method's effectiveness: compared with the original PPI datasets, the average improvements of 3.976 and 5.416 percentage units in the maximum matching ratio (MMR) are achieved on the new networks using the MIPS and SGD gold standards, respectively. In addition, our approach also proves to be effective for three other complex detection algorithms proposed in recent years, i.e. CMC, COACH and RRW.The rapidly growing biomedical literature provides a significantly large, readily available and relatively accurate source of interaction data, which can be integrated into the protein network for better protein complex detection performance. Protein complexes are molecular aggregations of proteins assembled by multiple protein-protein interactions. Many proteins are functional only after they are assembled into a protein complex and interact with other proteins in this complex. These protein complexes can help us to understand the principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to uncover protein complexes from protein interaction networks. A protein interaction network can be modeled as an undirected graph, where vertices represent proteins and edges represent interactions between proteins. Protein complexes are groups of proteins that interact with one another, so they are usually dense sub-graphs in PPI networks. Various algorithms based on graph theory have been applied to identify protein complexes and functional modules from protein interaction networks, including CFinder , CMC 2]2], COACHAt the same time, a number of databases, such as Gavin , Krogan Our work aims to quantifying the contribution of PPI data from biomedical literature as a supplement to the existing PPI datasets. In this paper, we present an approach of integrating PPI datasets with the PPI data from biomedical literature for protein complex detection. The approach applies a sophisticated natural language processing system, PPIExtractor , to extrIn this work, we apply the PPIExtractor system to extract PPI data from biomedical literature, which are then integrated into the protein network for protein complex detection.Among the popular machine learning approaches to extracting PPIs from biomedical literature, kernel-based methods including tree kernels , shortesIn previous work, we presented PPIExtractor to automatically extract protein-protein interactions from biomedical literature. PPIExtractor is a multiple kernels learning based system which combines the feature-based, convolution tree and graph kernels to extract PPIs. The combined kernel can reduce the risk of missing important features, yielding new useful similarity measures. More specifically, the weighted linear combination of individual kernel used instead of assigning the same weight to each individual kernel is experimentally proven to contribute to the performance improvement. Experimental evaluations show that PPIExtractor can achieve state-of-the-art performance on a DIP subset with respect to comparable evaluations. More complete details are presented in .PPIExtractor contains four modules: (i) Named Entity Recognition (NER) module which aims to identify the protein names in the biomedical literature; (ii) Normalization module which determines the unique identifier of proteins identified in NER module; (iii) PPI extraction module which extracts the PPI information in the biomedical literature and (iv) PPI visualization module which displays the extracted PPI information in the form of a graph. Figure http://www.ncbi.nlm.nih.gov/pubmed) with the query string "((Saccharomyces cerevisiae) OR yeast) AND protein". 126,165 protein interactions were extracted from these abstracts by the PPIExtractor system.The biomedical literature PPI data we used is 127,217 PubMed abstracts downloaded from PubMed website (http://www.uniprot.org/uniprot/?query=yeast&sort=score). The list is used to convert the protein names in PubMed abstracts to systematic names for nuclear-encoded ORFs. In our method, a PPI can be added into a PPI dataset only if the two proteins in the PPI already exist in the PPI dataset.Most of the protein names in the PPI databases are systematic names for nuclear-encoded ORFs begin with the letter 'Y' (for 'Yeast') while those in PubMed abstracts are not. Therefore, we built a yeast protein alias name list with about 6,000 entries from the UniProt website  and the clustering-wise positive predictive value (PPV). Let n be the number of the benchmark complexes and m be the number of the predicted complexes. Construct a confusion matrix T, and let i and predicted complex j. Sn and PPV are defined as follows:The second measure we used is the geometric accuracy as introduced by Broh´ee and van Helden , which ii, then Here, we define Sn value indicates that the prediction has a good coverage of the proteins in the true complexes, whereas a high PPV value indicates that the predicted complexes are likely to be true complexes. So it is necessary to balance the two measures by introducing the geometric accuracy (Acc), which is simply the geometric mean of the clustering-wise sensitivity and the positive predictive value:Generally, a high R as the standard complex, and P as the predicted complex. An edge connects a standard complex and a predicted complex if their neighborhood affinity score is larger than zero. Given n standard complexes and m predicted complexes, let j be the member of the predicted complexes, MMR then defined as follows:The third measure we used is the maximum matching ratio (MMR) which was introduced in . This meThe geometric accuracy measure explicitly penalizes predicted complexes that do not match any of the reference complexes. However, gold standard sets of protein complexes are often incomplete . As a coFirst, we tested ClusterONE on the Collins, Gavin, Krogan-core, Krogan-extended and BioGRID dataset. Tables The similar results were obtained when the SGD dataset was used as the gold standard as shown in Figure The performances of three other complex detection algorithms proposed since 2009  on these yeast PPI datasets are shown in Tables On the BioGRID dataset, the performances of these algorithms decrease with the threshold -0.6: in term of MMR, there is an 8.41 percentage unit decrease in the performance of the RRW algorithm using the MIPS gold standard while there are 11.15 and 4.89 percentage unit decreases in the performance of the CMC and RRW algorithms using the SGD gold standard, respectively. Through the analysis of the results, we found that these algorithms obtain more clusters on BioGRID with the threshold -0.6 than on the original BioGRID. However, many of them are not matched one, i.e. they can not match with any complex in the gold standards, which deteriorates the performances of the complex detection algorithms.The reason behind it is that, in our method, a PPI with the weight equal to or higher than a threshold is integrated into the PPI dataset only if both two proteins in the PPI already exist in the PPI dataset. Since the BioGRID dataset includes the most proteins 5,460), the most PPIs are integrated into it as shown in Figure 0, the moOn a dataset like BioGRID, many candidate complexes with star-like structures and low density values should have been discarded based on the density threshold by complex detection algorithms. However, when the PPI data from literature are integrated, many such candidate complexes will be retained since the density values of these complexes are increased with the inclusion of new PPI data. As shown in the example of Figure This assumption can be supported by the following fact: with the threshold -0.6, a total of 6,025 PPIs are integrated into the BioGRID dataset and a total of 105 detected complexes by ClusterONE are increased from 472 to 577). Since many of them can not match with any complex in the gold standards, the performance is deteriorated. As can be seen from Figures 2 to 577.On the other hand, we found if the threshold is set to 0 and less PPIs 1,210) are integrated into BioGRID, much better performance can be achieved using any gold standard (MIPS and SGD) as shown in Figures  are inteTherefore, with the databases with the low transitivity like BioGRID, the threshold should be set to higher to ensure less PPIs are integrated into the databases, and, in other cases, the threshold can be set to -0.6. In this way, the performances of protein complex detection algorithms can be improved through the integration of PPI datasets and the PPI data extracted from literature.Protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, making it possible to predict protein complexes from protein-protein interaction networks. On the other hand, the rapidly growing biomedical literature provides a significantly large, readily available and relatively accurate source of interaction data, which can be integrated into the protein network for better protein complex detection performance. In this paper, we present an approach of improving protein complex detection methods with integrated PPI data from biomedical literature. The approach applies PPIExtractor to extract PPI data from biomedical literature, which are then integrated into the protein network for protein complex detection. The experimental results of ClusterONE on five yeast PPI datasets show the effectiveness of our method: compared with the original networks, the average improvements of 3.976 and 5.416 percentage units in MMR are achieved on the new networks using the MIPS and SGD gold standards, respectively. In addition, our method also proves to be effective for three other algorithms proposed in recent years, CMC, COACH and RRW.Through the analysis of the experimental results, we found the choice of the threshold usually can be set to -0.6. However, for the databases with the low transitivity like BioGRID, the threshold should be set to higher. In this way, the performances of the state-of-the-art protein complex detection algorithms can be improved through the integration of the existed PPI datasets and the PPI data extracted from literature.A rapidly growing literature corpus ensures that PPI data is a readily-available resource for nearly every studied organism, particularly those with small protein interaction databases. PPI data provides a significantly large and readily available source of interaction data which, together with the guidelines and results reported here, will prove valuable especially for organisms in which protein-protein interaction data is sparse.The authors declare that they have no competing interests.ZHY conceived of the study, carried out its design and drafted the manuscript. FYY participated in the design of the study and performed the experiments. HFL and JW participated in its design and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.


S-nitrosylation (SNO), it is considered to act, in a manner analogous to phosphorylation, as a pleiotropic regulator that elicits dual effects to regulate diverse pathophysiological processes by altering protein function, stability, and conformation change in various cancers and human disorders. Due to its importance in regulating protein functions and cell signaling, dbSNO (http://dbSNO.mbc.nctu.edu.tw) is extended as a resource for exploring structural environment of SNO substrate sites and regulatory networks of S-nitrosylated proteins. An increasing interest in the structural environment of PTM substrate sites motivated us to map all manually curated SNO peptides (4165 SNO sites within 2277 proteins) to PDB protein entries by sequence identity, which provides the information of spatial amino acid composition, solvent-accessible surface area, spatially neighboring amino acids, and side chain orientation for 298 substrate cysteine residues. Additionally, the annotations of protein molecular functions, biological processes, functional domains and human diseases are integrated to explore the functional and disease associations for S-nitrosoproteome. In this update, users are allowed to search a group of interested proteins/genes and the system reconstructs the SNO regulatory network based on the information of metabolic pathways and protein-protein interactions. Most importantly, an endogenous yet pathophysiological S-nitrosoproteomic dataset from colorectal cancer patients was adopted to demonstrate that dbSNO could discover potential SNO proteins involving in the regulation of NO signaling for cancer pathways.Given the increasing number of proteins reported to be regulated by  S-nitrosylation (SNO), one of the various post-translational modifications (PTMs), is reversible and involves the covalent attachment of nitric oxide (NO) to the thiol group of cysteine (Cys) residues. SNO is considered to act, in a manner analogous to phosphorylation, as a pleiotropic regulator that elicits dual effects to regulate diverse pathophysiological processes in plant  patients  regulatory networks. To facilitate the study of protein SNO and their functions, the web interface is redesigned and enhanced. Published literature information related to SNO sites, structural characterization of SNO substrate sites, protein–protein interactions (PPIs) and metabolic pathways of SNO proteins are also provided in this online resource. The details of each improvement are depicted as follows.The remarkable improvements and advances in dbSNO 2.0 are presented in Supplementary Figure S1, including data update for S-nitrosylated’ against all fields of PubMed literature database. Due to a variety of proteomic identification experiments, a text-mining method is developed to extract the full-text literature that potentially describes the site-specific identification of SNO sites. Then, the full-length articles are manually reviewed for the extraction of the SNO peptides as well as the substrate cysteines. In this update, the SNO targets and sites are further defined as in vivo or in vitro according to their experimental model. For in vivo SNO, endogenous yet basal level, site-directed mutation on cysteine residue, induction or under stress and NO donor treatment in cultured cell is individually noted. In order to determine the precise locations of SNO cysteines within a full-length protein sequence, all in vivo or in vitro SNO peptides are mapped to UniProtKB  . InterPrDatabase , Online Database  and HumaDatabase  were inttegrated . This stS-nitrosylated annotations in dbSNO and mapped on metabolic pathways by Cytoscape program  binding domain and NAD(P)-binding domain.Based on the information from OMIM, KEGG and IPA database analysis, the correlation of these 720 human SNO proteins with disease-related processes and networks was also analyzed and categorized into 31 diseases. As shown in Figure S-nitrosoproteomic networks are assembled through reported direct and indirect PPIs and the human disease networks using OMIM and KEGG pathway. For instance, the CRC pathway from KEGG database is used as a case study. As shown in Supplementary Figure S7, 13 proteins and 11 SNO sites are matched in the classical CRC pathway based on the dbSNO data set. To further explore NOS2 and TXN, two well-studied regulating enzymes, in CRC, their associated proteins are also presented in CRC pathway by PPIs. Given a group of interacting proteins of one SNO target protein, its regulating upstream and downstream targets are also provided to discover new members that have the potential for involving in a mapped pathway. Taken together, the result provides us with a potential regulatory network connecting with the classical pathway that NOS2- and TXN-regulated protein family and their S-/trans-/de-nitrosylation function in cancer.Supplementary Figure S6 presents the web interface of reconstructing protein SNO network considering both the information of metabolic pathways and PPIs. In network visualization, the queried proteins that can be mapped to a specific member of a metabolic pathway are represented as squares colored with light blue. The queried proteins that could not be mapped to a specific member of a metabolic pathway are represented by blue circles. Moreover, the PPIs associated with the queried proteins are displayed as yellow lines. To further explore the relationship and functional assignments in disease and signaling pathway of SNO, the S-nitrosoproteome. The improvements and advances in dbSNO 2.0 are summarized in Table in vivo or in vitro according to their experimental models. The increasing interest in the structural environment of PTM sites prompted dbSNO to design a platform for providing the information of spatial amino acid composition, solvent-accessible surface area, spatially neighboring amino acids and side chain orientation for 298 SNO substrate sites based on protein tertiary structures. Owing to the importance of protein SNO in regulating protein functions and pathophysiological processes in various human disorders, the annotations of protein molecular functions, biological processes, functional domains and human diseases are integrated to explore the functional and disease associations for SNO proteins. Most importantly, the S-nitrosoproteomic study has been fueled by advances of high-throughput MS-based proteomic technologies that provide researchers with the updated dbSNO for exploring NO signaling dynamics.Due to the emerging roles of SNO proteins in the development and progression of cancers and diseases, dbSNO is updated as a resource for full investigation of large-scale S-nitrosoproteome data involved in human diseases. To provide more adequate information for functional and disease analysis, the descriptions associated with the biological function of SNO sites will be extracted with increased precision from full-text articles by using an enhanced information retrieval system. The SNO site-specific information associated with biological functions or disease annotations will greatly help the study of protein SNO in prospective works. Another area that we can envision dbSNO improving significantly in the future is in having the survey of SNO near drug binding sites in the protein tertiary structures and their effects (In the future, the growth of dbSNO is expected as the availability of data increases in research articles related to protein SNO, especially for the  effects .S-nitrosoproteome data involved in human diseases will be semiannually updated by manually reviewing the research articles. The updated resource is now freely accessed online at http://dbSNO.mbc.nctu.edu.tw/. All of the experimentally verified SNO sites could be downloaded in the text format.The data content in dbSNO 2.0 will be maintained and updated quarterly by continuously surveying the public resources and research articles. Also, the Supplementary Data are available at NAR Online.


In recent years, many protein complex mining algorithms, such as classical clique percolation (CPM) method and markov clustering (MCL) algorithm, have developed for protein-protein interaction network. However, most of the available algorithms primarily concentrate on mining dense protein subgraphs as protein complexes, failing to take into account the inherent organizational structure within protein complexes. Thus, there is a critical need to study the possibility of mining protein complexes using the topological information hidden in edges. Moreover, the recent massive experimental analyses reveal that protein complexes have their own intrinsic organization.Inspired by the formation process of cliques of the complex social network and the centrality-lethality rule, we propose a new protein complex mining algorithm called Multistage Kernel Extension (MKE) algorithm, integrating the idea of critical proteins recognition in the Protein- Protein Interaction (PPI) network,. MKE first recognizes the nodes with high degree as the first level kernel of protein complex, and then adds the weighted best neighbour node of the first level kernel into the current kernel to form the second level kernel of the protein complex. This process is repeated, extending the current kernel to form protein complex. In the end, overlapped protein complexes are merged to form the final protein complex set.Here MKE has better accuracy compared with the classical clique percolation method and markov clustering algorithm. MKE also performs better than the classical clique percolation method both on Gene Ontology semantic similarity and co-localization enrichment and can effectively identify protein complexes with biological significance in the PPI network. Mining protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks. So much attention has been paid to accurate detection of protein complexes from the increasing amount of protein-protein interaction (PPI) network data. In recent years, many protein complex mining algorithms have developed for protein-protein interaction network. However, most of the available algorithms primarily concentrate on mining dense protein subgraphs as protein complexes, failing to take into account the inherent organizational structure within protein complexes. Thus, there is a critical need to study the possibility of mining protein complexes using the topological information hidden in edges . MoreoveComplex social networks and complex biological networks both contain distinct community structures . The forStudies show that the PPI network is such a kind of the complex network, it has the properties similar to complex networks on topological structure, namely small world property , scale-fHalt et al. believed that criticality is an important property of protein complexes, and experimental data shows that critical proteins always heavily concentrate in certain complexes . Some reBased on the above ideas, we propose a novel protein complex mining algorithm called MKE (Multistage Kernel Extension) based on multistage kernel extension. MKE first transforms the undirected and unweighted graph of PPI network to a directed and weighted network graph, then selects the node set composed by high-degree and closely connected nodes in PPI network as the first level kernel of the protein complex, or as the kernel nodes of the protein complex, since these nodes are prone to play a key role in the biological function of the protein complex. Next, for each adjacent node of the first level kernel of the protein complex, MKE uses the definition of the weighted best neighbour node to determine the extent of the closeness between the adjacent node and the current kernel. If the extent of the closeness is greater than the average extent of closeness of the subgraph formed by the current kernel and its neighbouring nodes, then this neighbouring node can be added into the current kernel and be extended into the next level kernel. This process is continuously repeated; the kernel is extended stage-by-stage and finally a protein complex is constructed. Experimental results demonstrate that MKE is simple and effective, and the protein complexes identified with biological significance have a very high degree of match with reference protein complexes.In the protein-protein interaction network, for each pair of protein nodes, it is difficult to determine whether they belong to the same protein complex just by the degree of the nodes and their connection characteristics. Since two protein nodes have their own neighbour node set in PPI network, we can get their common neighbour node set. If one pair of protein nodes has more common neighbour nodes, it indicates that they have closer connection. Thus the possibility that the two proteins belong to the same protein complex is greater, and the probability that they participate in the same cell function is larger as well. Therefore, the common neighbour node set of one pair of protein nodes acts as an important role in weighing the relationship between the pair of protein nodes.s  and t , if there is an undirected edge between them, it can be converted into directed and weighted edge. Initially, the edge between node s  and node t  is undirected and unweighted.In the PPI network, for any two protein nodes, denoted by s  and node t  respectively in the PPI network, and the number of the common neighbour nodes between them is denoted by s  and node t  after conversion treatment, which is described in (2) and (3):s  and node t , t  and node s . Since it is an undirected graph, s , and t . After the conversion treatment, there are two directed and weighted edges between the node s  and node t , as shown in Figure Where, t  is very large, while the degree of protein node s  is so small. Although they have the same number of common neighbour nodes, according to formula (2) and (3), t , the possibility that node t  and node s  belong to the same protein complex is small, but in terms of node s , the possibility that node s  and node t  belong to the same protein complex is larger. Since the correlation is asymmetric, it is necessary to use two directed and weighted edges to weigh the correlation between the nodes. However, in these known protein complex mining algorithms, most of them fail to consider this detail, but treat it in the way with an equal weight, which is obviously unreasonable.In the above definitions, the weights of the edges between two nodes are unequal. Assume that the degree of protein node n  nodes in the network, let v , find its neighbour node set S . For any node t  in set S , the number of the edges between node t  and the other nodes in set S  is the number of common neighbour nodes between node v  and node t , and the time complexity is If there are In the PPI network, for one protein node, there are always a large number of neighbour nodes around it. Since the PPI graph has been transformed into a directed and weighted graph, it is convenient to find the neighbour node.v , if the directed weights between node v  and one of its neighbour node both greater than v , denoted by v  and its best neighbour node In terms of one node Where,c , c , v is one of the nodes in set u  is the node within cluster c . u  within cluster c  and its neighbour node v . The best neighbour node of cluster c  can be denoted by c  and its best neighbour node. The relationship of cluster c  and its best neighbour node In terms of a cluster, the neighbour nodes of the cluster refer to the nodes which have the direct interactions with the internal nodes of the cluster but are not in the cluster. For cluster In the PPI network, protein complex generally corresponds to the subgraph where proteins interact closely. The formation of protein complex is a slow procedure. Compared to other part of the protein complex, the first level kernel of the protein complex corresponds to the set of kernel nodes of the protein complex, which plays an irreplaceable role in the protein complex. Since the degree of the nodes in the PPI network follows the power-law distribution, there are a few nodes with a high degree and they play an important role in the network, corresponding to the core parts of the network. Since the closely connected and high-degree nodes have no significant difference between them, they are together taken as the first level kernel of the protein complex. n denotes the number of nodes in the network, k is the given degree threshold, v . k  in the PPI network. According to the different sizes of the PPI network, Where k  are selected to be the initial kernel nodes of the first level kernels of protein complexes. For the protein nodes within the first level kernel of the protein complex, there are plenty of common neighbour nodes between them, otherwise there is little chance that these nodes would belong to the same protein complex. Thus for two protein nodes, node s  and node t , when the directed weights between them are both greater than the given threshold value of the weight, it indicates that they closely connect with each other. Then they can be thought to belong to the same first level kernel of protein complex. [see Additional file According to the degree distribution of nodes in the PPI network, first, all the nodes in the network are ranked according to the degree, and nodes with degree greater than or equal to For each neighbour node of the identified first level kernel, we can adopt the definition of weighted best neighbour node to analyze the extent of closeness between the current kernel and the neighbour nodes. If the extent of closeness is greater than the average extent of closeness of the subgraph formed by the current kernel and its neighbour nodes, then add this neighbour node into the current kernel to generate the next kernel, otherwise, discard it.The first level kernel of the protein complex identified is usually the single protein node with high degree or the set of some protein nodes with high degree. Compared with the first level kernel of the protein complex, the degree of the nodes of the second level kernel is slightly small. However, like the first level kernel of the protein complex, the second level kernel of the protein complex is in the central position of the protein complex as well, so there is no substantial difference on the extent of connection closeness between the second level kernel and the first level kernel of the protein complex.Since the second level kernel of the protein complex is in the periphery of the first level kernel, we can naturally achieve the second level kernel by extending the first level kernel of the protein complex. In the protein-protein interaction network graph, the second level kernel of the protein complex can intuitively correspond to the network subgraph formed by the first level kernel and its adjacent nodes. Simply speaking, some special processing can be done on the adjacent network of the first level kernel of the protein complex, and then we can conveniently obtain the second level kernel of the protein complex.Generally speaking, in the local area, if the number of exchanges between two people or the number of common friends of two people exceeds the average number of exchanges or the average number of common friends between people in the district, then the relationship between the two people can be regarded as unusual. Likewise, the protein complexes in the PPI network possess the same property and law. Therefore, the average weight of the local network is taken as the criterion to measure whether two nodes belong to the identical protein complex. In terms of the directed and weighted PPI graph V| is the number of the nodes of the network formed by the current kernel and its neighbours, s  and node t . [see Additional file Where, |In the PPI network, the protein complex is a striking module structure which is formed by multistage kernel extension of the kernel protein nodes. The initial stage of kernel extension of the protein complex is very important, so we have elaborated on this before. Since the kernel extension stages of the protein complex are similar and the next stages of kernel extension are similar to the second stage, it is redundant and pointless to elaborate on the next stages of kernel extension of the protein complex.α  as a constraint.During the multistage kernel extension process, the previous extension stage is more important than the next extension stage. In general, the more important protein node sets account for smaller percentage in PPI network. Thereby, we can make a reasonable hypothesis accordingly that the number of the nodes added into the current protein complex kernel in the next stage is greater than that in the previous stage. In addition, due to the specificity of different networks, some kernels need to early terminate the extension to form the protein complexes after several extension stages; consequently, the algorithm introduces the Extended Level Parameter α  is greater than the threshold According to the above discussion, we implement the same process on the second level kernel like the way that the first level kernel extends to yield the second level kernel of the protein complex, and the process is repeated until the increased number In terms of a node in the PPI network, the extent of closeness between the node and a protein complex is obtained by calculating the extent of closeness between this node and the nodes satisfying special condition within the protein complex, rather than by calculating the extent of closeness between this node and multiple nodes within the protein complex. Therefore, using the definition of the weighted best neighbour node for the PPI network, we can find that algorithm in this paper predicts the protein complex by one node in the kernel extending to the nodes outside the kernel, which is different from most of other available algorithms that predict the modules by multiple nodes within the kernel extending to the nodes outside the kernel.i  and j , the overlap ratio between them is shown in (8):When the algorithm is over, we get the final set of the predicted protein complexes. Subsequently, we need to determine the possibility that two final kernels belong to the same protein complex according to the degree of overlapping. For the protein complexes i , and j . [see Additional file Where For the protein-protein interaction network data of all species, yeast protein-protein interaction network data is relatively complete, so the yeast protein-protein interaction network is selected as the main study object of the experiment. The experiment tests on Krogan dataset  and Collk  protein nodes. Provided that two k-cliques have Palla et al. (2005) have proposed algorithm CPM (Clique Percolation Method) which can identify the overlapped network cluster structures . The bas network . In the  network . And for network .In order to evaluate the predicted protein complexes, 408 protein complexes are artificially extracted to generate a elaborated catalogue from the published small scale experimental data, and a reference protein complexes dataset is created by filtering out 236 protein complexes with size at least 3, and the average size is 6.7. Meanwhile, since protein complex with size less than 3 is meaningless, the size of all predicted protein complexes analyzed in this paper is at least 3.α  of the algorithm to optimize the results of the algorithm on a given dataset.Different datasets have different network topology, and the organizational structures of clusters in different datasets vary too. Therefore we need to adjust the extended level parameter α . The impact that the extended level parameter α  has on the Krogan and Collins dataset is shown in Figure α . Although the average size of the protein complexes is stable when parameter α  is 4, it is far from the average size of the reference protein complex set. On Collins dataset, the average size of the protein complexes basically exhibits the similar trends. From Figure The MKE algorithm generates the protein complexes by extending the kernel clusters, but different datasets have different optimal extended level parameter n  represents the number of reference protein complexes, the number of columns m  represents the number of predicted protein complexes, the element of the matrix The accuracy measure Acc (accuracy) introduced by Brohee and Van Helden is usually used to compare the performance of algorithms , which iThe accuracy measure of algorithm can be defined asRespectively calculating these above measures of each algorithm on Krogan and Collins dataset, the results are listed in Table In Table On Collins dataset, the performance of CFinder and MCL exhibits high similarity as on Krogan dataset. Since the values of Sn and PPV of CFinder are extremely uneven, it results in lower values of Acc, which are 0.133 and 0.082 lower than Acc of MKE algorithm respectively on Krogan and Collins dataset. Consequently, on the whole, the MKE algorithm outperforms CFinder. Although Sn and PPV of MCL are relatively balanced on both dataset and there is no significantly difference on the values of Acc between MCL and MKE, MCL is slightly inferior to MKE algorithm.To assess the biological significance of the predicted protein complexes, we can calculate the P-value of the probability that the proteins with common function appear in the predicted protein complexes. P-value reflects the extent to which a given function enrich a protein complex, which is defined asN  denote the total number of nodes in protein-protein interaction network and C represent the number of proteins within the predicted protein complex, let k and F denote the number of proteins with a given function in protein complex and in PPI network, respectively. If P-value of the predicted protein complex is very low, then it explains that the probability of occurrence of these proteins in the network together exhibiting a given function as a protein complex is very small.Let Generally speaking, the function corresponding to the minimal P-value of the predicted protein complex is taken as its primary function or annotation function. Here, we adopt SGD's GO::TermFinder  to calcuCo-localization enrichment analysis is based on the fact that a protein complex can be formed only when its constituents are to be found in the same cellular compartment, and also that protein complex tends to be responsible for a given biological function and a molecular process . For a sm  and n , j , i  and predicted protein complex j .Where, the number of predicted protein complexes and reference cellular compartment protein complexes are respectively The GO (Gene Ontology) semantic similarity of the protein complex refers to the average degree of association of all protein pairs . The GO In the Table Therefore, by and large, although MKE algorithm is not better than all of the selected algorithms, MKE performs better than algorithm CFinder on the aspects of GO semantic similarity and co-localization enrichment, and can effectively detect the protein complexes with biological significance in the protein-protein interaction network.Due to the complexity of structure and the limitations of the experimental validation of the protein-protein interaction network, there is no convincing and strict definition regarding the verification standard of the protein complex up to now. Therefore, for protein complex mining, the detecting standard of the protein complex should be first confirmed. That is to say, what is the structure of the protein complex needs to be defined.The formation of the protein-protein interaction network follows its intrinsic law and the PPI network gradually develops by some protein complexes with inherent links. The criticality is an important property of the protein complex. Critical proteins can always be discovered within protein complexes, which are the high-degree nodes with many adjacent nodes. The protein nodes with higher degree tend to exhibit the criticality on biological properties as kernels and play an important role in the protein complex. Thus one or multiple critical nodes can be taken as kernels around which there are a lot of adjacent protein nodes closely connecting with each other, and the periphery of these adjacent proteins have also some adjacent nodes. All these nodes construct a relatively independent set which is able to implement some relatively independent biological functions. In other words, such protein sets are most likely to construct protein complexes.Inspired by the community formation law of the complex social network and the centrality-lethality rule, and combining the idea of critical protein nodes detection, this paper proposes a new protein complex mining algorithm MKE based on multistage kernel extension. MKE is the first algorithm to identify the innermost kernel of the protein complex, namely taking the critical nodes with high degree and more adjacent nodes as the first level kernel of the protein complex. Then MKE expands the first level kernel to be the second level kernel of the protein complex by adding the weighted best neighbour node into the current kernel, and repeatedly goes on expansion stage-by-stage to construct protein complex, and then MKE merges overlapped protein complexes to form the protein complex set. MKE has better accuracy compared with the classical clique percolation method and markov clustering algorithm. MKE also performs better than classical clique percolation method both on Gene Ontology semantic similarity and co-localization enrichment and can effectively identify protein complexes with biological significance in the PPI network.The authors declare that they have no competing interests.XS designed the protein complexes mining algorithm based on multistage kernel extension and weighted best neighbour node. YL implemented the protein complexes mining algorithm and run the experiments. YZ and JY helped plan the experiments analysed and contributed to writing the manuscript. TH and XTH supervised and helped conceive the study. All authors read and approved the final manuscript.Supplementary Algorithms and Time Complexity Analysis. A collection of algorithms and their corresponding time complexity analysis is available in Additional_file_1.pdf. Format: PDF. size: 123KBClick here for file


The server can therefore effectively model the structural differences between the template and target protein–peptide complexes. The performance of GalaxyPepDock is superior to those of the other currently available web servers when tested on the PeptiDB set and on recently released complex structures. When tested on the CAPRI target 67, GalaxyPepDock generates models that are more accurate than the best server models submitted during the CAPRI blind prediction experiment.Protein–peptide interactions are involved in a wide range of biological processes and are attractive targets for therapeutic purposes because of their small interfaces. Therefore, effective protein–peptide docking techniques can provide the basis for potential therapeutic applications by enabling an atomic-level understanding of protein interactions. With the increasing number of protein–peptide structures deposited in the protein data bank, the prediction accuracy of protein-peptide docking can be enhanced by utilizing the information provided by the database. The GalaxyPepDock web server, which is freely accessible at  Protein–protein interactions that are mediated by short linear peptides of interacting partners are critical in a broad range of biological processes, such as signaling pathways, protein cellular localization and post-translational modifications –4. The iAs in other areas of molecular modeling, it is very difficult to obtain reliable predictions by computational protein–peptide docking when prior knowledge of the interactions is not available. When there is no information on the binding site, putative binding sites must be searched for on the entire surface of the target protein. Such global docking methods show limited accuracy for predicting high-resolution complex structures, but successful predictions of at least part of the binding residues have been reported –11. WhenAs increasing number of protein–peptide complex structures are being deposited in the protein data bank, the probability of finding protein–peptide complexes similar to a given target complex in the structure database increases. For example, 87% of the non-redundant protein–peptide complexes in the PeptiDB set  have simThe GalaxyPepDock server presented in this paper utilizes information on protein–peptide interactions of similar proteins in the database of experimentally determined structures to generate high-resolution complex structures when reasonable template protein–peptide complex structures can be found. A further refinement by GALAXY energy-based optimization –24 enablhttp://www.ebi.ac.uk/msd-srv/capri/round28/round28.html) for 37 of the 57 PeptiDB targets, compared with the 9 targets returned by PEP-SiteFinder. A similar level of improvement by GalaxyPepDock was also observed when tested on 22 recently released protein–peptide complex structures. When tested on the CAPRI target 67, predictions of medium accuracy were made; this accuracy is among the best predictions made by human groups and superior to the best server predictions submitted during the CAPRI blind prediction experiment. For this target, the conformational change of the protein by peptide binding was also correctly predicted.GalaxyPepDock identified 75.4% of the binding site residues on average, compared with 66.2 and 40.9% by PEP-SiteFinder  and PepSThe overall procedure of template selection and model-building is illustrated in Figure ZTM measures the protein structure similarity by the Z-score of the TM-score of a database protein structure when aligned to the target protein structure by TM-align  for 37 of the 57 PeptiDB targets, compared with the 9 targets returned by PEP-SiteFinder (see Supplementary Tables S2 and S3 for details).The non-redundant set of PeptiDB  was firsIt should be emphasized that the flexible-structure energy-based model-building procedure of GalaxyPepDock can improve the predictions beyond that of a simple method that superimpose the target onto the template structure. The improvement in prediction accuracy achieved by additional energy optimization compared with the template superimposition method can be observed from the increased number of high-accuracy/medium-accuracy/acceptable predictions from 5/22/36 to 6/27/37 and the improved median ligand-RMSD/interface-RMSD/(fraction of native contact) values from 3.0 Å/1.6 Å/0.571 to 2.8 Å/1.3 Å/0.667.For an independent test, protein–peptide complexes involving 5- to 15-residue peptides and with available unbound protein structures that share ≤70% sequence identity were selected among recently released structures (released after 2009). This set consists of 22 complexes. On this set, GalaxyPepDock showed a similar level of improvement, producing structures with better than acceptable quality for 17 of the 22 targets, compared with 5 returned by PEP-SiteFinder (see Supplementary Tables S4 and S5 for details). The number of high-accuracy/medium-accuracy/acceptable predictions was increased from 0/8/15 to 0/9/17 and median ligand-RMSD/interface-RMSD/(fraction of native contact) values were improved from 3.1 Å/1.8 Å/0.588 to 2.5 Å/1.6 Å/0.693 by energy optimization.GalaxyPepDock was also tested on the CAPRI target 67 (PDB ID: 4N7H), and a medium-accuracy prediction was made (see Supplementary Table S6). Compared with template-superimposed models, the quality of the model was improved by energy optimization from acceptable to medium accuracy, with improvements in ligand-RMSD/interface-RMSD/(fraction of native contact) values from 2.9 Å/1.5 Å/0.500 to 1.8 Å/1.0 Å/0.688. In the CAPRI blind prediction experiment, no group submitted high-accuracy models for this target, and 6 of the 44 registered groups submitted medium-accuracy models. The best server predictions were only of acceptable quality. The ‘Seok’ group submitted models only of acceptable quality because no refinement procedure was applied at the time.http://biasmv.github.io/pv/) is used for the visualization of the predicted models.The GalaxyPepDock server runs on a cluster of 12 Linux servers of 2.33-GHz Intel Xeon 8-core processors. The web application uses the Python programming language and the MySQL database. The protein–peptide docking pipeline is implemented using Python. The protein–peptide docking algorithm is implemented in the GALAXY program package ,31 writtSInter) obtained by a linear regression of the PeptiDB test set results. For targets with a low estimated accuracy close to 0, it is suggested that ab initio docking servers such as PEP-SiteFinder are tried. A sample output page is shown in Figure The required input is a protein structure in PDB format and a peptide sequence in FASTA format. The size of the target protein and peptide is limited to 900 and 30 amino acids, respectively, for computational efficiency. The average run time is 2–3 h. Ten model structures can be viewed and downloaded from the website, and additional information on the predicted binding sites and estimated accuracy of the predicted interactions is provided. The prediction accuracy is estimated from a linear model of the relationship between the fraction of correctly predicted binding site residues and the template-target similarity (measured by the protein TM-score and GalaxyPepDock is a similarity-based protein–peptide docking web server that performs additional flexible-structure energy-based optimization. The effective combination of database search and physics-based optimization allows for a superior performance compared with the existing methods when complexes involving similar proteins can be found in the database.Supplementary Data are available at NAR Online.


Protein interactions mediate a wide spectrum of functions in various cellular contexts. Functional versatility of protein complexes is due to a broad range of structural adaptations that determine their binding affinity, the number of interaction sites, and the lifetime. In terms of stability it has become customary to distinguish between obligate and non-obligate interactions dependent on whether or not the protomers can exist independently. In terms of spatio-temporal control protein interactions can be either simultaneously possible (SP) or mutually exclusive (ME). In the former case a network hub interacts with several proteins at the same time, offering each of them a separate interface, while in the latter case the hub interacts with its partners one at a time via the same binding site. So far different types of interactions were distinguished based on the properties of the corresponding binding interfaces derived from known three-dimensional structures of protein complexes.Here we present PiType, an accurate 3D structure-independent computational method for classifying protein interactions into simultaneously possible (SP) and mutually exclusive (ME) as well as into obligate and non-obligate. Our classifier exploits features of the binding partners predicted from amino acid sequence, their functional similarity, and network topology. We find that the constituents of non-obligate complexes possess a higher degree of structural disorder, more short linear motifs, and lower functional similarity compared to obligate interaction partners while SP and ME interactions are characterized by significant differences in network topology. Each interaction type is associated with a distinct set of biological functions. Moreover, interactions within multi-protein complexes tend to be enriched in one type of interactions.http://webclu.bio.wzw.tum.de/PiType/PiType.zip.PiType does not rely on atomic structures and is thus suitable for characterizing proteome-wide interaction datasets. It can also be used to identify sub-modules within protein complexes. PiType is available for download as a self-installing package from  Detailed protein interaction maps derived for many important model organisms  have becSo far most of the mechanistic insights into the nature of protein interactions came from high-resolution structures of protein complexes ,3. One iProtein complexes can also be subdivided into two classes based on their binding affinity and lifetime. Constituents of permanent interactions, such as enzyme-inhibitor or antibody-antigen complexes, are only found in bound state while transient interactions, usually involved in intracellular signaling, are short-lived and readily associate and dissociate . InteracSeveral machine learning methods have been proposed to automatically classify protein complexes with known three-dimensional structure into various types based on physical, chemical, geometrical, and evolutionary properties of protein recognition sites -20. For Protein interactions can also be classified into two types based on their timing and the spatial distribution of binding sites on the protein surface. Products of co-expressed genes  may formSo far efforts to classify and predict protein interaction types have exploited structural information and are thus only applicable to the minor part of the currently known interactome for which atomic structures of protein complexes are available. Here we present the first attempt to classify protein interactions without reliance on 3D structures. We have devised an accurate prediction technique, called PiType, which is able to distinguish obligate from non-obligate interactions and SP from ME interactions based on readily accessible features, including sequence and functional properties of the two binding partners and their network context. We apply PiType to large-scale protein interaction data and investigate the cross-talk between SP/ME and obligate/non-obligate interactions.Homo sapiens, Escherichia coli, and Saccharomyces cerevisiae were extracted from the Uniprot database [Protein sequences and associated annotation for database  based oni.e. converted to a list of PDB IDs corresponding to Uniprot IDs) and then merged with the PDB-to-Uniprot mapping. All PDB chain IDs that corresponded to more than one Uniprot ID were removed, but we allowed an Uniprot ID to be mapped to several different PDB chain IDs.To establish the correspondence between known three-dimensional structures and the protein sequences in our dataset we used both the Uniprot-to-PDB mapping available from the Uniprot ftp site and the PDB-to-Uniprot mapping available through the PDB  SOAP serGene ontology  assignmeE. coli and is hence insufficient for our study.There are two well-known manually curated datasets of protein interaction types created by Zhu et al.  and MintE. coli, yeast and human complexes by a structure based classifier, NOXclass [We therefore created a larger dataset by predicting the interaction type of NOXclass . NOXclasNOXclass  while thNOXclass .vs non-obligate complexes. To generate our dataset we accepted only those protein chains for which NOXclass produced confidence values of at least 90% at both stages. The NOXclass predictions were subsequently merged with the manually annotated interactions from the Zhu dataset. In total we obtained 773 protein protein interactions with known or reliably predicted interaction type  were ignored. If a PDB entry contained more than two chains we considered all possible chain combinations and classified them using NOXclass. Two confidence values were obtained for each pair of protein chains - one for classifying this chain pair as a biological assembly or a crystal artifact, and another one for obligate pe Table .The Structural Interaction Network 2.0  (SIN) coEscherichia coli were obtained from the iRefIndex 9.0 meta-database [Protein interaction data for yeast, human, and database  which stdatabase ,33-36. We in a graph g. Graphlets are small, connected, induced subgraphs of a larger network . The local topology of an edge e can be determined by counting how often e is contained in all graphlets of size two to five in g. Moreover, one has to differentiate at which position e resides in a graphlet. For example, there are two distal edges at both ends of the graphlet G3 and as well as one edge in the middle. To distinguish between such cases the symmetry of each edge is described by its atomorphism orbits. There is a total of 69 orbits, numbered 0 to 68. However, the orbit 0 consists of just one edge connecting two nodes. Since each edge in g touches this orbit exactly once (namely itself), it is not considered while calculating EGDV. We used a modified version of the FANMOD algorithm [e   as a metk Figure . In thislgorithm  to find e Figure  and dete package . Since v2), orbit 12 within the 4-node clique (G8), and orbit 68 within the 5-node clique (G29). This over-representation of certain orbits is the consequence of the large amount of combinatorial occurrences of different graphlets in tightly connected network clusters. For example, a fully connected 10-node clique, in which each node is connected to each other node, will contain the 3, 4, and 5 node sub-cliques exactly We sought to identify the preferred network contexts for protein interactions of different types. To this end we investigated the enrichment of orbits in two specific local topological patterns - clusters and hubs. Edges constituting a highly connected sub-graph (cluster) would be expected to be enriched in orbits situated inside cliques, such as 2, 12, 8, 25, 52, and 68  that lie within cliques even if the associated graphlet includes further orbits that do not belong to any clique. For example, the graphlet Gi.e. their neighbors are sparsely connected. Edges incident to a hub are thus unlikely to form cliques.Edges connecting hub nodes and non-hub nodes, as well as those connecting two different hub nodes, are primarily associated with orbits 1, 5, and 18. The reason for this is that hubs tend to have a high degree and a low cluster coefficient, Finally, another crucially important type of network nodes are bottlenecks, which come in two flavors: hub-bottlenecks and nonhub-bottlenecks . Hub-bothttp://gaussian.bu.edu/pnns.html that describes the closeness of two nodes on the network. It was developed to determine whether or not two nodes share the same graph cluster. Since we expect that obligate interactions will tend to share a cluster the PageRank affinity score may be instrumental in separating non-obligate interactions from the obligate ones.We calculated the PageRank Affinity score http://gahttp://cran.r-project.org/web/packages/igraph/index.html to calculate edge betweenness.Betweenness  of an edge is defined by the number of shortest paths passing through that edge. Since non-obligate interactions are typically involved in signal transduction pathways they would be expected to reside on shortest paths more frequently than obligate interactions. We used the igraph R package http://cre is the number of edges that share at least one node with e. In other words the degree of an edge between node v1 and node v2 is the number of edges that have at least v1 or v2 as a node.The degree of an edge http://elm.eu.org/infos/news.html and searched in each protein sequence for all occurrences of each ELM. Hence each interaction was characterized by two integer values giving the numbers of ELMs found in both interaction partners.It has been suggested that interactions in eukaryotic organisms that are mediated by short linear sequence motifs tend to be non-obligate . To deteWe predicted disordered binding regions for interacting protein pairs by ANCHOR  and conshttp://www.bioconductor.org/packages/2.4/bioc/html/GOSemSim.html. This method describes the similarity between two GO terms based on their location in the GO graph. To calculate the functional similarity between a protein A having GO terms GO1, ..., GOi and a protein B with GO1, ..., GOj all i GO terms of A are compared with all j GO terms of B, yielding a matrix m with i rows j columns corresponding to GO terms of A and B, respectively. Functional similarity between A and B is then the mean over the maxima of each row and column of m:Functional similarity between two proteins was calculated based on their associated Gene Ontology (GO) annotation  using thhttp://www.cs.waikato.ac.nz/ml/weka, v. 3.6.6) and its java API for feature selection and classification.In this section we describe the applied machine learning methods  . These ic calculated asWe were interested to find out whether network clusters corresponding to protein complexes are enriched in a certain interaction type  or are rather a mixture of different interaction types. Such enrichment was assessed based on the information content of a protein complex H(c) denotes the Shannon entropywhere and P the frequency of interaction type t in the protein complex. The information content value ranges between zero and two bits, where a value of two means that all interactions in the protein complex are of the same type and a value of zero indicates that each protein interaction type is equally represented in the protein complex.vs non-obligate interactions and SP vs ME interactions  were 0.851, 0.657, 0.806, and 0.808 for all features, for functional similarity, sequence features, and network features, respectively. Using either disordered features or ELM features separately we achieved an auROC of 0.75 and 0.66, respectively. However, when both disordered and ELM features were utilized the auROC was substantially higher - 0.806 - underlying the importance of cross-talk between these two groups of biological properties.We preformed an extensive analysis and benchmarking of both the obligate/non-obligate and SP/ME classifiers, which can be found in the Additional file We applied our method to classify 13978 HeLa and 83788 iRefIndex protein interactions as either obligate or non-obligate as well as either SP or ME. Each interaction was also attributed to one of the four class combinations - obligate and SP, obligate and ME, non-obligate and SP, or non-obligate and ME - and assigned two confidence values - one for the SP/ME classification and one for the obligate/non-obligate classification.We analyzed the number of classified interactions for each class and class combination for various random forest confidence values Figure , 6. NoteWhat is the optimal threshold for the random forest confidence values? As seen in Figure For random forest confidence values >= 0.6 the total of 638 and 506 HeLa interactions as well as 1747 and 2620 iRefIndex interactions were classified as SP/obligate and SP/non-obligate, respectively. The total of 1010 and 6118 HeLa interactions were classified as ME/obligate and ME/non-obligate, respectively, while for the iRefIndex dataset the corresponding numbers were 1772 and 54580.It was recently suggested that SP interactions are mostly permanent and ME interactions are mostly transient . As discWe also evaluated the classification results for iRefIndex interactions measured by different experimental methods, focusing on yeast two hybrid (Y2H) essay and tandem affinity purification (TAP). It was suggested that TAP has a preference for detecting obligate interactions while Y2H has no bias towards obligate or non-obligate interactions . Indeed,We further applied our method to classify all intra- and inter-complex interactions in the CORUM and HeLa datasets (see section Protein complex data). The overlap between different class combinations in terms of GO categories associated with them is very low, implying that each interaction type is intrinsic for a distinct set of cellular functions  complex (HeLa ID 587, Figure We defined a protein cluster to be enriched in a given interaction type when it constituted at least 50% of the intra-complex interactions and plotted the fraction protein complexes enriched in each interaction type Figure . Both inhttp://webclu.bio.wzw.tum.de/PiType/PiType.zip.We report PiType, a novel technique for classifying protein interactions into obligate/non-obligate as well as into SP/ME based exclusively on sequence and network information. In contrast to previous work that relied on known 3D structures of proteins our method is suitable for large-scale characterization of interaction data. In particular it can be applied to improve protein complex prediction. Its performance is comparable with that of the structure-based classifiers, achieving an auROC of at least 80% and a F-measure close to 80% in a nested cross-fold validation. PiType is available at We have conducted a thorough investigation of sequence and network features characteristic for each type of interactions. Proteins involved in non-obligate interactions tend to have more disordered regions and short linear eukaryotic motifs than obligate interactors. Non-obligate interaction partners are also less functionally similar than obligate interaction partners. These results are in line with previous observations  note th are moreAPR: PageRank affinity; auROC: area under receiver operating curve; EGDV: edge graphlet degree vector; ELM: short linear eukaryotic motifs; GO: gene ontology; KNN: k nearest neighbors; MCM: mini-chromosome maintenance; ME: mutually exclusive; PR: precision-recall; ROC: receiver operating characteristic; SIN: structural interaction network; SP: simultaneously possible; SVM: support vector machine; TAP: tandem affinity purification; Y2H: yeast two hybrid.F.G. and D.F. jointly conceived the study. F.G. carried out the analysis of the data. D.F. supervised the work. F.G. and D.F. wrote and revised the manuscript.The authors declare that they have no competing interests.a Definition for raw interactions and non-redundant interactions are taken from http://wiki.thebiogrid.org/doku.php/statistics.Bottom 40 ranked features for both SP/ME classification and obligate/non-obligate classifications.Click here for fileBenchmarking methods for both SP/ME classification and obligate/non-obligate classifications.Click here for fileBenchmarking results for both SP/ME classification and obligate/non-obligate classifications.Click here for fileGO enrichment results.Click here for file