PhyloFacts, a structural phylogenomic database for protein functional and structural classification, is described. .The Berkeley Phylogenomics Group presents PhyloFacts, a structural phylogenomic encyclopedia containing almost 10,000 'books' for protein families and domains, with pre-calculated structural, functional and evolutionary analyses. PhyloFacts enables biologists to avoid the systematic errors associated with function prediction by homology through the integration of a variety of experimental data and bioinformatics methods in an evolutionary framework. Users can submit sequences for classification to families and functional subfamilies. PhyloFacts is available as a worldwide web resource from  Computational methods for protein function prediction have been critical in the post-genome era in the functional annotation of literally millions of novel sequences. The standard protocol for sequence functional annotation - transferring the annotation of a database hit to a sequence 'query' based on predicted homology - has been shown to be prone to systematic error -3. The tAn additional complicating factor in annotation transfer by homology is the complete failure of this approach for an average of 30% of the genes in most genomes sequenced: in some cases no homologs can be detected within a particular significance threshold, for instance, a BLAST  expectatWith the huge array of bioinformatics software tools and resources available, it might seem unthinkable that functional annotation accuracy would be so difficult to ensure. Rather like the parable of the blind men and the elephant, each tool used separately provides a partial and imperfect picture; taken as a whole, the probable molecular function of the protein, biological process, cellular component, interacting partners, and other aspects of a protein's function can often come into better focus. For instance, annotation transfer from the top BLAST hit may suggest a protein is a receptor-like protein kinase, while domain structure prediction reveals that no kinase domain is present; the two orthogonal analyses prevent mis-annotation of the unknown protein.In this paper we present PhyloFacts, an online structural phylogenomics encyclopedia containing almost 10,000 'books' for protein families and domains, designed to improve the accuracy and specificity of protein function prediction . PhyloFaPhyloFacts is motivated by two of the biggest lessons of the post-genome era - the power of integrating data and inference tools from different sources, and improved prediction accuracy using consensus approaches in bioinformatics. For instance, protein structure prediction 'meta-servers' making predictions based on a consensus over results retrieved from several independent servers typically have lower error rates than any one server used separately . In the We employ the same basic principles in this resource, by integrating many different prediction methods and sources of experimental data over an evolutionary tree. In cases where attributes are known to persist over long evolutionary distances , we can integrate predictions over the entire tree to derive a consensus prediction for the family as a whole. In cases where attributes are more restricted in their distribution in the family , inferences will be more circumspect, potentially restricted to strict orthologs. Evolutionary and structural clustering of proteins enables us to integrate these disparate types of data and inference methods effectively, to identify potential errors in database annotations and provide a platform to improve the accuracy of functional annotation overall.In addition to new methods developed by us for phylogenomic inference, PhyloFacts includes a number of standard bioinformatics methods available publicly. To motivate the need for protein functional classification integrating diverse methods and data in an evolutionary framework, we examine the major classes of bioinformatics methods in turn, and discuss their different pros and cons. Methods designed for predicting the biological process(es) in which a protein participates  [Database homolog search tools  .Iterated homology search methods such as PSI-BLAST  have beeDomain-based annotation and protein structure prediction libraries of profiles or hidden Markov models (HMMs) for functional or structural domains  annotations , PFAM doSlightly more than half of the books in the PhyloFacts resource represent experimentally determined structural domains; the remaining fraction is divided between global homology groups , conserved regions, motifs, and 'Pending', a label for those books that have not passed the stringent requirements for global homology and must be manually examined. Each book is labeled with the book type  to enable appropriate functional inferences. These labels are based primarily on multiple sequence alignment analysis. See Table The PhyloFacts phylogenomic resource can be used in several ways: sequences can be submitted for protein structure prediction or functional classification, protein family books can be browsed, and data of various types , phylogenetic trees, HMMs, and so on) can be downloaded from the resource.Each of the books in the library has a corresponding web page  for viewClassification to a protein family is enabled by HMM scoring. Biologists can submit either nucleotide or amino acid sequences in FASTA format; nucleotide sequences are first translated into all six frames and analyzed separately. Batch mode submission of up to five sequences is enabled. Results are returned by e-mail, and allow users to select families for more detailed classification of sequences to functional subfamilies based on scoring against subfamily HMMs Figure . This fuPhyloFacts includes books focusing on specific protein families or classes. The largest of these series is the PhyloFacts 'Protein Structure Prediction' library, with 5,328 books, each representing either a structural domain from the Astral database  or proteThe second major book series in PhyloFacts is the 'Animal Proteome Explorer' library, containing 4,226 protein families in the human genome, expanded to include additional homologs from other organisms. Specialized sections of the Animal Proteome Explorer series are devoted to protein families of particular biomedical relevance: G-Protein Coupled Receptors (65 books), Ion Channels (50 books), and Innate Immunity (52 books). The Animal Proteome Explorer series has been constructed using GHGCluster (see section 'Details on Library Construction and Software Tools'). The GPCR library includes books for protein families based on the classification of the GPCRDB .R (resistance) genes, proteins involved in defense signaling and effector proteins from plant pathogens.The 'Plant Disease Resistance Phylogenomic Explorer' forms the third main series of specialized books in PhyloFacts, devoted to protein families involved in plant disease resistance and host-pathogen interaction (105 books). Families in this series include the canonical plant These three main divisions are not strictly distinct, and there are some overlaps. For instance, a book for the Toll Interleukin Receptor (TIR) domain (PhyloFacts book ID: bpg002615) is placed in the Protein Structure Prediction library (due to the presence of a solved structure for this family) as well as in the Innate Immunity and Plant Disease Resistance libraries .Because our recommended protocol for protein function prediction starts with transfer of annotation from globally alignable orthologs , a large number of books in PhyloFacts are designated as type Global Homology, and subjected to rigorous quality control . Standard protein clustering tools typically ignore the issue of global sequence similarity, so that even resources intending to cluster proteins based on global similarity can occasionally fail  sequPhyloFacts currently includes almost 10,000 books providing pre-calculated phylogenomic analyses for protein superfamilies and structural domains, and over 700,000 HMMs enabling classification of user-submitted sequences to families and subfamilies. Between 64% and 82% of genes encoded in different model organism genomes can be classified at least at the domain level to one or more books in the PhyloFacts resource Table . PhyloFaIn an ideal scenario, annotation transfer between a query and homolog would meet three criteria : first, Books in the PhyloFacts resource are labeled by the level of structural similarity across members , and include phylogenetic trees, inferred subfamilies, and GO annotations and evidence codes to enable a biologist to check for agreement at the three criteria for transferring annotations. In cases where a protein of unknown function is placed in a global homology group with an ortholog having experimentally determined function, annotation transfer can proceed with high confidence. In other cases, the biologist can check for experimentally determined function in paralogous genes (bearing in mind that functions may have diverged), or at domain-based clusters, to obtain clues to the molecular function for different regions of a protein of interest. We attempt to accommodate all of these possibilities; a sequence search against the resource may match books representing global homology groups, structural domains, conserved regions, or even short motifs, all of which are presented to the user Figure .We note that while domain-based annotation is inherently less precise, PhyloFacts does provide predicted functional subfamilies within domain-based books as well as within books representing global homology groups. While annotation transfer across proteins having different overall folds is prone to systematic error, previous results suggest that subfamily classification of sequences aligned along a single common domain can be consistent with the overall domain structure and molecular function of sequences . Our expIn addition to the value PhyloFacts presents to a human investigator, it also provides a framework for the development of a fully automated functional inference system. A new generation of probabilistic methods for inferring molecular function automatically has arisen in recent years . Drosophila community is likely to use different names for a gene to that used by the Caenorhabditis elegans community, and both are likely to use different terms to those used by investigators working in mouse genomics. The value of a standardized nomenclature, such as that being developed by GO, is obviously important, but significant work remains in this area. An exhaustive thesaurus of equivalent biological terms would be valuable. The sparse nature of experimentally supported molecular functions provides an additional barrier to automated approaches. We discuss these issues further in the section 'Challenges to phylogenomic inference'.However, technical issues present barriers to the goal of fully automated function prediction .Oryza sativa GenBank protein AAR00644 is labeled as a 'putative LRR receptor-like protein kinase'. The canonical structure of receptor-like kinases (RLKs) consists of an extracellular leucine-rich repeat (LRR) region, a transmembrane domain, and a cytoplasmic kinase domain; AAR00644 contains no kinase domain. On the other hand, AAR00644 does match the canonical structure of closely related receptor-like proteins (RLPs), which are structurally very similar to RLKs, except that they terminate with a short cytoplasmic tail, and do not contain a kinase domain [For instance, the e domain . In the AAF19052 labeled as 'neutral human sphingomyelinase' [For a second example, the GenBank sequence elinase'  appears Lastly, G-protein coupled receptor (GPCR) classification is notoriously difficult, with many receptors having no known ligand (termed 'orphan receptors'). One such orphan, a GPCR from river lamprey (UniProt: Q9YHY4), is annotated as 'Putative odorant receptor LOR3', based on its expression in the olfactory epithelium . StandarAnomalous annotations such as these are often signs that annotation transfer has gone wrong. In other cases, anomalies may be quite real and provide new insights into the evolution of novel functions in a family. Automated anomaly detection faces the same technical barriers as automated functional annotation, including the need for probabilistic inference of gene function, standardized nomenclatures and exhaustive synonym tables of biological terms. At present, these anomalies - whether true functional differences or database annotation errors - are detected manually. In the future we expect automated function prediction methods will enable anomalous annotations to be flagged for expert examination. Protocols will then need to be established by the biological community to correct any errors and to ensure that sequence databases receive corrected annotations.Construction of the PhyloFacts resource required the development of a computational pipeline  . SequencFlowerPower is an iterative homolog detection algorithm like PSI-BLAST that retrieves homologs to a seed sequence (or query) and aligns sequences using profile methods. However, instead of using a single profile to identify and align new sequences, FlowerPower uses subfamily identification and subfamily HMM construction to expand the homology cluster in each iteration. Alignment analysis is used to restrict the cluster to match user-specified criteria  for domain-based clustering). Experimental validation of FlowerPower shows it has greater selectivity than BLAST, PSI-BLAST and the UCSC SAM-T2K methods of homolog detection at discriminating sequences with local similarity from those with global similarity. The FlowerPower server is available online .The Global Homology Group (GHG) Cluster program enables us to cluster a selected sequence database  into global homology groups, while also including homologs from a second, generally larger, database.Q, containing the sequences to be clustered, and a database D to use for expanding the clusters to include globally alignable homologs from other organisms. A superset of sequences, the expansion database E, is created by merging Q and D. To improve run time, E is partitioned into overlapping bins based on sequence length. A seed sequence (query) is chosen from Q and homologs are gathered from its corresponding bin in E, using PSI-BLAST . Each hit is assessed for global homology to the query, based on percent identity (≥20%), and bi-directional alignment coverage, that is the fractional aligned length of both seed and hit (ranging from 60% for sequences <100 residues to 85% for sequences of >500 residues). In some cases, PSI-BLAST returns multiple short aligned regions, none of which is long enough to pass the above requirements. In these cases, the failing hits are realigned to an HMM built from the seed, followed by alignment analysis. The seed and any accepted sequences are defined as a cluster and removed from Q (but not E). A new seed is then chosen from Q and the process is iterated until Q is empty.GHGCluster takes two inputs: a set of sequences A; the other cluster becomes B. We build an HMM from the cluster A alignment and use the COACH algorithm [B alignment to this HMM (and therefore to cluster A). If the total fraction of gap characters in the merged alignment ) is less than 20%, and the mean percent identity is greater than 20%, the merge is accepted. Otherwise, if the alignment fails based on gap content alone, we trim columns with >30% gaps from the amino and carboxyl termini and reassess: the merge is accepted if cluster sequences align at least 70% of their residues, on average, within the trimmed 'core' alignment and if the core alignment contains >50% of the columns from the merged alignment and <10% gaps. Note, this procedure is computationally efficient, but can produce clusters that fail to meet the criteria set for global homology group books. In these cases, books are flagged for additional automated analysis and manual inspection in order to maintain quality control.This procedure results in a set of clusters, some of which may contain the same sequence(s). At this stage we merge compatible overlapping clusters. We rank all pairs of clusters by the number of sequences they have in common and attempt to merge pairs in order. For each pair, we choose the alignment with the greater number of aligned columns and designate this as cluster lgorithm  to alignGHGCluster was used to cluster the human genome in the construction of the PhyloFacts Animal Proteome Explorer, including homologs from NR, using three iterations of PSI-BLAST.Our atomic unit in PhyloFacts clustering is the protein sequence, independent of its origin. Consequently, splice and allelic variants of a gene are not handled differently to genes from entirely different species during clustering, alignment and tree construction . If the variant retains the same domain structure and is globally alignable to other isoforms, it will be included in the global homology group cluster for those genes, otherwise it may end up in a different book in the resource. It should be noted that the presence of different isoforms for a gene can cause difficulties in phylogenomic inference if their common genome locus is not evident. Future releases of PhyloFacts will display this information and create links between different gene isoforms present in different books.The alignment method is selected based on the type of book. For alignments of global homology group proteins, we normally use the MUSCLE software  to realiTo be defined as a 'Global Homology' book, a multiple sequence alignment must meet the following criteria: first, ≤15% gap characters over the multiple sequence alignment; second, ≤30% columns with BLOSUM62  sum-of-pSubfamily identification is provided using the SCI-PHY (Subfamily Classification In Phylogenomics) software . SCI-PHYThe UCSC Sequence Alignment and Modeling (SAM) software is used to construct HMMs and in scoring sequences against HMMs . This soPFAM domains are identified using the consensus sequence for the family as a query using the PFAM gathering threshold as a cutoff. Matches to PDB structures are predicted by BLAST analysis of the family consensus sequence, using an E-value cutoff of 0.001 . Any putative homologous structures were aligned to the family HMM using local-local alignment (SAM parameter - sw 2). Transmembrane domains and signal peptides are predicted using the PHOBIUS server , selecteBecause many of the protein superfamilies in the PhyloFacts resource span extremes of evolutionary divergence , tree topologies produced by different methods can often disagree. For this reason, most of the protein families in the resource contain several phylogenetic trees built using different algorithms, enabling biologists interested in these families to examine the differences and commonalities between the trees.Neighbor-Joining trees are constructed using the PHYLIP software , using tPhylogenetic trees are displayed using ATV, a Java-based tree viewer . Users cc, we compute the log-odds of the positional conservation and the background conservation in the MSA: log (Fc/F). Here, Fc is the frequency of the most frequent amino acid at column c, and F is the average value of Fc over the multiple alignment. Subfamily-defining positions are computed similarly, based on a cut of the MSA into subfamilies using the SCI-PHY algorithm, and then averaging log-odds values across the subfamilies at each position. Positive log-odds values indicate higher-than-average conservation at a position, whereas log-odds values below zero reflect conservation that is lower than average; the magnitude of the log-odds gives a measure of the significance of the result. Computing the log-odds instead of the conservation per se enables us to differentiate truly informative positions from those that only appear conserved due to limited sequence divergence in the multiple alignment. Our default cutoffs for coloring residues based on this analysis are 0.7 for family conservation and 0.07 for subfamily conservation; cutoffs can be adjusted by the user. Conservation patterns are plotted on protein three-dimensional structures for the family using an interactive Java-based structure viewer, Jmol , we provide heuristic approaches enabling rapid classification of user-submitted sequences. For computational efficiency, we select books for HMM scoring via BLAST analysis of the submitted query against a dataset of over 2.5 million PhyloFacts training sequences using an E-value cutoff of 10; this significantly reduces the number of HMM scores required without affecting sensitivity. Users can override this 'BLAST pre-screen' protocol using the 'Advanced' settings page. Books retrieved based on either protocol can then be selected for scoring the submitted sequence(s) against subfamily HMMs for additional specificity of functional classification.C. elegans, D. melanogaster), and to keep our protein structure prediction library current. We plan to reduce redundancy in the library by combining books with significant sequence overlap. We will develop software tools to identify and include new family members as well as new experimental data . We will also include extensions to the subfamily HMM scoring protocol to differentiate between sequences representing a novel subtype and those that can be classified to the top-scoring subfamily . Phylogenetic trees for each family will be extended to include strict consensus trees across two or more methods, and bootstrap analysis will be provided for Neighbor Joining trees. Finally, we plan to provide community annotation tools to enable biologists to upload their data, commentaries and hyperlinks to experimental data for members of protein families.In future releases of the PhyloFacts resource we plan to include automated predictions of protein function using the SIFTER software. SIFTER will be used to provide predicted molecular functions and participation in biological processes for SCI-PHY subfamilies as well as for conserved evolutionary clades. Links between proteins, or between books, will be provided, to reflect the many types of relationships . Users will be able to navigate between proteins in the same pathway using the Cytoscape software, in which links to PhyloFacts books will be embedded. Users will be able to retrieve comparative (homology) models for selected proteins from the ModBase resource  through Phylogenomic analysis of protein function is known to improve the accuracy of functional annotation, but has had restricted application due to its technical complexity. The PhyloFacts resource enables biologists who may have limited bioinformatics expertise to take advantage of pre-computed phylogenomic analyses for hundreds of thousands of proteins. New sequences can be classified to families and subfamilies using over 700,000 hidden Markov models, for increased functional specificity. The resource as a whole brings together many different types of bioinformatics analyses and data; the integration of these data and improved orthology determination enables biologists to avoid making new annotation errors at the outset, and to detect, and possibly correct, existing annotation errors.Phylogenetic uncertainty and ambiguity remain significant challenges to phylogenomic inference of molecular function. Consensus analysis can be used to detect clades with support across two or more tree methods, but this approach could inadvertently be misleading if inherent biases in the methods are not taken into account. It is critical that the computational biology community and the systematics community work together to develop methods to assess the expected accuracy of phylogenetic methods for protein superfamily reconstruction; new simulation studies should be developed that model the kinds of structural and functional changes observed in protein superfamily evolution.Other challenges to automating functional annotation through phylogenomics methods include: the lack of a standardized nomenclature for gene names across different model organisms, although the GO consortium efforts are making progress in this respect; natural structural and sequence divergence among family members causing difficulties in clustering; ensuring multiple sequence alignment accuracy when divergently related sequences are included in a cluster; and the persistence of database annotation errors. Database annotation errors should be correctable at the source, so that the primary sequence repositories  can be kept current.One of the fundamental questions in phylogenomic inference of protein function is determining the evolutionary distance within which annotations may be transferred. Given the paucity of sequences with experimentally determined function, if annotations can only be transferred between orthologs , the vast majority of unknown sequences will remain without predicted function. Is this necessary or overkill? Analysis of different types of 'function' associated with proteins show that some types of attributes  persist over large evolutionary distances, while in other cases , functions can diverge extremely rapidly. Moreover, the degree to which different types of function persist over evolutionary distance can vary from one family to another. One intriguing possibility for the next generation of phylogenomic inference methods involves identifying attribute-specific evolutionary distances over which attributes may percolate.de novo function prediction efforts, analogous to the biennial CASP protein structure prediction experiments, are underway [Finally, assessing annotation accuracy is a very labor-intensive practice. Biological curators can spend days analyzing and annotating a single gene; to do this in high-throughput for thousands of sequences is clearly not feasible. An additional complication is that definitions of molecular 'function' or 'subfunction' are not at all standardized within biology. Instead, some biologists use the term 'function' very specifically  while ounderway , and areThe following additional data files are available with the online version of this paper. Additional data file Brief comparison of the Panther resource with PhyloFactsClick here for fileIllustration of detection of potential annotation errors using PhyloFacts analysesClick here for file


Basic region-leucine zipper (bZIP) proteins are a class of transcription factors (TFs) that play diverse roles in eukaryotes. Malfunctions in these proteins lead to cancer and various other diseases. For detailed characterization of these TFs, further public resources are required.We constructed a database, designated bZIPDB, containing information on 49 human bZIP TFs, by means of automated literature collection and manual curation. bZIPDB aims to provide public data required for deciphering the gene regulatory network of the human bZIP family, e.g., evaluation or reference information for the identification of regulatory modules. The resources provided by bZIPDB include (1) protein interaction data including direct binding, phosphorylation and functional associations between bZIP TFs and other cellular proteins, along with other types of interactions, (2) bZIP TF-target gene relationships, (3) the cellular network of bZIP TFs in particular cell lines, and (4) gene information and ontology. In the current version of the database, 721 protein interactions and 560 TF-target gene relationships are recorded. bZIPDB is annually updated for the newly discovered information..bZIPDB is a repository of detailed regulatory information for human bZIP TFs that is collected and processed from the literature, designed to facilitate analysis of this protein family. bZIPDB is available for public use at  Transcription factors (TFs) are responsible for gene expression in every living organism. The bZIP family shares a basic region and a leucine zipper domain. Homo/hetero-dimerization between family members is possible through the leucine zipper domain, and the proteins bind target promoters via the basic amino acid-rich region . The bZIWith the assistance of high-throughput technology, such as microarray technology, several researchers have attempted to decipher the regulatory networks of bZIP TFs -7. HowevTo facilitate our understanding of these proteins, we have generated a bZIPDB database containing regulatory network information on the human bZIP TF family. In particular, we focus on the signaling protein-TF interactions, TF-TF interactions, and TF-target gene interactions that are important for regulatory network analysis with high-throughput technology.The aim of bZIPDB is to accumulate known regulatory information on human bZIP TFs, particularly protein-protein and protein-DNA interactions. A list of human bZIP TFs with the appropriate synonyms is documented on our website. For database construction, public literature dealing with human bZIP TFs, including official symbols and synonyms, was initially obtained from PubMed  using weThe regulatory network of the bZIP family is grouped into six tables, depending on specific attributes. The system architecture of bZIPDB is depicted in Figure bZIP_TF_INFO: Basic information on human bZIP TFs, such as bZIPDB ID, official symbol, RefSeq ID and transcript variants.• GENE_INFORMATION: Information of the chromosomal loci and exons of human bZIP TFs.• PPI: Protein-protein interactions between bZIP TFs and other proteins.• TF_TARGET: bZIP TF-target gene promoter interactions.• CELL_LINE: Experimental cell lines and their origin.• TOI: Types of protein-protein interactions.• For 49 human bZIP TFs, bZIPDB ID was assigned on the basis of the distinct mRNA transcript. Since alternatively spliced or transcribed products encoded by the same gene have different biochemical properties , we assiIn the construction of a protein-protein interaction table, information on interaction types, directions of interactions, and cell lines is collected in addition to the identities of interacting proteins. While several databases have focused on the direct binding of proteins acting as complexes ,12, cellThe target genes of TF are less well characterized, compared to protein-protein interactions. For transcription, TFs bind to specific DNA sequences in the proper orientation, which is influenced by nearby proteins, such as histone or other TFs. While known TF-target gene relationships from a few databases have been used as positive examples, their number is too limited to constitute a positive dataset. For example, 51 mammalian target genes of human JUN protein are recorded in TRANSFAC 10.4 , while wAnother unique aspect of bZIPDB is the compilation of regulatory information for particular cell lines. Each cell line originates from different organisms or tissues, which maintain unique genetic and epigenetic compositions, hence affecting various cellular interactions. Therefore, careful consideration is required when data from several resources are used in conjunction. To clarify the distinctive cellular networks and accuracy of interactions, bZIPDB provides the list of regulatory interactions conducted in a particular cell type. Table In addition to protein-protein and protein-DNA interaction data, genomic information, such as chromosomal locus and exon/introns, synonyms and functional annotation, was obtained from Entrez  and the bZIPDB provides a convenient search engine with which users can explore the database either by typing bZIP TF names within the query box or by clicking on the listed names Figure . The knoThe results page returns basic information, such as names, RefSeq ID, chromosomal locus and exon/intron positions of the bZIP TF protein examined. By clicking on the 'Protein interaction' or 'Target genes' menu on the right side of the results page, researchers can recover detailed reports on protein-protein or protein-DNA interactions of bZIP TF, respectively Fig.  and 2C tInteractions within specific cell lines can be viewed on the 'Cellular Network' page Figure . In totabZIPDB aims to serve as a portal for researchers studying the human bZIP TF family. To date, the database has focused on amassing the relevant literature data. However, the updated version of bZIPDB will provide other types of data. One data category involves the potential target genes of bZIP TFs, which are computationally predicted using phylogenetic footprinting and motif search algorithms ,20. AnotbZIPDB contains extensive information on human bZIP TFs, such as manually curated protein-protein and protein-DNA interactions, genomic information, synonyms, and gene ontology. Moreover, this novel database provides classified interaction data for popularly used cell lines, leading to a clearer picture of the cell type-specific subnetwork. Thus, bZIPDB constitutes a valuable resource to facilitate comprehensive understanding and analysis of the cellular network of human bZIP TFs.bZIPDB home page : Operating systems(s): LinuxProgramming language: JavaLicense: the database is freely available to academic and non-academic users.Basic region-leucine zipper (bZIP), transcription factors (TF), transcription factor binding site (TFBS), protein-protein interaction (PPI)TR designed the database and drafted the manuscript. JJ, SL, HJN, SWH and JWY participated in data curation. Dong-ki Lee and Doheon Lee supervised the work.


The recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. Specifically, the availability of information about experimental protein-protein interactions and other protein features on the Internet enables human protein-protein interactions to be computationally predicted from co-evolution events (interolog). This study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. Computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction.This study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. The scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs. The predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms – rat, mouse, fly, worm, thale cress and baker's yeast.Evaluation results of the proposed method using functional keyword and Gene Ontology (GO) annotations indicate that some confidence is justified in the accuracy of the predicted interactions. Comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods. Drosophila melanogaster , the presence or absence of a keyword are represented as 1 or 0, respectively. Protein self-interactions or homo-dimers tend to have high scores, and always share the same functional annotations. Hence, these interactions were eliminated from the predicted pairs to eliminate bias in the results.where First, the number of interaction pairs sharing at least one UniProt overlapping functional keyword was determined to verify the accuracy of the predicted interactions. Second, the number of interaction pairs sharing common GO annotations at a particular depth in the GO 'molecular function' and 'biological process' hierarchy was analyzed to confirm that the results and that were not just a general GO term applied. Comparisons were made among KNOWN2, TP4, TP0, BTP, PU4, PU0, BPU and RANDOMS data sets Figure .p-value is obtained by the following equation:Finally, the probability that two proteins share the same UniProt functional keyword by chance is determined through the hypergeometric distribution . The p-vN and M denote the total number of proteins in the population, and the number of proteins that have a particular functional keyword, respectively, and n and x denote the total number of proteins in the set, and the number of proteins annotated with the particular functional keyword, respectively. Since a pair of proteins is observed, both n and x are equal to 2. A protein pair is treated as enriched by a UniProt functional if the corrected p-value is ≤ 0.05. The total of 90, 871 predicted interactions with this p-value are listed  and interolog scores (I) based on the quasi-clique of protein networks. The raw conservation score and interolog score and corresponding standard error of true positive and putative interaction data sets from different InParanoid score (0.0 to 1.0) were evaluated. The result reveals that the conservation and interolog scores in the true positive data set were higher than those in the putative data set.Table Table CS), equation (8), was identified to increase the true positive ratio and indicate the relationship between the number of predicted interactions and the coverage of known interactions. The maximum precision was obtained by a threshold of CS ≥ 4. Table The cut-off threshold of confidence score (E ≤ 0.005 was configured in the BLAST tool). The protein sequences were downloaded from UniProt. The InParanoid one-to-one mapping (InPranoid score = 1.0) and one-to-many mappings (InPranoid score ≥ 0.0) were also compared, as were the InParanoid data sets with threshold CS = 4. Table All of the 175, 085 known interactions . Surprisingly, the results of the proposed interolog-based approach and the experimental high-throughput method did not overlap significantly, revealing that the methods applied to detect interactions have different biases. Therefore, two methods  may reveal different and partial sub-networks of the whole human protein interaction network. The proposed method is based on evolutionarily conserved interologs, and can not distinguish between species-specific interactions from the two experimental data sets.All of the proteins were mapped to UniProt Entry ID, and proteins (and their interactions) that could not be confidently mapped were eliminated. Figure et al.'s group [et al.'s method [The proposed method was compared with other interolog-based methods for predicting human PPIs, namely HomoMINT , HPID 2, IPPRED 's group , OPHID ['s group , POINT ['s group  and Rhods method .The properties of the ortholog identification methods and other features are as follows.IP). Although similar structures typically share similar biological functions, the structural classification at the protein superfamily level (SS) is not trivial in the identification of structural similarities at the human protein level on the large scale.• An ortholog identification method indicates the orthologs between model organisms. Orthologs between organisms do not have a one-to-one relationship with BLAST search (B) or BLAST search with E-value (BE); yet one-to-many and many-to-many mappings exist. The InParanoid clustering algorithm distinguishes potential true orthologs from paralogs according to the InParanoid score (C), domain-domain combinations (D), sub-cellular localization (L), cell-cycle phase (P) and tissue-specificity (T) were also carefully examined in this study. Other existing methods apply the 'biological process' (BP) and 'molecular function' (MF) annotations in the GO hierarchy.• Other features indicate that some other factors affecting their interactions are considered. The quasi-clique with maximal conservation score (The brief comparisons in Table C) and interolog (I) scores (Table Many predicted pairs have been identified in existing known human PPI databases (KNOWN) and the two human experimental PPIs data sets  from reference to target organism, and the interolog score (I) from the orthologous scores (IP) and (C). The other features of the protein interaction, such as spatial proximity  and tissue-specificity (T)), temporal synchronicity (cell-cycle phase (P)) and domain-domain combinations (D) are also considered. Each score is normalized, and then these scores are summed into the final confidence score (CS). Figure Protein-protein interactions can be represented as a network graph whose vertices are proteins. These vertices are linked by edges if the corresponding proteins interact. In this study, the maximal quasi-clique determines a conservation score  denote a graph, where V is the set of vertices, and E is the set of edges in graph G. A graph is γ-dense, such that γ = 2 |E|/|V| (|V| - 1). For a subset S ⊆ V, GS is the sub-graph induced by S. A quasi-clique, also called a γ-clique S, is a subset of G, such that the induced graph GS is connected and γ-clique. The original maximum problem γ-clique S is to find a 1-clique, complete sub-graph (γ = 1) with maximum vertices in graph G.Let γ = 1) is not always biologically significant. Hence, C = γ |E| is defined as the protein complex conservation score. The value of |E| is the functional links of a protein complex.A quasi-clique in PPI networks is a group of proteins that tend to interact with each other, but a complete sub-graph , , , ,  and  can be inferred to interact in a target organism, and the interolog score (I) can be determined as follows.The interolog concept states that proteins that interact in a single organism co-evolve so that their respective orthologs maintain the ability to interact in another organism. For example, as shown in Figure wec) is defined such that a higher wec value indicates an organism that is genetically closer to humans. The following wec values were considered: wrat = 1.0, wmouse = 1.0, wfly = 0.75, wworm = 0.75, wthalecress = 0.5 and wyeast = 0.25 for rat, mouse, fly, worm, thale cress and baker's yeast, respectively. Because rat and mouse are both mammals, and are thus genetically closest to human, they were assigned the highest value of 1.0. Drosophila and C. elegans are two animal models that are widely studied to understand human disease genes and development, and are ranked second closest to humans among the organisms studied. Finally, thale cress is sorted in higher order than yeast, since it is multi-cellular organism, while yeast is a single-cell species. If a pair of human protein interactions is derived from two or more reference model organisms, then only the highest interolog score is used to generate non-redundant (nr) human protein-protein interactions.The weight of evolutionary conservation  and N  are the number of interacting protein pairs and the total number of protein pairs that contain  in known interactions, respectively.where The tissue specificity is another spatial proximity value to be considered. Two proteins that are activated at the same sub-cellular localization, and co-expressed in the same tissue, are likely to interact with each other. This information can be used to discover tissue-specific PPIs associated with human diseases for biomedical research. Tissue-specific gene expression information was extracted from the GeneAtlas Affymetrix data set, which includes 44, 775 human probe sets  from 79 normal human tissue samples .T denotes the tissue specificity score, calculated by summing the number of common tissues if two proteins both have 2-fold up-regulated expressions (log2 expression ratio = 1) than the mean expression value of specific tissue.Score eAi and eBi are the normalized expression values of proteins A and B, respectively, in tissue sample i, and where The physical PPI requires contact between two proteins at certain cellular locations. Hence, this study used the Gene Ontology (GO)  annotatiL is the sub-cellular localization score, which is the deepest level number of the common GO term among ancestor terms (including itself) in the GO hierarchy. For example, a protein pair  has the GO cellular component annotation 'GO:0005623 cell' and 'GO:0005819 spindle' at depths of 2 and 8, respectively. The sub-cellular localization score L = 2 since the deepest level of common GO term among ancestors is at a depth of 2 in the GO hierarchy. Figure If two interacting proteins share a common ancestor of the GO term, then P is given by the number of cell cycle phases in the overlap between two interacting proteins.Human cell cycle cDNA microarray analysis  reveals I, D, T, L, P) is an overall confidence score determined from equation (8), where the The five-tuple score  scores, normalized C, I, D, T, L, P, CS and p-value, respectively.Click here for fileDistributions of the various components of the confidence metrics and ANOVA tests between different interaction data sets. This file contains distributions of the various feature scores  and ANOVA tests between different interaction data sets, i.e. KNOWN2, TP4, TP0, BTP, PU4, PU0, BPU and RANDOMS.Click here for fileTop 20 total predicted putative interacting protein pairs not present in existing experimental data sets (KNOWN2). The top 20 predicted putative interacting protein pairs are listed with their OMIM ID, GO 'molecular function' and 'biological process' annotations and UniProt functional keywords.Click here for file


Activated Protein C (ProC) is an anticoagulant plasma serine protease which also plays an important role in controlling inflammation and cell proliferation. Several mutations of the gene are associated with phenotypic functional deficiency of protein C, and with the risk of developing venous thrombosis. Structure prediction and computational analysis of the mutants have proven to be a valuable aid in understanding the molecular aspects of clinical thrombophilia.We have built a specialized relational database and a search tool for natural mutants of protein C. It contains 195 entries that include 182 missense and 13 stop mutations. A menu driven search engine allows the user to retrieve stored information for each variant, that include genetic as well as structural data and a multiple alignment highlighting the substituted position. Molecular models of variants can be visualized with interactive tools; PDB coordinates of the models are also available for further analysis. Furthermore, an automatic modelling interface allows the user to generate multiple alignments and 3D models of new variants..ProCMD is an up-to-date interactive mutant database that integrates phenotypical descriptions with functional and structural data obtained by computational approaches. It will be useful in the research and clinical fields to help elucidate the chain of events leading from a molecular defect to the related disease. It is available for academics at the URL  Activated protein C (APC) is a vitamin K-dependent serine protease that plays a central role in the regulation of blood coagulation. It is the key component of an anticoagulant system that provides an essential mechanism in thrombosis prevention and in inflammatory response control.APC binds to its cofactor protein S, and the complex inactivates the two cofactors involved in the clotting cascade, factors Va and factor VIIIa, leading to the efficient inhibition of the coagulation process. Furthermore APC has direct and indirect anti-inflammatory actions. It prevents leukocyte rolling, tissue factor exposure and tumour necrosis factor production by monocytes, thrombin-mediated inflammatory actions and apoptosis of endothelial cells . The antProtein C, the zymogen of APC, is synthesized as a single chain precursor containing an amino-terminal leader sequence followed by a propeptide, which are cleaved upon secretion. It circulates in plasma mostly as a two-chain zymogen obtained by removal of the dipeptide Lys198 and Arg199 that results in the formation of two chains  linked by a disulphide bridge between Cys183 and Cys320. The zymogen is activated by thrombin through the proteolytic cleavage of a 12 amino acid peptide (Asp200-Arg211) .Protein C has a multi-domain structure: the light chain contains a γ-carboxyglutamic acid (Gla)-rich membrane binding domain and two epidermal growth factor (EGF)-like modules, while the heavy chain has the form of a typical trypsin-like serine protease domain .The structure of the Gla-domainless APC has been solved by X-ray crystallography , while tMutations on the gene have been found in patients with protein C deficiency OMIM 176860), a disorder associated with development of purpura fulminans and severe recurrent thrombotic events in the homozygous form while, in the heterozygous form, it is responsible for an increased risk of venous thromboembolism in early adulthood 60, a dis.Phenotypically, two distinct types of protein C deficiency are recognized: type I deficiency, the most common, is characterized by a parallel reduction in protein C concentration and function (measured in plasma by amidolytic or anticoagulant methods); type II deficiency is identified by normal or increased concentration and reduced function .A large number of the mutations that contribute to the protein C deficiency fall in the structurally solved domains and thus are amenable to homology modelling and computational analysis.Structural models can be useful in the research and clinical fields to elucidate how a mutation may interfere with enzymatic activity, ligand binding and cofactor interaction and relate the effect to patient phenotype.In the last published database of mutations of the protein C gene ,11, 161 We describe here an updated, 3D-structure oriented database of protein C that associates clinical and phenotypical descriptions with functional and structural data obtained by computational approaches. It includes the description of 21 new variants that we have identified and analyzed in a previous work 17]. The. The17].We collected a total of 195 naturally occurring mutations in the coding region of the protein C gene that include 182 missense and 13 stop mutations. Of these, a set of 21 variants were identified by our group through the screening of the protein C gene of 42 patients with a phenotypic functional deficiency of protein C . The remGeneral information associated with each entry was derived from literature sources. A multiple alignment obtained with CLUSTALW  using a Variants with substitutions in the structurally solved regions of the protein C, have been modelled starting from X-ray coordinates (PDB entry 1AUT). Molecular modelling of the 21 variants identified by our group, were achieved by residue replacement using InsightII . The lowest energy rotamer was chosen as the starting side chain position, followed by energy minimization calculation consisting of 500 steps of steepest descent keeping the backbone fixed, followed by 500 steps of conjugate gradient on the whole structure. A detailed computational analysis of these variants, such as electrostatic potential calculations, was formerly carried out and the results are also stored in the database.All other variants were modelled using an automatic approach based on an adapted Python script of Modeller . The scrThe data are stored in a relational database managed by a MySQL Database Management System . The datWe created a web based interface with the aim of helping users search for specific information, to browse the entire database or to visualize 2D and 3D images of the variants. The web site is also a source of documentation about protein C, and a point of access to external resources and databases related to protein C. This user interface has been built with PHP language scripts  on an ApAll entries in the database are labeled by an unique identifier and may include the following fields:- sequence position of the mutated residues numbered according to:UniProtKB/Swiss-Prot entry P04070, Foster's codon numbering  and the - wild type and mutated residues;- gene localization;- clinical or laboratory phenotype data as obtained from other database reports or from the literature;- links to PUBMED database;- cross-references to other databases reporting the mutation.- There is also a structural information section that assigns the mutated residue to its specific chain, secondary structure and domain localization. A multiple alignment of homologous sequences, a 3D gallery of structural images and the PDB coordinates of the mutants are also present. Results of computational analysis are collected in a 3D-notes page that includes considerations on the physico-chemical properties of the mutant residue compared to the wild-type, , a list of hydrogen bonds and hydrophobic interactions. Additional information resulting from further computational studies, such as electrostatic potential calculation, and the prediction of structural-functional effects of the mutation are associated with some entries.The ProCMD database aims to provide a summary of the sequence and structure information on variants with substitutions in the coding region of the protein C gene. The database is interfaced with a fully interactive website, through which the user can retrieve entries of interest, find cross-references and visualize structural models with interactive tools.The home page of ProCMD web tool is shown in figure A query page allows the user to retrieve entries by the position in the sequence of a mutated residue, by amino acidic substitution, and by domain localization. Results of the query are listed in a table showing the amino acidic substitution and the sequence position for each entry, and provide links to details pages summarizing all the associated data.An example of the output is shown in figure Results of more detailed computational analysis and interpretation of the effect of mutation, when available, appear in a 3D-notes page with the corresponding images  and is available at the following URL ProCMD:protein C mutation database, PDB:Protein Data Bank, APC: Activated Protein C, HGMD: Human Gene Mutation Database, VRML: Virtual Reality Modelling Language.PD conceived and designed the database and drafted the manuscript, FM carried out the data collection and annotation, implemented the SQL database and the web server pages and drafted the manuscript, AC structured the database and supervised the implementation, LM contributed to manuscript revision, EMF contributed to data communication and critically revised the manuscript, ER coordinated and supervised the project and prepared the manuscript. All authors read and approved the final manuscript


Recent advances in high-throughput experimental methods for the identification of protein interactions have resulted in a large amount of diverse data that are somewhat incomplete and contradictory. As valuable as they are, such experimental approaches studying protein interactomes have certain limitations that can be complemented by the computational methods for predicting protein interactions. In this review we describe different approaches to predict protein interaction partners as well as highlight recent achievements in the prediction of specific domains mediating protein–protein interactions. We discuss the applicability of computational methods to different types of prediction problems and point out limitations common to all of them.In our companion review published in the last issue , we outlIn this review, we report on several methods to predict protein or domain interaction partners. Some computational methods are based on the co-localization of potentially interacting genes in the same gene clusters or protein chains , on co-evolution patterns in interacting proteins (sequence co-evolution methods), and on the co-expression of genes. Some methods find patterns of co-occurences in interacting proteins, protein domains, and phenotypes , while others use the presence of sequence/structural motifs characteristic only for interacting proteins . To analyze interaction specificity at the domain level, in this second paper of the review we describe methods that are aimed at identifying specific domains mediating interactions in an interacting protein pair.Genes with closely related functions encoding potentially interacting proteins are often transcribed as a single unit, an operon, in bacteria and are co-regulated in eukaryotes. Different methods have been developed trying to predict operons based on intergenic distances –6 FigurA. DespitN elements, where N is the number of genomes (The phylogenetic profile (PP) method is based on the hypothesis that functionally linked and potentially interacting nonhomologous proteins co-evolve and have orthologs in the same subset of fully sequenced organisms ,15–19. I genomes B. The prSome drawbacks of PP include its high computational cost, its dependence on high information profiles, and homology detection between distant organisms. For example, ubiquitous unlinked proteins present in all genomes  will be counted by PP as correlated. The same is true for proteins that are specific to a given genome . Shared phylogenetic relationships between two proteins can also produce false correlations between profiles. This issue has recently been addressed by incorporating the phylogenetic trees in the analysis of correlated gains and losses of pairs of proteins .Escherichia coli, the Rosetta Stone method found 6,809 potentially interacting pairs of nonhomologous proteins; both proteins from each pair had significant sequence similarity to a single protein from some other genome. Analysis of pairs found by this approach revealed that for more than half of the pairs both members were functionally related [The Rosetta Stone approach infers protein interactions from protein sequences in different genomes –27. It i related . CompariAs was mentioned earlier, interacting proteins very often co-evolve so that changes in one protein leading to the loss of function or interaction should be compensated by the correlated changes in another protein. The orthologs of coevolving proteins also tend to interact, thereby making it possible to infer unknown interactions in other genomes . It has The similarity between two phylogenetic trees is influenced by the speciation process, and therefore there is a certain “background” similarity between trees of any proteins, no matter if they interact or not. Different statistical techniques have been developed to account for “phylogenetic subtraction” . SimplifOne example of how co-evolution studies could be used in confirming and predicting putative interaction partners is the case of DNA colicins and their immunity proteins . ColicinDifferent classification methods have been successfully applied to the prediction of protein and domain interactions –54. ThesMultiple sources of direct and indirect data on protein–protein interactions can be combined in a supervised learning framework or integrative scoring scheme to predict protein–protein and domain–domain interactions ,53,56–60By far the most coverage of experimental data describing protein interaction networks comes from high-throughput experiments giving us the identity of interacting protein pairs (see our previous review ). UnfortThe following section gives an overview of domain prediction methods which are listed in 2(ij/PiPjP), where ijP is the observed frequency of domains i and j occurring in one protein pair; iP and jP are the background frequencies of domains i and j in the data. In this approach predicted domain interactions have been defined as those having positive log-odds scores and having several instances of occurrence of a given domain pair in the database. Using this method, it was found that certain domains can be found quite often in protein interacting pairs and can be used for protein interaction prediction.This group of methods looks for the characteristic sequence or structural motifs which distinguish interacting proteins from non-interacting –74. For ij, fp, fn)θ. To solve this problem, the Expectation Maximization algorithm is used to find maximum likelihood estimates of unknown parameters θ by finding the expectation of the complete data consisting of observed data and unobserved data in two iterative steps. The observed data includes protein–protein interactions and the domain composition of the proteins, and the unobserved data includes all putative domain–domain interactions.The association method which uses correlated sequence signatures  considerθ values. On the contrary, specific, rare interactions between certain members of two domain families can be neglected. The domain pair exclusion analysis method accounts for this by estimating an ijE score which measures the evidence that domains i and j interact and is defined as the logarithm of a ratio of two probabilities. The numerator corresponds to the probability that two proteins interact given that domains i and j interact. The denominator corresponds to the probability that proteins interact given that domains i and j do not interact. To compute E-scores for a given domain pair, the probability in the numerator is calculated with the Expectation Maximization procedure (similar to the one described in the previous section). For the probability in the denominator, the procedure is repeated where the probability for a given pair of domains to interact is set to zero. This allows the competing domains to maximize ijθ.The domain pair exclusion analysis method extends the previously described MLE method and can detect specific domain interactions see C which aθ values and high E-scores. Although this model does not account for false positives and negatives in the experimental data, it was shown that the E-scores perform better than its constituent quantities, finding 2.9 times more true positives than random assignment; for comparison, θ values yield 1.4 times more true positives than random assignments [A high E-score value shows the high propensity of two domains to interact, while a low value indicates that competing domains from the same protein pair are more likely to be responsible for this interaction. Therefore, specific domain interactions can be found by screening for low ignments  .p-value method tests a null hypothesis that the presence of a particular domain pair in a protein pair has no effect on whether two proteins interact [p-values show the reliability of domain interactions given that two proteins interact, and the domain pair with the lowest p-value is most likely to interact. The p-value method performs reasonably well when there are nine or more domains on a protein pair. However, interestingly enough, for the majority of test cases, random domain prediction outperforms all methods tested, pointing to the low accuracy of all prediction methods of domain interactions.The interact . To testThe methods of domain interaction prediction described in this section all have varying degrees of success, but have limitations common to most of them. First, domains are assumed to interact independently, although their interactions can depend on other domains in a protein pair. Second, incomplete domain assignments, due to insufficient coverage of domain databases and limited searching ability of domain profiles, can lead to false positive and negative interaction predictions. Finally, protein interaction data is not complete, whereas domain prediction methods are based on this data.In this paper, we reviewed various computational methods to predict protein and domain interaction partners . All of


Saccharomyces cerevisiae genes, using Gene Ontology (GO) terms as the basis of our annotation. In a cross validation study we show that the integrated model increases recall by 18%, compared to using PPI data alone at the 50% precision. We also show that the integrated predictor is significantly better than each individual predictor. However, the observed improvement vs. PPI depends on both the new source of data and the functional category to be predicted. Surprisingly, in some contexts integration hurts overall prediction accuracy. Lastly, we provide a comprehensive assignment of putative GO terms to 463 proteins that currently have no assigned function.Dramatic improvements in high throughput sequencing technologies have led to a staggering growth in the number of predicted genes. However, a large fraction of these newly discovered genes do not have a functional assignment. Fortunately, a variety of novel high-throughput genome-wide functional screening technologies provide important clues that shed light on gene function. The integration of heterogeneous data to predict protein function has been shown to improve the accuracy of automated gene annotation systems. In this paper, we propose and evaluate a probabilistic approach for protein function prediction that integrates protein-protein interaction (PPI) data, gene expression data, protein motif information, mutant phenotype data, and protein localization data. First, functional linkage graphs are constructed from PPI data and gene expression data, in which an edge between nodes (proteins) represents evidence for functional similarity. The assumption here is that graph neighbors are more likely to share protein function, compared to proteins that are not neighbors. The functional linkage graph model is then used in concert with protein domain, mutant phenotype and protein localization data to produce a functional prediction. Our method is applied to the functional prediction of  Functional annotation of genes is a fundamental problem in computational and experimental biology. The problem can be solved at various levels of resolution ranging from identifying high level processes where a given protein might be associated with, to discovery of the cell specific protein-ligand interaction targets of a protein in different biological conditions. The most established and reliable methods for protein function prediction are based on sequence similarity using BLAST i and j, has a functional relationship, given the various types of genome-wide data. These algorithms output a functional linkage graph Bayesian network methodologies for data integration have been explored Saccharomyces cerevisiae). Our methodology combines PPI data, gene expression data, protein motif information, mutant phenotype data, and protein localization data, while using Gene Ontology (GO) “biological processes” terms In this paper, our contribution is twofold. First, we propose a simple and relatively transparent probabilistic model for protein function prediction that allows us to efficiently calculate the posterior probability that each gene has a particular function, given various types of genome-wide data. Second, we analyze the effect of combining the heterogeneous data sources in a substantially more comprehensive manner than has been done to date, with the goal of better understanding just which types of genes benefit most from the integration of which types of data sources. In particular, we develop a relatively simple yet useful method to integrate functional linkage graphs with categorical information. The functional linkage graphs are constructed from PPI data and gene expression data. As usual the assumption here is that physically interacting proteins or co-expressed genes are more likely to share protein functions than a randomly selected pair of proteins By combining five types of data, the number of correctly recovered known gene-term associations is increased by 18% at the same precision (50%), compared to using PPI data alone. We specifically focused on certain points on the ROC curve in our analysis that we believe are potentially feasible for follow-ups on the prediction in experimental labs. We show that by adding different types of genome-wide data, different types of the GO terms that are specific for the type of information are newly recovered. Also, by conducting robustness analysis of the integration model to PPI edge removal, we provide a novel perspective on the amount of PPI data necessary to obtain high prediction accuracy by the integration model. In that analysis, we find some conditions where integration actually hurts performance rather than improving accuracy. Plausible functions are assigned to 463 currently unannotated proteins by our method, and we discuss some of these novel assignments.Saccharomyces cerevisiae genes are extracted. We eliminated self-self interactions and duplicated protein interaction pairs from the database to construct a PPI functional linkage graph From the GRID database −10, indicating that for each experiment we are only using a set of declared co-expressed pairs for which a false declaration is exceedingly unlikely. Finally, 38151 non-redundant gene pairs are obtained from the combined gene pairs to construct a co-expression functional linkage graph.Four types of gene expression data, the Rosetta compendium data From the MIPS From the MIPS From the MIPS From the 06/03/2006 version of the Yeast SGD database t and for each functional linkage graph l, we calculate p1l)( is expected to be higher than p0l)( and p0l) are a member of the constructed pairs in each data set. For each GO label Proteins can be associated with categorical features according to different types of categorical information. The categorical features that are used in our predictive methodology are defined below.id is associated with a protein where id = 1 if the protein contains domain id, and id = 0 otherwise. A feature vector d = T is defined for each protein, where dq is the total number of protein motif features (dq = 992 in our case).Protein motif (domain): Random variable ip is associated with a protein where ip = 1 if the gene knockout exhibits phenotype ip, and ip = 0 otherwise. A feature vector p = T is defined for each protein, where pq is the total number of phenotype features (pq = 175 in our case).Phenotype: Random variable il is associated with a protein where il = 1 if the protein localizes in il, and il = 0 otherwise. A feature vector l = T is defined for each protein, where lq is the total number of localization features (lq = 41 in our case).Protein localization: Random variable Naturally, a protein can have several features at the same time. Our aim is to integrate these sources of evidence in a smooth fashion to improve the accuracy and coverage of the functional predictors based on the assumption that if a protein has specific features, then this can increase the probability to infer specific protein functions.i and GO term t, a Boolean random variable i,tL is associated, where i,tL = 1 if i is labeled with t, and i,tL = 0 otherwise. We want to calculate the probability of i,tL = 1 for all combinations of i and t, given the structure of functional linkage graphs constructed above, and the category features that the protein i has, and all the assignments of GO terms to the other proteins. We assume that probability distribution for the labeling i,tL = 1 is conditionally independent of all other nodes given the functional annotation of the neighbors and category information of the protein.For each protein P,…, iNm),…, i,tkm),…, cin), where iN(l) is the number of graph neighbors (excluding unannotated neighbors) of gene i in a functional linkage graph l, ik(l) is the number of the neighbors of gene i which are labeled with term t in the graph l, m is the number of different types of functional linkage graphs, cij) is the feature vector that the gene i has for a category feature type j, and n is the number of different types of categories (not the number of features). For example, in this paper, iN(1) and ik(1) is the number of neighbors of gene i, and the neighbors that have term t in a PPI network, respectively. iN(2) and ik(2) is the number of the neighbors and the neighbors that have term t in a co-expression network, respectively. ci(1), ci(2) and ci(3) are feature vectors that gene i has for the three types of categories, i.e., protein motif feature vector d, mutant phenotype feature vector p, and localization feature vector l, respectively (Section 2.2).We want to calculate the posterior probability given functional linkage graphs and category features of a protein i has function t P,…, iNm),…, i,tkm),…, cin) can be rewritten (with omitting subscript i and t) as:Applying Bayes' theorem, the posterior probability that gene i,tL is independent of the number of graph neighbors N(1),…, Nm), …, Nm) =  P(L). Also, we assume that k(1),…, km),…, cn),…, Nm),…, Nm)( except Nl),…, Nm).Here, we assume that the probability distribution of P(kl)) is the probability that kl)( neighbors are labeled with t out of Nl). Similarly, p0l)(|L) is the probability that a gene i has feature vector cj)( given that the gene i has term t. P(L) is the prior probability that the gene i has term t. This is calculated as P(L) = f, where f is the frequency of term t among genes.P(xcj)(  = 1|L) = (# of t-labeled genes that have a feature xcj)/(# of t-labeled genes), and P(xcj)( = 1|L ¯) = (# of genes that are not labeled with t and have a feature xcj)/(# of genes that are not labeled with t). Since we do not want lack of information to affect protein function prediction, we assume P(xcj)(  = 0| L)/P(xcj)( = 0|L ¯) = 1. For example, suppose that the current genome-wide data do not contain information that a protein is localized in mitochondria. However, this does not necessary mean that the protein does not localize in mitochondria. The reason might be that just lack of the currently available data.Hence the neighborhood function (1) becomes:Saccharomyces cerevisiae. The algorithm fuses probabilities obtained from diverse data sources including PPI, gene expression, protein motif information, gene knock-out phenotype, and protein localization. The functional annotations used to train our models are based on the GO category and are obtained from Yeast SGD database The integration algorithm described in the g and term t, the probability that gene g has term t is calculated based on equation (2), given that we know every other gene-term associations in the training set. It is predicted that gene g has term t if the probability exceeds a specified threshold. A positive g-t association set is obtained from the GO “biological processes” data, and negative g-t association set is defined as follows: If the association is not in the positive set, and g is annotated with at least one biological process t, and t is neither ancestor nor descendant of the known function in the GO hierarchy. First, we attempted to predict known protein-term associations by 5-fold cross validation. For each gene −6, respectively, at the 50% and 80% precision levels.These two levels of precision, i.e., 50% and 80%, were chosen as being reasonably representative of the range of possible improvements observed in our study. In addition to the performance characteristics just described, we also examined the issue of falsely predicted proteins, as a function of the threshold applied to posterior probabilities. Using the method of Next, we analyzed whether prediction accuracy depends upon the functional category to be predicted. It is expected that the prediction performance of specific GO terms depends on what kinds of data sources one uses. Here is an example how the combination of different types of data helps to predict protein function more specifically. Genes YKR055W, YIL118W and YJL128C have a GO term “intracellular signaling cascade”, but neither the PPI data nor the protein motif information alone can predict the GO term for the proteins. When PPI data alone is used, a GO term “signal transduction”, which is a parent of “intracellular signaling cascade” in the GO hierarchy and hence a broader term, can be predicted. However, when both PPI data and protein motif information are used, the GO term can be predicted correctly. In this case, information that the proteins have a protein motif “protein kinases signatures and profile” or “prenyl group binding site” helps to predict more specific term “intracellular signaling cascade” correctly.x-axis shows how much of PPI edges are present, compared to the original PPI network. For example, at x = 50, half of PPI edges are randomly removed from the original PPI network. The error bar shows the standard deviation of 10 independent experiments. At 50% precision, the integration model always wins, regardless of the number of PPI edges present. However, interestingly, at 80% precision, the integration model wins only when more than 50% of PPI edges are present. In other words, in order to obtain high prediction accuracy (80% precision) by the integration model, certain amount of PPI data is necessary . This result suggests that the combination of gene expression, protein motif, mutant phenotype and protein localization data is still a weak indicator of protein function, and hence need to have certain amount of PPI information (strong indicator of protein function) in order to obtain high prediction accuracy (80% precision).In the recall experiment in Section 3.1, we showed that PPI data is the strongest source of evidence for protein function prediction in our model, compared to other data sources. Here, we want to know whether our integration model works well or not when the amount of PPI data is limited. In this experiment, a certain fraction of the PPI edges are randomly removed from the original PPI network, and then protein function is predicted using our integration model. Saccharomyces cerevisiae proteins  are not truly conditionally independent S. cerevisiae, we believe that similar advances are possible for fly, worm, mouse and human where analogous resources are being compiled.Although the result presented here is a case study for yeast Table S1Improved GO terms by adding gene expression data at 50% precision.(0.02 MB XLS)Click here for additional data file.Table S2Improved GO terms by adding protein motif data at 50% precision.(0.02 MB XLS)Click here for additional data file.Table S3Improved GO terms by adding phenotype data at 50% precision.(0.02 MB XLS)Click here for additional data file.Table S4Improved GO terms by adding localization data at 50% precision.(0.02 MB XLS)Click here for additional data file.Table S5Prediction result of unannotated genes. N1 is the number of neighbors in PPI network, k1 is the number of t-labeled (t is the predicted GO term) neighbors in PPI network, N2 is the number of neighbors in co-expression network, and k2 is the number of t-labeled neighbors in co-expression network.(0.23 MB XLS)Click here for additional data file.


Here, we present a classification of domain interfaces according to their geometry. Our new algorithm uses a hybrid approach of both sequential and structural features. The accuracy is evaluated on a hand-curated dataset of 416 interfaces. Our hybrid procedure achieves 83% precision and 95% recall, which improves the earlier sequence-based method by 5% on both terms. We classify virtually all domain interfaces of known structure, which results in nearly 6,000 distinct types of interfaces. In 40% of the cases, the interacting domain families associate in multiple orientations, suggesting that all the possible binding orientations need to be explored for modelling multidomain proteins and protein complexes. In general, hub proteins are shown to use distinct surface regions (multiple faces) for interactions with different partners. Our classification provides a convenient framework to query genuine gene fusion, which conserves binding orientation in both fused and separate forms. The result suggests that the binding orientations are not conserved in at least one-third of the gene fusion cases detected by a conventional sequence similarity search. We show that any evolutionary analysis on interfaces can be skewed by multiple binding orientations and multiple interaction partners. The taxonomic distribution of interface types suggests that ancient interfaces common to the three major kingdoms of life are enriched by symmetric homodimers. The classification results are online at  The behaviour of biological systems is governed by protein interactions. Considerable effort has already been dedicated to characterise individual proteins and their evolution. As a next step, researchers need to understand the characteristics, dynamics, and evolution of complex networks of proteins. While many experimental techniques determine high-throughput protein–protein interactions, only few provide structural insights into the actual interfaces. The authors provide a comprehensive compendium and classification of these structural interfaces. To this end, they design a fast and accurate algorithm, which they apply to all known structural interactions. As a result, they shed light on the geometry and the evolution of protein interfaces. Their analysis reveals that 40% of protein interactions between homologues associate in multiple orientations. This has, in particular, implications for gene fusion events detected by conventional sequence homology: for one-third of these genes, the fused and nonfused proteins associate in alternative binding orientations. The classification also shows that any evolutionary analysis, such as interface conservation, can be skewed by multiple binding orientations and interaction partners. Hub proteins, which are highly connected to many other proteins in interaction networks, are shown to use distinct surfaces, or faces, for different partners. Interestingly, some proteins develop many different faces for the same partner , and others use the same face for evolutionary unrelated partners . Finally, the authors show that ancient interfaces, which appear in all three kingdoms of life, are dominated by symmetric homodimers, reflecting the direction of evolution from symmetric to asymmetric or heteromeric. Protein tertiary and quaternary structures often provide a deep insight into a protein's function and its underlying mechanism. Though the number of available structures is growing rapidly, including multidomain proteins and protein complexes, solving large protein structures is still challenging. If the structures of the component domains and subunits are known, systematic docking  or multiNumerous studies have focused on the characterisation of interfaces using physicochemical properties, shape, packing density, and binding energy –16. The The potential utility of the representative interface types is diverse. For example, the classification provides a convenient framework for screening common interface motifs shared among homologous and even unrelated folds ,25. For Our work elaborates and improves the previous work by Kim and colleagues  in severhttp://www.scoppi.org with a convenient query environment.Our classification method is primarily based on structural alignments, while the previous work depends on sequence alignments. The classification accuracy significantly improved from 78% recall and 90% precision in ) was also excluded because the binding partners can be any foreign proteins. The unclassified families were less than 0.5% of the total families; thus, the influence on the result was expected be marginal.Both face angle and face overlap were computed by Python scripts using PyMOL  and a reference (R) classification on a set of interfaces. The different interface types are denoted as iR and j,T respectively. The number of domain pairs belonging to each interface type is given as | iR | and | jT |, respectively. Then, each reference classification (iR) was mapped to one of the test classification (jT), which maximizes | iR ∩ j |T, the number of common domain pairs between the two classifications.iR to jT (i → j) was done to maximize recall, precision was calculated as follows:The accuracy was measured by comparing a test  shows the most face diversity of 51 face types. As the number of domains in a protein increases, the distribution shifts towards a higher face average, since its domain faces add up. The average number of faces for a 1, 2, 3, 4 domain protein is 3.2, 6.4, 9.6, 12.8, respectively.(30 KB PDF)Click here for additional data file.Figure S2(29 KB PDF)Click here for additional data file.Table S1The benchmark dataset contains a hand-curated set of interaction interfaces, which are particularly difficult to classify.(19 KB TXT)Click here for additional data file.Table S2Genuine gene fusion cases, in which the binding orientation of the fused and nonfused domains is preserved.(30 KB TXT)Click here for additional data file.Table S3Nongenuine gene fusion cases, in which the binding orientation of the fused and nonfused domains is not preserved.(41 KB TXT)Click here for additional data file.http://scop.mrc-lmb.cam.ac.uk) accession numbers for the domain families mentioned in this paper are 14-3-3 protein (a.118.7.1), AAA–ATPase domain (c.37.1.20), actin/HSP70 (c.55.1.1), breast cancer associated protein, BRCA1 (c.15.1.3), calmodulin-like (a.39.1.5), protein kinases, catalytic subunit (d.144.1.7), CO dehydrogenase ISP C-domain–like (a.56.1.1), collagen-like peptides (k.3.1.1), DNA polymerase III clamp loader subunits, C-terminal domain (a.80.1.1), ferritin (a.25.1.1), DNase I-like domain (d.151.1.1), fibronectin type III (b.1.2.1), G proteins (c.37.1.8), long-chain cytokines (a.26.1.1), molybdenum cofactorbinding domain (d.133.1.1), nuclear receptor (g.39.1.2), nuclear receptor ligand-binding domain (a.123.1.1), p53 DNA-binding domain-like (b.2.5.2), protein serine/threonine phosphatase (d.159.1.3), PUA domain (b.122.1.1), ricin B-like domains (b.42.2.1), RING finger domain, C3HC4 (g.44.1.1), V set domains (antibody variable domain-like) (b.1.1.1).The SCOP (http://www.pdb.org) accession numbers for the structures mentioned in this paper are α-amylase from Bacillus licheniformis (1bli), aldehyde oxidoreductase of Desulfovibrio desulfuricans (1dgj), Allium sativum lectin (1kj1), CO dehydrogenase of Hydrogenophaga pseudoflava (1ffu), DNA clamp loader complex (1sxj), fused domain pair of Scilla campanulata agglutinin (1dlp), guanine nucleotide exchange on Ran by the regulator of chromosome condensation (RCC1) (1i2m), haemagglutinin component (HA1) of the progenitor toxin from Clostridium botulinum (1qxm), Haemophilus ducreyi cytolethal distending toxin (1sr4), ClpB protein (1qvr), molybdenum cofactor-binding domain in CO dehydrogenase from Oligotropha carboxidovorans (1n60), Ran-binding domain complexed with Ran bound to a GTP analogue (1rrp).The Protein Data Bank (http://www.expasy.ch/enzyme) for the enzymes mentioned in this paper are carbon monoxide dehydrogenase (1.2.99.2) and carboxylate reductase (1.2.99.6).The Enzyme Classification numbers (