Protein-protein interactions (PPIs) play a key role in understanding the mechanisms of cellular processes. The availability of interactome data has catalyzed the development of computational approaches to elucidate functional behaviors of proteins on a system level. Gene Ontology (GO) and its annotations are a significant resource for functional characterization of proteins. Because of wide coverage, GO data have often been adopted as a benchmark for protein function prediction on the genomic scale.We propose a computational approach, called M-Finder, for functional association pattern mining. This method employs semantic analytics to integrate the genome-wide PPIs with GO data. We also introduce an interactive web application tool that visualizes a functional association network linked to a protein specified by a user. The proposed approach comprises two major components. First, the PPIs that have been generated by high-throughput methods are weighted in terms of their functional consistency using GO and its annotations. We assess two advanced semantic similarity metrics which quantify the functional association level of each interacting protein pair. We demonstrate that these measures outperform the other existing methods by evaluating their agreement to other biological features, such as sequence similarity, the presence of common Pfam domains, and core PPIs. Second, the information flow-based algorithm is employed to discover a set of proteins functionally associated with the protein in a query and their links efficiently. This algorithm reconstructs a functional association network of the query protein. The output network size can be flexibly determined by parameters.http://bionet.ecs.baylor.edu/mfinderM-Finder provides a useful framework to investigate functional association patterns with any protein. This software will also allow users to perform further systematic analysis of a set of proteins for any specific function. It is available online at  PPI data have a central role in understanding functional behavior of proteins. Recent high-throughput techniques ,2 have gIn the last decade, a wide range of computational algorithms have been introduced to predict protein complexes or functional modules from genome-wide PPI networks -12. A fuA previous study  proposedSecond, the core of functional association pattern mining is an information flow-based algorithm that runs on the weighted genome-wide PPI network. When a protein is given by a user, this algorithm generates a group of proteins functionally associated with the protein and their functional links by random walk simulation. Since this algorithm has the advantage of being remarkably efficient, it is well-applicable to web-based tools. In this article, we introduce web application software, called M-Finder, that reconstructs a functional association network from a protein specified by a user. This interactive web-based tool takes a protein entered by a user in a query (using systematic names or gene symbols as protein identifiers) and visualizes a network generated by dynamic information propagation starting from the query protein. The visualized network represents a functional linkage pattern associated with the protein of interest. The output network size can be flexibly determined by the parameters that a user specifies. Moreover, M-Finder provides detailed ontological and experimental information of each interactor and each interaction, which can be obtained through the hyperlinks on the visualized networks. M-Finder will allow users to characterize functional mechanisms of proteins on the genomic scale in a systematic perspective.An ontology provides well-defined, structured and computable semantics of domain knowledge . Becauset1 and t2 having the annotation of two proteins of interest, respectively. First, edge-based methods explore the paths between GO terms in a DAG (Directed Acyclic Graph) structure of GO. For instance, we can compute the shortest path length between t1 and t2. Since each ontology has a different scale, the shortest path length between two terms can be normalized by the ontology depth, i.e., the greatest length among the shortest paths from the root to leaf terms. Another example in this category is to measure the depth to the most specific common ancestor term  of t1 and t2. The greater depth to SCA indicates higher semantic similarity between t1 and t2. This method can be normalized by the average depth to the individual GO terms, t1 and t2. However, it has been observed that these methods are not appropriate for assessing functional similarity of proteins because GO has inherent complex relationships among GO terms and it cannot be guaranteed that the edges in GO represent the same quantity of specificity.Suppose we measure the semantic similarity between two GO terms t1 and t2, respectively [t1 and t2. This approach can be normalized by the union of two ancestor term sets of t1 and t2, called simUI [Second, node-based methods measure the overlap between two sets of ancestor terms of ectively . The greed simUI .C(t1) is a set of all ancestor terms of t1.where c is defined as the negative log likelihood of c. In the same manner, information content of a GO term i tcan be described as - log P (it), where P (it) is the proportion of annotating proteins to it. In order to make information contents of any GO terms represent their specificity, if a protein is annotated to it, we should annotate the protein to all ancestor terms of i ton the paths towards the root. Then, the smaller number of annotating proteins a GO term has , the more specific it is. The root term of GO is the least specific because it has the maximum number of annotating proteins. Resnik's method [t1 and t2 by the greatest information content of common ancestor terms of t1 and t2. In other words, this method estimates the specificity of SCA.Third, annotation-based methods utilize the number of annotating proteins on each GO term to infer the term specificity. In Information Theory, information content of a concept s method  computesC is a set of all common ancestor terms of t1 and t2. Lin [t1 and t2.where  t2. Lin  proposedt1 and t2, and measure the semantic similarity between t1 and t2 by the inverse of the differences. The smaller the differences of the information contents between SCA and t1 and between SCA and t2, the more similar t1 and t2.Jiang and Conrath  calculatt1 and t2 converge, multiple SCAs of t1 and t2 generally occur in a DAG structure since each GO term has multiple parent terms. Couto et al. [t1 and t2 as common disjunctive ancestors. They proposed add-on semantic similarity methods, GraSM which averages the information contents of common disjunctive ancestor terms and DiShln which is a slight modification of GraSM [Schlicker et al.  proposedo et al.  defined of GraSM .Finally, many integrative approaches of two different categories have recently been proposed to achieve higher accuracy in measuring functional similarity of proteins. Wang et al.  introducC(t1) is a set of all ancestor terms of t1. IntelliGO [where ntelliGO  integratntelliGO  proposedPrevious experiments ,34 have p1 and p2, should be derived from multiple semantic similarity scores between two sets of GO terms, S1 and S2. Three different ways are commonly used for aggregating semantic similarity scores between pairwise combinations of the GO terms having the annotation of p1 and p2: averaging, maximum, and the best-match averaging (BMA). The averaging method is to compute the average semantic similarity score of all possible pairwise combinations of the terms in S1 and S2.Our aim is to quantify functional similarity between two proteins using semantic similarity scores of GO terms. Since each protein is typically annotated to multiple GO terms, a single semantic similarity score between two proteins, The maximum method is to select the maximal semantic similarity score out of them.Finally, we can compute the average of all pairwise best-matches  such as:Previous studies have also observed that the BMA approach is the best for estimating functional similarity between two proteins which perform multiple functions.t1 and t2, not a difference between them. In contrast, Lin's and Jiang's methods measure their difference only. In particular, Lin's model reflects a significant bias towards higher scores when the set of annotating proteins on SCA is similar to those on t1 and t2. This case commonly occurs in GO because of the shallow annotation problem [t1 and t2 can be normalized by their distance. Two integrative approaches, simICNP and simICND, presented in [t1 and t2 as the normalization factor.Additional improvements of semantic similarity measurement by integrating two orthogonal features have been proposed. Resnik's method focuses on the commonality of two GO terms  problem . To enhaented in , use theandt0 is SCA of t1 and t2 which has the greatest information content among their common ancestor terms. The normalization factor of simICNP is the path length between t1 and t2 in the ontology, whereas that of simICND is the difference of information contents of t1 and t2. simICNP works better when the ontology has precise information of relationships between specific terms. In contrast, simICND has a better performance when specific terms in the ontology have a sufficient amount of annotations. Therefore, simICND and other annotation-based semantic similarities such as Resnik's method and Lin's method have high accuracy of measuring functional consistency between two proteins for well-studied model organisms. However, their weakness is low accuracy for rarely-studied organisms.where In this study, we make a complete evaluation of simICNP, simICND and other competing semantic similarity methods in a biological perspective, as shown in the next section. For both simICNP and simICND, we use the BMA approach in Formula 1 to achieve functional similarity scores of all interacting protein pairs.To discover the functional associations  of a given protein, we apply the information flow algorithm, presented in , to the This approach allows to set a parameter to terminate the information flow on a path. As information flows continuously through the links, the algorithm generates monotonically decreasing functional influence scores according to the path strength model. When the score is lower than a user-specified threshold, the flow stops on the specific path. The algorithm finally terminates when any link does not have a flow. The lower the threshold, the longer the algorithm runs.This approach requires additional parameter to return a functional association network which represents a subgraph of the genome-wide PPI network. When the information flow terminates, the algorithm collects the proteins and their links whose functional influence scores are greater than a user-specified threshold. This threshold thus determines the size of the generated network. As the threshold decreases, the algorithm returns a larger functional association network.Following the underlying idea that functionally related proteins present common physical attributes, semantic similarity measures are usually assessed by evaluating their agreement to other biological features, such as sequence similarity, the presence of common domains, and protein-protein interactions . We emplS. cerevisiae. The selected protein pairs are annotated not only to GO terms but also in Pfam and KEGG databases. For each feature, a quantitative similarity score is calculated for each protein pair. The Pearson correlation is then used to evaluate the agreement between semantic similarity and the other features on the whole data set. The higher the correlation, the better the tested measure. It has been pointed out that, in general, the relationship between sequence and semantic similarity is not linear, and therefore Pearson correlation might not be the best measure to assess their agreement. Indeed, in the comparison with sequence similarity, CESSM also considers resolution as a quality measure [CESSM  is a rea measure . IntuitiTo score the semantic similarities of selected protein pairs, we used two ontologies in GO, biological process (BP) and molecular function (MF), separately. GO annotation data have been collected by published results from various high-throughput approaches including both experimental and computational analysis. GO provides evidence codes to indicate the types of methods that create the annotation. All evidence codes have been curated manually with the exception of Inferred from Electronic Annotation (IEA). We thus tested two different sets of semantic similarity scores measured with and without IEA annotations, respectively.Table The results of semantic similarities measured in BP ontology with and without IEA annotations are shown in Table The results of semantic similarities measured in MF ontology with and without IEA annotations are shown in Table P of interacting protein pairs and a negative set N of non-interacting protein pairs, semantic similarity measures can be compared in terms of their ability to divide interacting and non-interacting protein pairs.It has been verified that semantic similarity is also a good predictor of PPIs. The rationale behind this is that interacting protein pairs are likely to be involved in similar biological processes or molecular functions, and therefore should present higher values of semantic similarity than non-interacting protein pairs. Thus, given a positive set S. cerevisiae with different characteristics. First, a small, high quality positive set P1 of 11,936 interactions has been extracted from Hint [P2. I2D is a collection of interactions derived from several databases, and currently counts 147k interactions. The negative sets N1 and N2 were built by randomly selecting |P1| and |P2| protein pairs, respectively, not present in the iRefIndex [k, a linear separator predicts as interacting (I) all the protein pairs in P and N with the semantic similarity scores above k, and labels all the other protein pairs as non-interacting (nI).We validated simICND and simICNP on two PPI data sets of rom Hint , a databrom Hint  network RefIndex  data set|P ∩ I|/|P |) and specificity (|N ∩ nI|/|N |). The former, also called a true-positive rate, is the fraction of protein pairs in P whose scores are above the threshold. The latter, also called a true-negative rate, measures the fraction of proteins pairs in N whose scores are below the threshold. A false-positive rate is then calculated by 1-specificity. The true-positive and false-positive rates at different cut-off thresholds are collected and incorporated into a receiver operating characteristics (ROC) curve which is frequently used to evaluate prediction performance on a broad range of cut-off thresholds.Two significant indicators to compare the performance of different semantic similarity measures are sensitivity  We made this threshold value variable, and examined how accuracy of our approach changes as the threshold changes.The performance of our functional association mining approach can be validated by comparing the outcome to functional modules. The genome-wide PPI data of several model organisms are publicly available from many open databases such as BioGRID , IntAct Finally, 1,000 resultant functional association sub-networks were compared to functional modules. We used FunCat data from MIPS  as the fFigure The functional modules in FunCat are organized in a hierarchical tree structures. The first-level  modules indicate the most general descriptions of cellular functions whereas the bottom-level includes the most specific descriptions of cellular functions. We extracted the modules from the top three levels of the tree structure. The average precision of the results comparing to the functional modules on the three different levels is also shown in Figure We introduce the interactive web application software, called M-Finder, to analyze functional associations  from any protein of interest. This web-based tool is designed as an interactive system which enables a user to enter any protein in a query, choose a semantic similarity method, and specify the minimum association threshold as a parameter. Then the information flow algorithm, embedded in this tool, runs with the user inputs on the up-to-date genome-scale PPI network with edge weights, and the generated functional sub-network associated with the query protein is visualized.S. cerevisiae only, but it will be extended to C. elegans, D. melanogaster and H. sapiens in near future.The PPI data set is regularly updated with the most recent version from BioGRID . The PPISince the information flow algorithm has a very quick response time  even on a large-scale network with complex connectivity, it is suitable for this interactive web-based tool. The threshold to halt the information flow was hard-coded as 0.01 in this tool because we observed this threshold is not sensitive to the result. However, the threshold to select the proteins and their links for a final functional association network should be a parameter that users can enter. The default of this threshold is 20, but it should decrease if a larger functional association network is needed.Cytoscape Web  is used M-Finder has further special features. Users can download the output functional association networks to any image files. Users can also search additional information of proteins and interactions on the output functional association network through hyperlinks. For example, ontological information related to the visualized proteins is provided.This article presented a novel computational method to analyze functional association patterns related to a user-specified protein in a query. This approach adopts the integration of interactome data and GO annotations, and the information flow algorithm reconstructs a functional association network linked to the query protein, which is a small subgraph of the genome-wide PPI network. As discussed in Introduction, a variety of graph clustering algorithms have been applied to detect functional modules from PPI networks. These graph clustering algorithms mostly search densely connected subgraphs assuming that proteins interact to perform a cellular function. However, listing all the clusters, i.e. the sets of proteins, in the genomic scale is not meaningful for genetic studies to characterize a specific gene or a specific function. The proposed approach is thus unique in that it investigates the patterns of potential functional linkage associated with a specific protein of interest. The introduced web application software to analyze and visualize functional association networks would be geared specifically to the needs of systematic and quantitative results in genetic studies.This study has two significant contributions to current bioinformatics. First, biological data integration is increasingly demanding as an early stage of current data-intensive bioinformatics research. The automated high-throughput technologies have made rapid generation of large-scale data. However, as a downside, they decrease reliability of the data sets. It has been observed that the interactome data currently available in open databases include a large number of false positives, i.e., the spurious interactions which do not occur within a living cell. Although the high-throughput methods have produced interaction data over the entire genome scale, it is expected that there still exist an extremely large amount of false negatives across several model organisms, i.e., the actual interactions that have not been determined yet. The reliability of interaction data can be assessed by inspecting other resources which enable us to judge the feasibility of functional association between genes, such as gene expression profiles. We suggested, in this study, the integration of ontological data for filtering the interactome. We made a complete evaluation of recently proposed two integrative methods of semantic similarity, simICNP and simICND. The CESSM test and PPI test results demonstrated that the proposed approaches outperform the previous methods in terms of measuring functional closeness of two proteins. Our ontological data integration model would provide an effective framework for curation of genome, transcriptome, proteome and interactome data.Second, efficiency and scalability are key issues on the large-scale, complex interactome data mining. A single protein influences multiple phenotypes in different environmental conditions, known as the pleiotropic effect. When separating the conditions is disregarded, interaction networks are typically structured by complex connectivity. Moreover, the scale of the interactome data increases remarkably for higher-level organisms in evolution. The proposed approach is formulated based off a data-mining technique which is implemented efficiently on large-scale networks with complex connectivity. Our heuristic model enables us to search functional associations very efficiently by simulating random walks. This efficient and scalable approach would be generalized to any integrative analysis of complex systems. This would also be the best fit to be embedded into a web application tool introduced in this article. For further improvement of the tool MFinder in terms of efficiency and accuracy, we can explore the inherent topological properties of the genome-wide PPI networks. It might be feasible to predict and suggest the best parameter value for a specific species by analyzing the properties.The authors declare that they have no competing interests.YRC coordinated the project, developed the methods, and drafted the manuscript. MM implemented semantic similarity evaluation and wrote the manuscript partially. YL developed application software, M-Finder. NK collected and curated PPI data. PHG designed semantic similarity evaluation. All authors revised and confirmed the manuscript.


Ricinus communis is an industrially important non-edible oil seed crop, native to tropical and subtropical regions of the world. Although, R. communis genome was assembled in 4X draft by JCVI, and is predicted to contain 31,221 proteins, the function of most of the genes remains to be elucidated. A large amount of information of different aspects of the biology of R. communis is available, but most of the data are scattered one not easily accessible. Therefore a comprehensive resource on Castor, Castor DB, is required to facilitate research on this important plant.R. communis, integrating information from several diverse resources. CastorDB contains information on gene and protein sequences, gene expression and gene ontology annotation of protein sequences obtained from a variety of repositories, as primary data. In addition, computational analysis was used to predict cellular localization, domains, pathways, protein-protein interactions, sumoylation sites and biochemical properties and has been included as derived data. This database has an intuitive user interface that prompts the user to explore various possible information resources available on a given gene or a protein.CastorDB is a specialized and comprehensive database for the oil seed plant CastorDB provides a user friendly comprehensive resource on castor with particular emphasis on its genome, transcriptome, and proteome and on protein domains, pathways, protein localization, presence of sumoylation sites, expression data and protein interacting partners. Ricinus communis (Euphorbiaceae family) is an industrially important non-edible oil seed crop with several well established applications in industry. Castor bean genome is around 350 Mb and was sequenced and assembled in 4X draft by Chan et al. [R. communis. Sequence data of Castor bean plant was obtained from various resources like National Center for Biotechnology Information (NCBI) [R. communis with information on protein domains, biosynthetic pathways, protein localization, and presence of sumoylation sites, gene expression data, and information on interaction between proteins. CastorDB not only provides researchers an opportunity to extract detailed biological information on any specific gene or protein from a single resource but also prompts the researcher to use the information to explore new information that is becoming available in plant genomics.n et al.  using whn (NCBI)  and JCVIR. communis was downloaded from JCVI Castor Genome database [Sequence information on 31,221 proteins and genes of R. communis were obtained from dbEST. Each EST sequence was mapped on genes by performing nucleotide BLAST [R. communis with e-value cutoff 10-6.dbEST  is a divde BLAST  against R. communis proteins were mapped with gene ontology information on the basis of GO annotation available for Pfam domains from Gene Ontology database [R. communis.database ,8. The mdatabase . 11847 pR. communis proteins localization was generated using the Wolf-PSORT [Prediction of the lf-PSORT , SignalPlf-PSORT ,12 and Tlf-PSORT ,14 progrlf-PSORT  program,R. communis protein sequences. Pfam, a large collection of multiple sequence alignments and Hidden Markov Models covering many common protein domains and families, has two parts; Pfam-A (a curated database with 9318 protein families) and Pfam-B, which contain large number of small families taken from PRODOM database [R. communis protein sequences were scanned for probable domains using pfam_scan program with an E-value cut-off of 10-3. A total of 3546 domains were found for 18445 protein sequences, information for which is incorporated in CastorDB. Top 10 high frequency domains are shown in Figure Pfam  databasedatabase  that do R. communis protein sequences were predicted by using KEGG Pathway database [R. communis proteins  were compared to the Swiss-Prot database [-6. Each query protein sequence from R. communis was assigned probable pathways based on pathway information available from KEGG database for their homologous protein sequences in other species. A total of 112 probable pathways were predicted for 3785 Castor bean proteins. All predicted pathways were manually checked to remove false positives from the prediction result.Putative pathways for the database . KEGG PAdatabase ,20 usingeins 31,2 were comteins 31,1 were coR. communis were predicted using interaction information protein interaction for Arabidopsis thaliana from Arabidopsis thaliana Protein Interactome Database (AtPID) [Arabidopsis thaliana proteome. The Protein-protein interaction pairs in AtPID are predicted by integrating several methods with the Naive Baysian Classifier. Proteins from R. communis were BLAST against the Arabidopsis thaliana protein sequences obtained from The Arabidopsis Information Resource (TAIR) [-6. The R. communis proteins which were predicted to show similar domain architecture (i.e. same domains) to that of homologue proteins from A. thaliana were only selected for further predicting probable interacting protein pairs. A total of 33,000 interacting protein pairs were predicted during the analysis. Schematic diagram showing prediction of protein-protein interaction in R. communis is shown in Figure Probable protein-protein interactions in  (AtPID) . The AtPe (TAIR)  and viceRicinus communis proteins were predicted using SUMOsp 2.0 [R. communis at a high cut-off value.Putative sumoylation sites in MOsp 2.0  softwareR. communis proteins. Pepstats calculated molecular weight, isoelectric point, charge, size of protein, extinction coefficient and average residue weight for all the proteins in R. communis.Biochemical properties of the protein sequences were calculated using Pepstats program from European Molecular Biology Open Software Suite (EMBOSS) package . PepstatR. communis protein sequence in NCBI [-10 against protein sequence dataset obtained from NCBI and KEGG using keyword Ricinus communis. The hit with maximum identity and lowest e-value was selected as best homologue.In order to find the best homologue for  in NCBI  and KEGG in NCBI , protein in NCBI  was perfThe architecture and design of CastorDB Figure  consistsGraphical interface provides the user access to CastorDB using various input queries and provide links to additional information pages which guide the user during browsing of CastorDB. The query inputs from user interface are sent to program and scripts in layer T2 via post method.T2 consists of Apache web server  for WindThe Relational Database Management System MySQL  was usedCastorDB provides access to explore the stored information by three different kinds of search methods: (i) Simple Search (ii) Advanced Search (iii) BLAST Search using protein or nucleotide sequence.This feature of CastorDB allows user to browse database by inputting keyword for selected query option. There are seven query options Figure  which acThis mode of searching CastorDB allows user to combine multiple queries with one another. Database can be searched in multiple dimensions looking for records which satisfy the given conditions for all combined queries. For example: Query can be generated to search for genes having at EST's from leaves, involved in glycolysis pathway and localized in chloroplast of cell. Similarly many other queries can be generated using available options Figure .R. communis generated using formatdb from standalone BLAST package. The result table generated after running the program display BLAST hits sorted according to percent identity in descending order.BLAST ,21 basedInformation section for selected gene provides information about Domains along with image generated using Domain Image Generator program from Prosite , PathwayThis feature allows user to download information in form of text file for all gene appearing in search result using multiple export option or by selecting each gene individually.Other web interfaces includes "Help" section which provides description of each query option and accepted keyword input in CastorDB. "Literature and Links" section provides links to external literature databases such as Pubmed and Agricol; and links to web resources used during analysis of castor genome. "Feedback" section allows user to comment on data and utilities incorporated in CastorDB.R. communis. Currently, information about this important oil seed plant is available in different sources. Among the existing databases, (i) JCVI Castor Genome Database and NCBI provides sequence information on R. communis genes and proteins; (ii) Information on EST expressed during different condition is available from dbEST division of NCBI database.The queries provided by CastorDB are focused on retrieving available information from various databases along with queried information for a particular gene or protein in R. communis obtained from various resources and develop a comprehensive database. CastorDB database provides researchers information not only on gene and protein sequences but also on possible Go annotation, domains present in a protein, predicted pathways, probable interacting partners, sub-cellular localization, protein sumoylation sites, gene expression and even biochemical properties of a given protein. In addition to a common BLAST search, CastorDB provides the user with a scope for keyword search using the options like CastorDB ID, locus tag, gene name, domain name, pathway, localization, EST accession number. Also, some of the experimental data obtained from external resources are represented in more interpretable form which can provide researchers with a better understanding about the plant and help in designing critical experiments to gain deep insights into its biology. In order to incorporate newer findings the database will be updated in every 6 months.CastorDB is, designed to facilitate the analysis of information on etc. In addition to a common BLAST search and simple keyword search, CastorDB provides the user with a scope of doing advanced search by using different keywords and options. Also, some of the experimental data obtained from external resources are represented in more interpretable form. Thus, CastorDB would be an important database providing researchers with information to better understand the biology of this important plant.CastorDB was generated by correlating the information available on its genome, transcriptome, and proteome and a comprehensive resource was built on protein domains, pathways, protein localization, presence of sumoylation sites, expression data, protein interacting partners, Ricinus communisProject Name: CastorDB: a comprehensive knowledge base for http://CastorDB.msubiotech.ac.inProject homepage: The database is currently available at Operating system(s): Platform independentProgramming language(s): HTML, Perl, CGI, Java, JavascriptLicense: Free for academics, Authorization is needed for commercial use (Please contact the corresponding author for more details)The authors declare that they have no competing interests.ST developed programs, scripts, tools for the database, carried out data analysis and drafted the manuscript; SJ helped in conceiving and designing the web server idea, analyzing the data wrote the manuscript; BBC provided critical inputs to develop the database, and to write the manuscript. All authors have read and approved the final manuscript.


Characterization of unknown proteins through computational approaches is one of the most challenging problems in silico biology, which has attracted world-wide interests and great efforts. There have been some computational methods proposed to address this problem, which are either based on homology mapping or in the context of protein interaction networks.Saccharomyces cerevisiae to predict functions of unknown proteins using cross validations. Comparing with other several existing algorithms, the results have demonstrated the effectiveness of our proposed methods in protein function prediction. Furthermore, the algorithm DSCP using experimental determined complex data is robust when a large percentage of the proteins in the network is unknown, and it outperforms DCS and other several existing algorithms.In this paper, two algorithms are proposed by integrating the protein-protein interaction (PPI) network, proteins’ domain information and protein complexes. The one is domain combination similarity (DCS), which combines the domain compositions of both proteins and their neighbors. The other is domain combination similarity in context of protein complexes (DSCP), which extends the protein functional similarity definition of DCS by combining the domain compositions of both proteins and the complexes including them. The new algorithms are tested on networks of the model species of The accuracy of predicting protein function can be improved by integrating the protein-protein interaction (PPI) network, proteins’ domain information and protein complexes. The function annotation of a protein is an important challenge in post-genomics due to the critical roles of proteins in various biological processes. However, it is expensive and time-consuming to experimentally determine protein functions. With rapid advances in large scare genome sequencing technologies, there is an increasingly widening gap between the number of newly found proteins and the completeness of their annotations, which requires a faster and more effective way to annotate unknown proteins automatically. Hence, the protein function prediction through computational approaches has become a major research topic, which has drawn much attention from researchers in the areas.Computationally predicting protein function is based on the idea that assigning functions to unknown proteins according to the known functions of similar proteins. The most common and reliable methods are using homology mapping to transfer annotations to newly sequenced proteins. One of the way to infer to homology is detecting sequence similarity by using BLAST and FASTOn the other hand, with the increase of large scale protein-protein interaction (PPI) data generated by two-hybrid and co-immunoprecipitation techniques, many researchers have attempted to determine protein functions by using information extracted from the PPI data. ExistinDirect methods are based on the fact that about 70% to 80% of proteins share at least one common function with their interacting partners. One of Instead of predicting functions for each protein separately, module-assisted methods first identify the function modules or protein complexes, and then annotate functions to all proteins in the function modules or protein complexes. Although the module-assisted methods vary in the clustering algorithms for identifying the function modules or protein complexes, they are based on the fact that functional module means a group of cellular components and their interactions that can be attributed to a specific biological function. PreviouSaccharomyces cerevisiae. As the comparisons are shown, our methods can make an apparent improvement on the performances of function prediction than other methods, such as neighbor counting[With respect to the above issues, we propose a new algorithm by defining a domain combination similarity in PPI networks as a measurement of the protein function similarity, named by DCS. In DCS, the protein functional similarity combines the domain compositions of both proteins and their neighbors. Then we propose another new algorithm DSCP which extends the protein functional similarity definition in DCS by combining the domain compositions of both proteins and complexes including them. Differently from previous homology mapping approach, our methods integrate the PPI data information and protein complex information. Differently from previous methods based on PPI data, our methods combine the domain information of protein itself and take protein complex information for consideration. We carry out experiments on data from counting, Chi-squcounting, Zhang-Dcounting, Markov counting and relacounting.This paper is organized as follows: First of all, recent advances of function prediction algorithms are introduced briefly. Then the relevant definitions as well as the new method are described in details. In the third section, the materials used in this paper are given and we present the results produced by our new algorithms and a comprehensive comparison between the new algorithms and five other existing algorithms. Finally, challenges and directions of the future work are discussed in Conclusions and discussion.When we predict the functions for an unknown protein, firstly the function similarities between proteins are defined. Then we find out a known protein with the highest functional similarity value to the unknown protein from the network, and the functional annotations of the known protein are then assigned to the unknown protein. In this work, we propose two algorithms to evaluate the functional similarity between two proteins. The one is DCS, the domain combination similarity, which combines the domain compositions of both proteins and their neighbors. The other is DSCP, which extend the protein functional similarity definition of DCS by combining the domain compositions of both proteins and complexes including them.PA and PB in spite of their different distributions while it is the same one kind of domain type that is contained by protein PA and PB, which indicates PA and PB probably share similar functions. To formally define the domain combination similarity, the following variables are introduced.Our measurement of protein domain combination similarity is consisted of two parts, the context similarity, which indicates the domain similarity of proteins’ neighbors, and the composition similarity, which indicates the proteins’ internal domain similarity. The context similarity is presented in Figure SN = {P1, P2, …, Pn} represent a set of all n proteins in the PPI network and NP denotes a set of neighbor proteins of protein P with P itself included.Given a PPI network, let P) denote a set of domain types in protein P. Given a protein set S = {Ps1, Ps2, …, Psl}, we defineLet DT(P) be a set of distinct domain types in the neighbor proteins of P , which is called the domain context of P. Consequently we haveLet DC between two proteins PA and PB will indicate a greater probability that they share similar functions.A larger value of fsim for protein function prediction. The pseudo code for calculating the functional similarity fsim between two proteins PA and PB using algorithm DCS is presented below.For the convenience of discussion, in the following sections DCS (Domain Combination Similarity) will be adopted to calculate the current definition of fcont, we don’t just consider the neighbors of a protein as in most classical algorithms but also search for the complexes containing the proteins. Here it is believed that functionally similar proteins tend to cluster together and protein complexes are this kind of collections of functionally related proteins[We argue that the original manner of taking neighbors as the domain context in DCS can be further improved by using the protein complexes information instead. Since the PPI network is not complete and has false interactions due to experimental limits and errors, merely considering the neighborhood can produce bias results. For the sake of common noises in the interaction data, when calculating the domain context similarity proteins-31. Therproteins,32. Dengproteins have useSCP be the set of all proteins inside those complexes containing protein P. Then the previous definition of domain context of P is adjusted as follows:Let fcont, fcomp and fsim still remain the same as before. Again a larger value of fsim indicates a greater probability of sharing similar functions.The formulas defining With regard to the protein complex data, we can use either known real protein complexes from assays or protein complexes predicted by various network clustering algorithms. Since both the known protein complexes and predicted protein complexes have the limited coverage for the original proteins in the whole network, we will still use the neighborhood as the context scope, if a protein is not included in any complex. We call the prediction algorithm based on this new definition as DSCP (Domain combination Similarity in context of protein complexes). The following pseudo code illustrates the procedure of calculating the functional similarity by using algorithm DSCP.fsim between each pair of proteins can be calculated conveniently. When predict the functions for an unknown protein Pu, we find out a known protein Pm with the highest value of fsim to Pu from the network, and the function annotations of Pm are then assigned to Pu. If there exist more than one protein that are of the same highest fsim to Pu, the first coming one will be selected as the reference.Given the protein similarity definitions described above, the functional similarities To evaluate the performances of a predicting algorithm, the cross validation is generally used,33,34. ATP), true negative (TN), false positive (FP) and false negative (FN). TP and TN are the correct predictions while FP and FN are the two kinds of wrong classifications. FP is a positive prediction that is in fact negative and FN is a negative prediction that is actually positive. Therefore, in some studies[PPV), recall , F-Measure and Matthew correlation coefficient . The three measurements are defined as follows.In a binary classifier system, there are four types of possible outcomes for each prediction, namely, true positive  protein interaction networks are widely used as a gold standard data in the research of network-based function prediction algorithms because the species of yeast has been studied most widely and thus the available interaction data for yeast is the most complete and convincible. Here, we also adopt yeast interaction network to test our new algorithms. The PPI network data is obtained from DIP database[2012.3.3) downloaded from GO official website[26.0). As for the protein complex information, we used the data of CYC2008[The database. The ann website as is th CYC2008 which coThe original interaction networks are transformed to use the UniProtKB/Swiss-Prot entries because the Pfam domain data use such labelling system. Consequently a network of 5,088 proteins and 22,277 interactions is obtained after removing the self-interaction and some proteins without UniProtKB/Swiss-Prot entries.The prediction performance is validated based on Gene ontology (GO) annotations. The GO system consists of three separate categories of annotations, namely Molecular Function (MF), Cellular Component (CC) and Biological Process (BP). The Predictions are validated separately for each of the three GO categories. Since the GO terms are organized as a hierarchical structure, in which a protein that is annotated with a GO term is also annotated with all its ancestors, using all GO terms for validation may result in biased conclusions. To avoid too special and too general, only thMoreover, we only use the Pfam-A data because the Pfam-A data have been manually checked and thus more convincible while Pfam-B part is automatically generated by HMM computational methods. In the λ is introduced to adjust the two parts of fcont and fcomp. With different values of parameter λ, the performances of prediction might differ greatly. As a result, we investigate the effect of parameter λ on algorithm DCS by running 20 times with equal interval of λ from 0 to 1. The corresponding indices of PPV, TPR and MCC at different value of λ are calculated. The results based on GO terms in MF, CC and BP are illustrated in Figure λ is either 0 or 1 is inferior to when λ is set to other values ranging from 0 to 1, which means that the prediction performance by integrating the context similarity and the composition similarity is better than that by using only either similarity. For each one of the three GO annotation categories, in terms of values of PPV, TPR and MCC, the performance of DCS rises sharply when λ is 0.5 and drops rapidly when λ is larger than 0.4. Although the values of λ vary a litter when the values of PPV, TPR and MCC reach their peak, DCS remains high performances when λ is in the range of 0.05 to 0.35. Therefore, we use λ = 0.1 as a default value in all the following experiments.In our definition of domain combination similarity, a parameter λ set as 0.1. Then they are averaged over different values of GO overlaps to get a distribution curve. As can be seen in Figure Then, we make a statistic on the average domain combination similarities with regard to different function similarity values which are presented in terms of the overlaps of the GO terms between proteins. Firstly, the domain combination similarities between each pair of proteins in the network are calculated using the definition of DCS with We test the qualities of our algorithms for predicting protein functions using the leave-one-out cross validation, which means for each round there is only one protein in the testing set. Note that we will filter out those GO terms whose sizes are smaller than 10 or greater than 200 because they are either too rare or too general for prediction. Here, the size of GO term refers to the number of proteins in each GO term. Moreover, experiments will be implemented separately for MC, CC and BP. Therefore, GO terms in each annotation category have been partitioned into four groups based on their positive size. The top part of Table Next, the algorithm of DSCP is adopted to predict protein function on the same PPI data sources by using the experimentally determined protein complexes obtained from CYC2008. 966 of 2895, 1281 of 3868 and 1314 of 3909 proteins that are annotated by a least one GO term in MF, CC and BP respectively present in at least one CYC2008 complex. For those proteins belonging to CYC2008 complex, DSCP infers their domain context by searching for the complexes containing them, whereas the domain context of the proteins that are not covered by any one of CYC2008 complexes is derived from their direct neighbors. The results, as listed in the middle part of Table Moreover, we have also used predicted protein complexes generated by various clustering algorithms including the widely used IPCA, MCODE, CPM2929, DPCluAs can be seen, the performances of DSCP decrease contrarily when using predicted protein complexes even though the protein coverage increases. This is easy to understand since clustering algorithms cannot predict accurate protein complexes because of many false positive and false negative complex members. Consequently we use the CYC2008 data for the algorithm DSCP afterwards.PR) curve is made used of, whose horizontal and vertical coordination are the values of TPR and PPV, respectively. The leave-one-out cross validation is carried out on all these methods to evaluate their effectiveness. Our methods DCS and DSCP as well as Zhang-DC infer functions of an unknown protein from its top K similar known proteins ranked by these methods. The functions of these known proteins are regarded as the predicted functions of the unknown protein. Moreover, the similarity scores between the unknown protein and the known proteins should be larger than zero. For NC, Chi-square and RLC, we select top K GO terms ranked by these methods as predicted functions of an unknown protein and also ensure that the corresponding scores are larger than zero . Here the parameter K ranges from 1 to 50. MRF assigns a function to unknown protein if the probability that the unknown protein has the given function is above a threshold. The threshold for MRF ranges from 1 to 0 decreased by 0.01. For a given testing protein and each threshold or parameter K, the TPR and PPV values can be calculated according to the definition in Equations (9) and (10). The final PR curves of each comparing method are plotted based on the average TPR and PPV values over all testing proteins[PR curves of all methods and the digits in legend are the maximum F-measures for all methods on corresponding GO annotation category .Furthermore, we perform a comparison of our algorithms of DCS and DSCP with algorithm Zhang-DC which haproteins. Figure F-measures among all comparing methods on each GO annotation category. For the GO terms in category CC, the PR curves of DSCP and DCS are above that of all comparing methods, which means that our methods have a higher number of true positives and at the same time a smaller number of false positives when selecting different thresholds. For the GO term in categories MF and BP, the PR curves of DSCP and DCS are above that of the other four existing methods  and are also above that of RLC when inferring functions form a small number of the most similar proteins. However, the precision values of DSCP and DCS drop sharply when inferring function from a large number of similar proteins. Since a lot of functions of these similar proteins will be assigned to the unknown proteins, which will introduce many false positives. The precision values of RLC drop slowly with decrease of the threshold of protein-GO term relationship score. Because decreasing of the threshold slightly increase the number of predicted functions for an unknown protein.As shown in Figure There are many databases of PPI network data available online and different datasets vary from each other a lot. Therefore we have tested our new algorithm DSCP on four different scale networks that are obtained from different sources of online databases.To reduce the influence of noise data, we also remove the duplicated interactions, self interactions and interactions associated with proteins that are functionally unknown or cannot be mapped to UniProtKB/Swiss-Prot entries. As a result, the four datasets are MIPS PPI whicK = 1). RLC and MRF show the results when they achieve the maximum F-measure based on average prediction performance on each testing GO term. The results in Table PPV, F-measures and MCC values. The difference between DCS and Zhang-DC rises from whether or not the domain context similarity includes the similarity of proteins themselves. The improvement of DCS proves the effectiveness of our strategy. Both DSCP and MRF utilize protein complex and protein domain information to predict functions for proteins. However the F-measure values of DSCP are obviously higher than that of MRF. For example, on DIP network, the F-measure values of DSCP are 0.16, 0.3 and 0.23 higher than that of MRF for MF, CC and BP category respectively. It is caused by that MRF infers functions for a protein from its direct neighbors and highly depends on the completeness of domain and complex information from known proteins. By compared with RLC, its performance is inferior to DSCP and DCS, and comparable to Zhang-DC. Since RLC annotates function for unknown proteins by integrating GO term similarity and global PPI network information, it can cover more true functions and has relative higher TPR values. However the proportion of positive results are relative lower, which leads to its lower PPV, F-measure and MCC values. All of facts prove that our methods propose an effective strategy for combining protein domain information, protein complexes information and PPI network and outperform other existing methods in function prediction.Table In the former sections, we used leave-one-out cross validation to demonstrate the algorithms’ improvements made on predicting protein functions. However, in practical applications, there are usually much more proteins without annotations rather than solely one unknown protein. As a result, we next used the leave-percent-out cross validation, which is also a widely accepted validation method,34, to dPPV, TPR, F-Measure and MCC. The percentage is set as 10%, 20%, 50% and 80% respectively and the prediction results are displayed in Table F-Measure and MCC value when the percent of unknown proteins rises up to 50%. Therefore, DSCP seems to be a suitable method for annotating unknown proteins.We run our predicting programs of DCS and DSCP, Zhang-DC and RLCIn this paper, we have proposed an algorithm DCS for protein function prediction by using domain combination similarity to estimate the function similarities between proteins. In addition, we have used the protein complexes to expand domain context scope, and consequently made a desirable improvement to the final prediction results. Our experiments have demonstrated that the Pfam domain data is a useful resource for protein function prediction and its advantages become increasingly obvious as the data grows more complete. In addition, we have illustrated our new algorithm DSCP to be robust when a large percentage of the proteins in the network are unknown. In a word, the algorithm of DSCP described in this paper can be an effective approach for predicting functions for unknown proteins. Additionally, the functional similarity of two proteins measured by our methods DCS and DSCP can be easily used to weight PPI network and plugged into NC, Chi-square, RLC and MRF. In the future work, we will focus on designing more effective method based on the weighted PPI network to predict protein functions.Like many other similarity-based algorithms, when there is more than one known protein with the same similarity value to an unknown protein, we have met difficulties in choosing a proper reference. How to a construct a good similarity definition with the minimum conflicts remains an important problem to be solved in the future. Besides, in our algorithms the unknown proteins always refer to the same most similar protein whichever GO term is being considered. Mining out the differences between two proteins with a large similarity by using other biological information, such gene expression profiles,44 can bSince proteins can function variously in different organisms or organs, the protein function prediction becomes much more complex when the surrounding environment changes. MoreoveThe authors declare that they have no competing interests.JC obtained the protein-protein interaction data, GO annotation data and domain data. JXW, JC, WP, ML and LC designed the new methods and analyzed the results. JXW, JC and WP drafted the manuscript together. FXW participated in revising the draft. All authors have read and approved the manuscript


Classification of structural details of protein-protein interactions can provide valuable insights for modeling and abstracting design principles. Here, we aim to cluster protein-protein interactions by their interface structures, and to exploit these clusters to obtain and study shared and distinct protein binding sites. We find that there are 22604 unique interface structures in the PDB. These unique interfaces, which provide a rich resource of structural data of protein-protein interactions, can be used for template-based docking. We test the specificity of these non-redundant unique interface structures by finding protein pairs which have multiple binding sites. We suggest that residues with more than 40% relative accessible surface area should be considered as surface residues in template-based docking studies. This comprehensive study of protein interface structures can serve as a resource for the community. The dataset can be accessed at  Proteins physically interact with each other through their binding sites. Some proteins interact with their partners simultaneously using different interaction sites, some interact with their partners via the same interaction site at different times, and some appear to interact with only one protein The main step to achieve a unique interface set is to flag similar interfaces. Comparisons of two protein interfaces can detect similarities in amino acid sequences of protein interfaces  or similarities in 3D coordinates of amino acid positions in the proteins . Protein interface clustering can be done in three different ways: using only sequential or only structural alignment scores of all protein interfaces, or a hybrid strategy which includes both sequential and structural alignment scores of protein interfaces. The PFAM Detecting evolutionarily related proteins via structural similarities is more reliable than via sequence similarities since structure is more conserved. Sequence based methods are easy to derive and computationally cheap, so sequence based methods are generally the starting point of the studies; however, this has two limitations et al. compare structural alignment of interfaces by extracting dimensionless interface vectors using the center of mass of core and rim Cα coordinates et al. perform pairwise structural alignments of protein binding regions and classify them with agglomerative hierarchical clustering using the complete linkage method To classify protein interface structures by either sequence, or structural alignments, or both, some kind of clustering methods should be used. In most structural clustering studies, a hierarchical clustering algorithm is used. Ghoorah There are methods other than hierarchical clustering, such as centroid models (k-means clustering), distribution models , density models, subspace models (biclustering), and graph based models. Graph theory has also started to become popular in the last decade for analyzing the relationship between events. Barabasi and his colleagues presented various properties of networks by tracking the internet routes In light of previous structural classification studies and the exponentially increasing number of Protein Data Bank (PDB) The novelty in this work is in generating structurally non-redundant protein-protein interfaces which are sensitive to small perturbations in protein binding sites that have a significant impact in template-based docking. Template-based docking strategies are based on non-redundant structures of protein interfaces which are compared with target monomer surfaces aiming to match similar interface partners 22604 unique interface types are defined in our study. These can be exploited for template based docking, for studies of binding specificity, function-domain evolution, and drug design.An interface is described as contact region between two interacting proteins. Previous works show that there are different approaches to find contact residues in a complex. Distance based approaches use atomic distances between two proteins to extract interacting residues Two residues are defined as contacting if the distance between any two atoms of the two residues from different chains is less than the sum of their corresponding van der Waals radii plus 0.5 Å An interface is labeled with the PDB ID plus the chain IDs. For instance, if the PDB ID of a protein structure is 1GQP and there is an interface between chains A and B, then this interface is named 1GQPAB as in our previous studies th 2012, there were 78477 PDB entries and 45491 of them were complex structures which resulted in 622321 possible binary interactions, assuming all protein chains interact with each other. However, not all chains in a protein complex interact physically with each other, necessitating chain pair detection. Extracting interface residues in complexes by distance thresholds needs excessive time. Hence, the accessible surface area (ASA) of the possible interface pairs are first calculated by using NACCESS 2. NACCESS calculates the monomer and complex ASA values of the proteins and the interaction surface is calculated simply subtracting complex ASA value from the monomer ASA values of two proteins. When small interfaces were eliminated, 184342 interface candidates remained. ). The remaining interface candidates were processed according to our interface definition. As a result, there were 130209 interfaces where each binding site had at least five residues.In order to generate a protein-protein interface dataset, we extract all possible binary interactions of the protein structures and check regardless of whether they interact. All PDB entries are used to generate the interface set. As of January 14MultiProt Interface similarity is calculated based on the structurally matched residues of the two interfaces. The similarity formula is:The Interface similarity is defined asPairwise comparison of all interfaces is a time consuming process. Comparing a small interface with a big interface is unnecessary. Gao and Skolnick’s Girvan and Newman A network is formed by nodes and edges. The network of protein interfaces is constructed by using interface structures as nodes. If two interfaces are similar  in order to find the best possible clusters. If the network has a clustering coefficient value higher than the criterion the network separation process stops.Minimum cluster size criterion, set at 5, is used because we do not want network nodes to fall apart as a network of size 1. After a network separation, if one of the clusters has less than 5 interfaces, this cluster is no more divided into two clusters.In order to increase the speed of network separation, the nodes in the network are clustered in 5 steps according to the interface similarity values . Nodes which have similarity values higher than 0.80 are grouped as a single node (starting with the nodes which have the highest similarity) and their neighbors are linked to this new node . The newThe new networks generated by the community finding algorithm are processed again, combining nodes which have similarity values higher than 0.85, 0.90, 0.95 and 1. After five cycles, all generated networks are reprocessed by the community finding algorithm without any node grouping to obtain the final results. Communities extracted at the final stage are the interface clusters.3) complexity. The largest connected component of the network has 1638090 edges which restrict applying the community finding algorithm because of the runtime. Edge betweenness centrality values of the new network should be calculated after each edge removing process, so this step is the bottleneck of the community finding algorithm. Therefore, the nodes are grouped according to their similarities, then the separation step is applied which speeds up the removal of the edges from the network, and simplifies finding the edge betweenness values of the network in the last step. The main steps of the methods are shown in This grouping of the nodes according to their similarity values procedure is used to minimize the run time of the community finding algorithm. Calculating edge betweenness centrality of a network has O = the average dissimilarity between interface i and all other interfaces in its cluster.b(i) = the minimum of the average dissimilarities between interface i and the interfaces in the other clusters.The clustering procedure was performed using an in-house software. Codes are written in Python, and the betweenness and clustering coefficient values are found by using the NetworkX package There are various clustering methods and they have different pros and cons. In this work, we used two step approaches for a reliable clustering result. First, grouping the nodes according to their similarities iteratively is used to provide compactness  of the clusters. Secondly, the edge betweenness property of the protein interface network is used to cluster similar interfaces, providing the connectedness of the clusters.Choosing the clustering method is the starting point of the data analysis but the important part is finding appropriate criteria for generating the best results. Different clusters are obtained by using different parameters and all results from different clusters are evaluated to select the best cluster set. The parameters for generating different clusters are the clustering coefficient for the stopping criteria, the similarity value adjustments , and choosing the maximum or minimum similarity values for the new node and the neighboring nodes during node formation.For the evaluation of a clustering method, there are two main measurements; external and internal Similar interfaces, similar global protein folds. In most cases, if the interfaces are similar, the overall protein folds are also similar. Such similar interface, similar fold clusters contain a single family. Here, we observe that , protein interface structures can be similar when the global folds of the complexes are similar. An example is shown in Similar interfaces, dissimilar global protein folds. Even if the global folds of proteins are different, these proteins can interact by similar interface structures as shown in Investigation of the interface clusters reveals that as expected, protein interactions can occur between homo- or heterodimeric chains. Further, when members of a single interface cluster are investigated, different types of interfaces are observed. We previously labeled three interface types: Type 1: In order to show the performance of the current dataset, we compared it with our previously derived non-redundant protein interface dataset et al.We also tried to compare our new dataset with the work of Kim W.K. Structurally non-redundant interface architectures are obtained by extracting representatives of all clusters. These representative interfaces are a valuable resource for template based docking studies. The main purpose of template based docking is matching monomer surface to a template interface structure; thus surface extraction of the monomers is an important step. In order to obtain reliable docking results, the same method should be used to extract the surface of monomers with the templates. Jones and Thornton A protein can interact with its partners using the same or different interfaces. Finding possible interaction sites is challenging and it is critical to predict possible interaction modes. Docking, homology modeling, and template based docking can be used for this purpose. When all PDB entries are investigated, some proteins with multiple partners are observed to exploit multiple interaction sites with their partners. These experimentally found structures can be extracted using distinct interface structures of the complexes (representative interface structures) present in PDB.To find proteins with multiple interfaces, firstly, pairwise sequence alignments of all the monomers in the PDB are downloaded from PDB FTP Services (22 November 2012) and labeled with a cluster ID. All monomers which have the same cluster ID have 100% sequence identity. This leads to 48669 different cluster IDs. Then, two monomers which have an interface are labeled as first monomer cluster ID underscore second monomer cluster ID (e.g. 423_1002). According to their monomer sequence identities, 26825 distinct interface pairs are extracted. These pairs are compared with the structural clusters extracted from interface similarity. 7962 protein pairwise interactions out of 26825 have more than one interface in the PDB. A general view of the database is shown in Proteins prefer different conformations to bind other proteins. Analysis of those 7962 monomer pairs illustrates that these proteins interact with their partners using slightly different conformations to bind their partners at the same shared site or at different binding sites. 3500 out of the 7962 use the same shared site to bind their partners and the rest employ different binding sites. For example, in Analysis of The numbers of both available protein interfaces and their interface clusters increased exponentially in recent years. However, interface clusters have smaller increment than protein interfaces as shown in Here we generate an updated non-redundant dataset of protein-protein interfaces, containing 22604 unique interface structures. The new dataset has not been used for prediction as yet; however, our older datasets have been extensively used for predictions of protein-protein interactions http://prism.ccbb.ku.edu.tr/piface.This dataset can be accessed at


Specifically, we used 3 data sources to calculate the similarity scores: sequence similarity, protein-protein interactions, and gene expressions.Protein function determination is a key challenge in the post-genomic era. Experimental determination of protein functions is accurate, but time-consuming and resource-intensive. A cost-effective alternative is to use the known information about sequence, structure, and functional properties of genes and proteins to predict functions using statistical methods. In this paper, we describe the Multi-Source kNN had term-based Area Under the Curve (AUC) accuracy of Gene Ontology (GO) molecular function predictions of 0.728 when 7,412 human training proteins were used, and 0.819 when 35,622 training proteins from multiple eukaryotic and prokaryotic organisms were used. By aggregating predictions from all three sources, the AUC was further improved to 0.848. Similar result was observed on prediction of GO biological processes. Testing on 595 proteins that were annotated after the CAFA submission deadline showed that overall MS-kNN accuracy was higher than that of baseline algorithms Gotcha and BLAST, which were based solely on sequence similarity information. Since only 10 of the 595 proteins were represented by all 3 data sources, and 66 by two data sources, the difference between 3-source and one-source MS-kNN was rather small.We report the results in the context of 2011 Critical Assessment of Function Annotation (CAFA). Prior to CAFA submission deadline, we evaluated our algorithm on 1,302 human test proteins that were represented in all 3 data sources. Using only the sequence similarity information, MS-k-nearest neighbor algorithm is an efficient and effective model for protein function prediction; (2) it is beneficial to transfer functions across a wide range of organisms; (3) it is helpful to integrate multiple sources of protein information.Based on our results, we have several useful insights: (1) the  Determining biological functions of proteins is a key challenge in the post-genomic era. The experimental methods for protein function prediction are time-consuming and resource-intensive. It is infeasible to experimentally determine the functions of all known proteins. For that reason, computational methods that predict biological functions of a protein using known information about its sequence, structure, and functional behaviour, are becoming an attractive low-cost alternative. During the recent couple of decades, many computational methods for predicting protein function have been developed, and the 2011 Critical Assessment of Function Annotations (CAFA) has been designed to establish a state of the art in the field.The sequence alignment-based function inference is the most widely used form of computational function prediction . These an links, instead of considering only the direct neighbors.Beyond sequence similarity, several computational approaches have been proposed to utilize other types of biological data, such as protein-protein interactions (PPI) and gene expression data. The methods that use PPI data to predict protein functions are based on a simple premise: a protein does not perform its function in isolation; instead, a group of proteins needs to interact in order to perform a certain function. Therefore, the functions of a querying protein can be inferred from its interacting partners. Schwikowski et al.  used a nUse of gene expression data for function prediction has been motivated by an observation that co-expressed genes are likely to be functionally related -8. In thArguably, each data source captures only one aspect about proteins' properties. Thus, combining such heterogeneous data can bring a more complete picture about protein function. Recently, several studies showed promising improvements in protein function prediction by integrating multiple types of biological data. Troyanskaya et al.  proposedThere are several challenges that need to be addressed in future research on multi-source function prediction. The first is that different sources of information may have vastly different coverage. For example, while sequence similarity covers all known proteins, PPI data coverage is significantly smaller, and gene expression similarities are constrained by a specific microarray platform. The second challenge is differences in data quality. For example, PPI can be obtained by a variety of techniques that differ in cost and reliability. A confounding issue is that functional annotations have an uneven coverage biased towards certain types of proteins and functions, and that determination of protein functions, such as the one provided by Gene Ontology , is a suk-nearest neighbor approach for function prediction proposed in [k nearest neighbors of the query sequence and transfers their functions by weighted averaging, such that nearer neighbors have larger influence to prediction than the farther ones. In this paper, we propose the Multi-Source kNN (MS-kNN) algorithm able to use multiple sources of protein information. To provide the final prediction, MS-kNN uses weighted averaging of the source-specific prediction scores. In the algorithm design, we explored several approaches to determine weights, ranging from averaging to solving a constrained optimization problem. We observe that a query protein does not have to be present in all data sources. For example, we might know the protein's sequence and whether it interacts with other proteins, but not its gene expression . Averaging of the source-specific scores provides a simple mechanism for dealing with potential missing predictions.In an attempt to address some of the identified challenges and faced with the tight deadline of 2011 CAFA, we focused our attention on the posed in . This isIn the following, we will discuss evaluation of several prediction approaches prior to CAFA submission deadline, describe how we selected the predictor, summarize and discuss results on CAFA proteins, and propose some directions for the future research.At the beginning of the challenge, 48,298 proteins were released by CAFA organizers as the test proteins. In the released test data, the protein names, Uniprot IDs, and sequences were provided. A large majority of these proteins did not have known functional assignments, as defined by the Gene Ontology (GO)  annotatiWe considered integration of three different data sources for protein function prediction. These three data sources were: 1) protein sequence data; (2) microarray expression data; and (3) protein-protein interaction data. Particularly, prior to CAFA deadline we focused on human proteins, in order to more easily evaluate and characterize our approaches. Visual summary of the data sets we used is in Figure  protein By courtesy of Dr. Predrag Radivojac from Indiana University, we obtained a data set of GO annotations of 36,924 proteins, as well as their pair-wise sequence similarities (expressed as percent identity), and pair-wise similarities between these proteins and the 48,298 CAFA proteins. These 36,924 proteins with their GO annotations and sequences were used as the training set for function prediction. We note that there were 474 proteins that were present in both training and CAFA data set, as they were already partially annotated. To simplify the experimental design, these 474 proteins were excluded from the training set during pre-CAFA evaluation. We still provided function predictions for them, as they were in the CAFA data set.http://www.ncbi.nlm.nih.gov/geo/). The GPL96 is one of the most widely used human microarray platforms. We linked the Affymetrix probe IDs with Uniprot IDs through Entrez. After ID mapping, the microarray data covered 7,372 human proteins in the training set, and 3,397 human proteins in CAFA set. These datasets were already pre-processed by Affymetrix Microarray Suite Version 5.0 and we did not apply any additional pre-processing.We downloaded 392 Affymetrix GPL96 Platform microarray datasets from GEO (http://www.phenopred.org/). This data source includes 41,457 interactions between 9,141 proteins. After ID mapping, the PPI data source covered 3,217 proteins in the training set, and 737 proteins in CAFA set.For PPI data source, we used physical interactions between human proteins listed in OPHID database  and False Positive Rate (FPR) was calculated at different discrimination thresholds, creating the Receiver Operating Characteristic (ROC) curve. The Area Under the ROC Curve (AUC) was calculated by integrating the ROC curve, which is corresponded to the TermAUC evaluation metric of CAFA. We only considered the GO functions having more than 15 annotated proteins among the 1,567 3-source human proteins left after removing the 1,302 test proteins. This resulted in 122 molecular function (MF) and 546 biological process (BP) GO terms. We used k = 20 in all experiments in this section. In the following, we will discuss performance of several proposed function prediction algorithms.There were 2,869 annotated human proteins in the training set that were represented by all 3 data sources. Among them, we randomly selected 1,302 proteins as test set in our pre-CAFA analysis. Given the prediction scores on the 1,302 test proteins for function kNN classifier and lin-sim incorporated kNN classifiers. The results for kNN using sequence similarity were based on the training set of 1,567 3-source human proteins, remaining after exclusion of 1,302 test proteins. While we had 392 different mi-croarray datasets available for this experiment, due to the tight deadline we used only the largest microarray data set (having 221 microarrays) with GEO accession number GSE4475.In this section, we compare the accuracies of the two different prediction algorithms: baseline lin-sim kNN classifier had slightly higher accuracy than the baseline kNN. However, to estimate the lin-sim between all GO terms and to use them during prediction time is very time-consuming. This includes a need to determine the lin-sim function similarity threshold through cross-validation. As a result, we reasoned that the accuracy improvement was not large enough to justify use of lin-sim kNN predictor in CAFA.The results in Table kNN classifier was used as the prediction model and we used the same GO terms as in Table In this section, we explore how useful it is to transfer functions from paralogous and orthologous proteins. Paralogous proteins are similar proteins within the same organism that are probably created by duplication and functional divergence. Orthologous proteins are similar proteins across different organisms that are related by speciation. The test set was still the 1,302 human proteins. We used 10 different training sets, after excluding the 1,302 test proteins: (1) 1,567 human training proteins represented by all 3 sources , we concluded that the issue lies in the interpretation of zero labels. Formulation (5) assumes that ij Y= 0 means that i-th protein does not have j-th function. However, ij Y= 0 often means that the function is not known and not accounting for this results in reduced accuracy. We think that this is a valuable insight that might be helpful in design of future predictors of protein function.We observe that by averaging scores from all 3 data sources using MS-kNN is able to improve prediction scores using sequence similarity with seemingly inferior prediction scores coming from PPI and microarray data. In order to understand why two seemingly inferior predictors can help the superior one, in Table k in kNN to 20. We can see that TermAUC obtained with sequence data (0.829) was much larger than with microarray data (0.613) and PPI data (0.564). However, integrating these 3 sources improved the TermAUC to 0.938. For the first 5 proteins listed in Table k = 20 nearest neighbors were annotated with GO:0044106. Microarray-based score of PEPD_HUMAN was relatively small, but it was sufficient to improve its ranking near the top one third. In case of PON1_HUMAN, it had the top ranking based on PPI data and a very high ranking based on microarray data, such that it was ranked 18th in aggregate.It might be somewhat surprising that MS-y data 0.13 and PPy data 0.13 and PPlin-sim kNN classifier improves prediction performance only slightly, while it is computationally costly and sensitive to the lin-sim threshold choice. Therefore, due to the time constraints of the competition, we decided not to use the lin-sim approach for score calculation. We used MS-kNN as our predictor because, as it can be seen from Table kNN and both simpler and more accurate than other MS-kNN algorithms we studied. A given CAFA protein could be represented in one, two or three sources. If a data source was not available for a test protein, the score for that source in MS-kNN was set to zero. In this way, scores of proteins represented by multiple sources were biased upwards, reflecting increased prediction confidence.By considering the results presented above, we observed that For CAFA assessment we provided predictions for all 48,298 CAFA proteins and for all GO terms . One of the rules of CAFA was that, for the final submission, one protein cannot be associated with more than 1,000 GO terms. Thus, we sorted the prediction scores for each protein and submitted the top 1,000 GO terms with the corresponding prediction scores. We note that for vast majority of CAFA proteins  we only had sequence information available.Only 595 of the CAFA proteins were experimentally annotated after the submission deadline, and they were used to evaluate the prediction accuracy. Of these 595 proteins, 366 proteins were associated with MF functions and 436 with BP functions. In the evaluation set, there were 2,786 new MF annotations and 11,075 new BP annotations. Among the 595 proteins, only 10 were covered by all 3 data sources, while 66 were covered by 2 of the 3 data sources. For the remaining 519 proteins we only had sequence information.The CAFA organizers used the following 3 baseline algorithms for comparison with the submitted predictions.Priors. Prediction score of every protein for a given GO term was the same and was calculated as the probability of that GO term occurring in Swissprot. This approach made it more likely for a protein to be annotated with a more common GO term.(1) BLAST. To obtain prediction score for annotation of a target protein with a GO term, the protein's sequence was compared with all protein sequences annotated with this GO term using BLAST. The sequence identity of the most similar protein was used as the prediction score.(2) Gotcha [3)  Gotcha [protein-centric and one was function-centric. In this paper, we report only on the AUC results for simplicity of analysis. In protein-centric evaluation methods, the prediction scores of each protein across all available GO functions are sorted. Then, AUC is calculated for each protein. In function-centric evaluation method, for each function, the prediction scores of all proteins associated with this particular function are sorted and AUC is calculated for each GO term.The CAFA organizers used 4 different evaluation methods; 3 of them were At each threshold, precision and recall are calculated and reported as averages across all proteins. If a particular score for a term/protein pair is above the given threshold, then the annotation at that threshold is propagated towards the root of the ontology.For a particular protein, the scores are first sorted. Then, for the highest 20 scores, precision and recall are calculated. If there is a tie between more than one term, all such terms are used to calculate AUC.For each threshold, weighted precision and recall are calculated based on the information content of each term. The information content of a GO term is calculated from the January 2011 version of Swissprot, as the negative log of the frequency of the term among proteins annotated with at least one experimental evidence code.kNN algorithm.If more than 25 of the 595 test proteins were annotated by a GO term after the CAFA deadline, Term AUC was calculated. For TermAUC, precision and recall are calculated at each threshold after propagating annotations for testing proteins. We note that we used TermAUC during the design of MS-kNN and MS-kNN. The results over the 4 different AUC accuracies show that MS-kNN worked the best overall on MF prediction. Particularly, the improvements in Threshold and Weighted threshold AUC were quite large. For the TermAUC, sequence-based kNN and MS-kNN had similar results. This could be explained by the fact that for 519 of the 595 test proteins we used only sequence information. In BP predictions, MS-kNN was also overall the most accurate, although in was not the most accurate on any of the 4 accuracy measures. For a further insight, TermAUC of the 11 MF terms based on different prediction methods are compared in Figure kNN was better than BLAST on all but one MF term. It was better than Gotcha on 7 out of the 11 MF terms.The AUC scores for MF terms based on different evaluation methods are shown in Table kNN as compared to similarity-based kNN on 595 CAFA proteins. Because we had only 8 proteins for evaluation for both MF terms and BP terms, the TermAUC accuracies for GO terms were not reliable and are not shown. As can be seen, AUC of MS-kNN was much larger than that of similarity-based kNN on all accuracies except Top n AUC on BP terms. We note that these results are consistent with those reported in Table We performed several additional experiments to get a better insight into the proposed algorithms and to explore some alternatives. Among the 595 test proteins, 66 proteins were in 2 data sources, and only 10 proteins in all 3 data sources. Among these 10 proteins, 8 of them were annotated with MF terms and 8 of them with BP terms. We studied results on these 10 proteins in more detail. While the results in Tables k-NN algorithm, coupled with simple integration of prediction scores from multiple data sources, proved to be reasonable.The protein function prediction is a complex problem. In this paper, we focused on the question of how to integrate multiple data sources to improve the prediction accuracy. We discussed and evaluated several different integration schemes in this paper. Our pre-CAFA and CAFA results strongly indicate that integrating information from multiple data sources could improve protein function prediction accuracy. At the level of sequence similarity-based predictions, we observed that it is beneficial to consider all available annotated proteins, regardless how evolutionary distant they are from a query protein. Considering the time limitations associated with the tight deadline for submission of the CAFA predictions, our strategy to use the simple and efficient There are certainly many avenues for future improvements of function predictions. A straightforward one is to include as many available sources of structural and functional protein information. For example, in CAFA, we used only microarray data from a single, albeit commonly used, human microarray platform. Information beyond microarray data and protein-protein interaction data, such as chromosomal neighborhood of a gene, mutations, role in various diseases, or protein structure, could certainly be valuable. Based on our experience during CAFA, we think that further advances in statistical approaches for function prediction are needed. Particularly, we would like to point to two open problems we believe could lead to significant advances in protein function prediction. One is related with the observation that a lack of a protein's annotation with a certain GO term should not be treated as negative evidence, but rather as a missing label. As a consequence, it would be advisable to treat function prediction problem as a one class classification, instead of binary classification. Another is the problem of sampling bias, created by the fact that available annotations are not a random sample from the protein/term space. Developing methods that are robust to sampling bias or the ones that could correct its negative effects should be one of the priorities of future research in the field.k-nearest neighbor (kNN) classifiers we evaluated and used during CAFA, as well as the approaches for integration of predictions from multiple data sources.Below, we describe p has function f, we used a weighted variant of the kNN algorithm, as proposed in  [f for protein p is calculated asTo calculate a likelihood that protein ., 2009) . The presim denotes the similarity score between proteins p and p', I is an indicator function that returns 1 if p' is experimentally annotated with f and 0 otherwise, and kN(p) is the set of the k nearest neighbors of p according to the metric sim. The similarity score between two proteins sim on each of the three data sources we considered was calculated in the following way. For protein sequence data, the similarity score was calculated as percent identity divided by 100. For microarray data, Pearson correlation coefficient, a popular method for measuring the similarity between gene expressions [where ressions , was usesimilar functions. Their proposed prediction model isIn protein function prediction problem, a single protein may have multiple functions, and the functions are organized in a hierarchy. Pandey et al  proposedlinsim denotes the similarity score between functions f and f'. The Lin's similarity measure [where  measure  is used f and f' are the Gene Ontology terms. The p(f) denotes the probability of a protein being annotated with function f, which is estimated from the available set of GO annotations. The joint probability ms p is calculated as S is a set of common ancestors of functions f and f'. It is easy to see that linsim = 1 when f = f', and limsim = 0 when their minimum subsume is the root of the ontology. Thus, the limsim score is always in the 0[where p, f). In particular, we can obtain one score using sequence similarity and one using PPI. Since we used J (= 392) microarray data sets, we had one prediction score for each gene expression data set. Given the J + 2 scores for each  pair, an open question was what is the best way to integrate them into a final, single score. We studied the following prediction score integration schemes: (1) averaging; (2) the same weighted averaging for any  pair; (3) different weighted averaging for different GO term clusters. In the schemes (2) and (3), the weights for different data sources were obtained by solving a convex optimization problem.By using equations (1) or (2), we can obtain scores for each protein/function pair  the score obtained from sequence similarity data, by PPIscore the score obtained from PPI data, and by EXPscorej the score obtained by the j-th microarray data set. By averaging, the final score is obtained asLet us denote by Multi-Source kNN (MS-kNN), as this was the final algorithm we used in CAFA.We call the resulting algorithm the kNN assumes that each data source is equally informative, which might not hold in general. We thus considered using weighted averaging of the scores from different sources. For MS-w-kNN we learned the weights from training data using a large margin method as follows. Let us assume that we are given m data sources, {Dj, j = 1..m}, and n proteins {i, i x= 1..n}. Each protein is assigned to several functions from the set of k functions. Let i Ydenote the set of functions that protein i xis assigned to, and i xis not assigned to. Furthermore, let f be a vector of length m, whose j-th element is the score of protein x for function y on the data source jD. Then, a weight vector w, used for averaging of m prediction, is found by minimizing the following optimization problem,MS-e is a vector of ones. The resulting convex optimization problem can be solved using standard optimization tools, such as CVX (http://cvxr.com/cvx/). With the trained weight vector w, the protein-function scores from different data sources can be integrated by taking their weighted average as wT·f.where kNN, we considered clustering of all GO terms at the root level to MF and BP terms, and at the first level to 7 MF and 25 BP functional clusters.Instead of learning a single weight for all GO terms, we can partition functions into clusters and assign cluster specific weights. Since the Gene Ontology is already organized in a hierarchical structure, we can directly use it to cluster the GO terms. In MS-CW-The authors declare that they have no competing interests.LL, ND, YG and SV conceived the study and developed the evaluation strategies. SV and YG directed the work. LL and ND implemented all methods and performed the experiments. LL wrote the first draft of the manuscript. All authors participated in the preparation of the manuscript and approved the final version.