The application of high throughput approaches to the identification of protein interactions has offered for the first time a glimpse of the global interactome of some model organisms. Until now, however, such genome-wide approaches have not been applied to the human proteome.In order to fill this gap we have assembled an inferred human protein interaction network where interactions discovered in model organisms are mapped onto the corresponding human orthologs. In addition to a stringent assignment to orthology classes based on the InParanoid algorithm, we have implemented a string matching algorithm to filter out orthology assignments of proteins whose global domain organization is not conserved. Finally, we have assessed the accuracy of our own, and related, inferred networks by benchmarking them against i) an assembled experimental interactome, ii) a network derived by mining of the scientific literature and iii) by measuring the enrichment of interacting protein pairs sharing common Gene Ontology annotation.The resulting networks are named HomoMINT and HomoMINT_filtered, the latter being based on the orthology table filtered by the domain architecture matching algorithm. They contains 9749 and 5203 interactions respectively and can be analyzed and viewed in the context of the experimentally verified interactions between human proteins stored in the MINT database. HomoMINT is constantly updated to take into account the growing information in the MINT database. The dynamic assembly of stable or transient protein complexes regulates cell physiology by presiding over basic cell functions. In principle, if we knew the kinetic details of the interaction between any macromolecule in a cell, as well as the concentration of each player, we could start thinking about modeling a virtual cell in order to understand, or infer, its response to any given stimulus.H. pylori[E. coli[S. cerevisiae [C. elegans [D. melanogaster[Regrettably we are very far from this level of understanding of the interactions within a cell proteome. In recent years, however, high throughput approaches based on the yeast two hybrid  and TAP H. pylori, E. colii[E. coli, S. cererevisiae -8, C. el elegans  and D. manogaster,11. Companogaster. Neverthanogaster have useanogaster to inferanogaster have devanogaster. Thus th.Our strategy starts by assigning proteins to orthology groups having a human protein as the main ortholog. An interaction between human proteins is then inferred if both partners of an interaction experimentally verified in model organisms have at least one human ortholog.Similarly to Lehner and Fraser , we haveIn addition we have tuned the orthology assignments by imposing the condition that proteins in the same orthology group must have the same domain architecture. This filtering step evaluates the overall protein similarity and eliminates any incongruity caused by the local nature of the BLAST algorithm. Motivated by the observation that multidomain proteins, sharing an exact domain architecture, have significantly higher functional conservation ,18, we dThe inferred HomoMINT network has been incorporated into the MINT database . In esseIn the latter case one obtains, as a result of the query, both the experimentally verified interactions and the inferred ones. Appropriate links make it possible to retrieve information about the experiments supporting the interaction either directly (experiments carried out with human proteins) or indirectly (experiments carried out in model organisms) Fig. .During any MINT search session it is possible to extend the analysis to HomoMINT, by clicking the HomoMINT hyperlink. The composition of the orthology groups used to infer the human interactions can also be inspected via the 'orthology table' hyperlink. A distinction is made between main orthologs (orthologs) and co-orthologs .Finally the HomoMINT network can be analyzed, expanded, edited in the context of the experimentally verified protein interactions in the MINT database by using the MINT viewer tool Fig. . For insSeveral low throughput experiments, providing evidence of protein interactions between human proteins, have been published in the scientific literature over the past decades. This dataset is approximately the same size as the datasets obtained from the results of high throughput experiments carried out in model organisms, although it is not readily accessible. Recently, a number of databases have started to capture this information and release it in a computer readable format according to a common standard . By mergThis assembled human experimental network (HEN) is likely to have some bias in the coverage of the interaction space due to the interest of the scientific community in investigating specific biological domains or to a biased selection of the journal articles curated by the databases. Nevertheless it represents the most accurate representation of the human interactome to date. We used HEN as a benchmark for the initial assessment of the accuracy and the information content of HomoMINT and related inferred networks Table . The netwhere P(I|D) and P(~I|D) are the frequencies of interactions, in a given dataset (D), that are or are not observed in the benchmark dataset (I), while P(I) and P(~I) represent the prior expectations .The overlap between the human experimental network and the one inferred from model organisms (HomoMINT) is 694 interactions Table . This co).The PubMed resource, containing more than 15 million biomedical abstracts, is a valuable resource for high quality protein interactions. As a whole, concurring proteins in PubMed sentences can be considered and modeled as a literature network, which can be superimposed on experimental interaction data or on putative relationships, making it possible to compare new and existing knowledge possible. Here we have made use of a novel text-mining resource, called iHOP (Information Hyperlinked over Proteins)  as an inTable For this comparisons we mapped all the proteins to Locus Link ids. In this process proteins (and their interactions) that could not be confidently mapped were eliminated from the networks. For this reason, H_MINT in Table The extent of shared annotation in a protein interaction dataset has been previously shown to correlate with accuracy ,26. ThusProtein interaction networks can be described as graphs where nodes and edges represent proteins and their interactions respectively. Although, at a first sight, apparently random in their topology, biological networks are characterized by a number of properties differentiating them from random networks. Specifically they have a large average clustering coefficient . Most reIn Table Several databases, using a variety of computational methods to make inferences about functional relationships between genes and proteins, are available on the web -35. HomoApproximately one fifth of the interactions present in the MINT database could be mapped to human orthologs thus resulting in the assembly of an inferred network linking 4125 human proteins with 9749 edges. While a large proportion of these proteins are not functionally annotated one can use HomoMINT to transfer functional information from better characterized neighbors in the graph.Because of evolutionarily frequent molecular processes leading to gene family expansion or contraction, the transfer of interaction information between organisms, especially high eukaryotes, is complicated by the abundance of paralogs in orthology groups. The InParanoid algorithm is designed to distinguish paralogs arising before or after speciation events. We have chosen to transfer the interaction information only to the main human ortholog in each group. Thus our inferred network is essentially based on orthology mapping by the reciprocal best hit approach. However, the orthology groups assembled in our web available table contain paralogs, so permitting any alternative choice. Furthermore since the InParanoid algorithm provides a confidence score for each orthology assignment the likelihood of the inferred interactions can be evaluated from the confidence score of the model organism and human gene orthology assignment as proposed for instance by Lehner and Fraser .To assess the predictive value of HomoMINT, we performed a number of tests aimed at assessing to what degree of accuracy and coverage the orthology based inferred networks could be supported by previous knowledge. We first assembled a human experimental network from the protein interaction data stored in PPI databases and determined the percentage overlap between this network and HomoMINT or related networks. Next, we estimated the enrichment in the inferred networks of interacting proteins sharing Gene Ontology annotation. Finally we estimated the overlap between the inferred networks and the iHOP literature network.Our approach is based on the assumption that protein interactions between ortholog proteins are conserved in evolution. To what extent this is true cannot at present be estimated because of the incompleteness and inaccuracy of the available experimental datasets . Even hy. The HomoMINT dataset is available either as a flat file or a PSI XML file , Caenorhabditis elegans (predicted proteins), Drosophila melanogaster (predicted proteins), Escherichia coli K12 (predicted proteins), Homo sapiens (predicted proteins), Mus musculus (predicted proteins), Rattus norvegicus (predicted proteins), Saccharomyces cerevisiae (predicted proteins),  Multiple species proteome set (predicted proteins),  by querying the database for proteins belonging to the following species: Sus scrofa (Pig), Xenopus laevis (African clawed frog), Ovis aries (Sheep), Oryctolagus cuniculus (Rabbit),Gallus gallus (Chicken), Canis familiaris (Dog),Bos taurus (Bovine).The proteome sets for the BLAST searches and ortholog table assembling were downloaded or built from the following sources: The procedure implemented in the InParanoid algorithm  starts wAn InParanoid confidence level cut-off of 0.6 was chosen for the assignment of in-paralogs to orthology groups. Due to the redundancy of the starting proteome sets, several groups contained identical copies of the same protein. To limit this problem we decided to eliminate paralogs with InParanoid confidence level above 0.98. InParanoid performs its comparison between each pair of proteomes. To build an orthology table with orthology groups including proteins from all organisms of interest, we used python scripts to merge the InParanoid results keeping a human protein as reference for each orthology group. DIP (Flat file),833 unique interactions at  BIND (XML PSI 2 file),4073 unique interactions at  MINT, 3679 unique interactions at  HPRD (XML PSI file), 6153 unique interactions at  MIPS (XML PSI file), 322 unique interactions at  Only interactions that could be confidently mapped to Uniprot ids were added to HEN.The human experimental interactome has been assembled by importing the data in a Postgresql database from the following resources: Intact (XML PSI files),1300 unique interactions at A procedure has been developed to improve and to measure the functional coherence in orthology groups, based on dynamic programming techniques and implemented as a string matching algorithm .We modeled every protein in our orthology groups as an ordered string of domains. To this end, we used the domain annotations available in SMART  and PFAMThen we developed a PERL string matching algorithm to establish distances between the proteins in terms of similarities between their domain architectures. Each protein is represented as a string of concatenated ordered domains. Thus we were able to measure a distance between two proteins by counting the number of domain editing steps  in order to match the domain architecture of the two proteins. Proteins identical in their domain architecture will have an "edit distance" equal to zero. Distances are normalized by dividing for the total number of domains in the ortholog human protein.This procedure prevents proteins with markedly different domain architecture (and function) from being clustered mistakenly in a group, although they share similarities only within distinct regions of a multidomain protein. In this way we tried to take in account not only local relationships among sequences to be merged in the orthology groups but global relationships as well.To assess the filtering procedure we examined the consistency of the annotation of the members within each orthology group, as reported in the ENZYME database . We wereThe algorithm for measuring the Gene Ontology annotation similarity of a pair of proteins is based on the simLL function of the GOstats package of Bioconductor . For eac(1) Finds all the terms to which Pi and Pj are annotated including the parent terms. These sets of terms in the Gene Ontology tree represent the nodes of the GO graphs induced by Pi and Pj, respectively.(2) Find the set of terms which the GO graphs induced by Pi and Pj have in common. Denote this set Sij.(3) Define the depth of each term in Sij to be the length of the shortest path between the term and the root node of the ontology (here length refers to number of connecting edges).(4) Find the maximum depth of terms in the set Sij. We refer to this value as Dij.MP carried out programming and analysis on ortholog-paralog detection, Gene Ontology annotation, domain architecture matching algorithm and participated in graph theory based network analysis as a Ph.D.student in the lab of GC; she designed the organization and drafted the manuscript.AC performed the integration of HomoMINT in the enviroment of MINT database; he also assembled the human experimental network and helped to draft the manuscript.CG and AF developed the software for modeling and generation of random networks, performed network comparison analysis and helped to draft the manuscript. RH was involved in the benchmarking of HomoMINT against iHOP resource. GC supervised the study and wrote the manuscript. All authors read and approved the final manuscript.In the homomint-flat file (dump-homomint.txt), the fields are: • the mint id of the interaction it has been infered from • uniprot id of the first human protein • short label for the first protein • uniprot id of the first protein of inference (model organism) • uniprot id of the second human protein • short label for the second protein • uniprot id of the second protein of inference (model organism) • model organism from which the interaction on Human has been inferedClick here for fileIn the XML file (homomint-2005-04-30.xml) : • each ProteinParticipant has for first xref the uniprot reference of the human protein and as a secondary xref the ortholog in the model organism (secondary="inferred from") • the xref at the interaction level contains the mint id for the interaction from which ithas been infered • the attribute list on the interaction level contains an attribute "model organism" that contains the organism from which this interaction has been inferred.Click here for file


The functional annotation of proteins relies on published information concerning their close and remote homologues in sequence databases. Evidence for remote sequence similarity can be further strengthened by a similar biological background of the query sequence and identified database sequences. However, few tools exist so far, that provide a means to include functional information in sequence database searches.We present ProFAT, a web-based tool for the functional annotation of protein sequences based on remote sequence similarity. ProFAT combines sensitive sequence database search methods and a fold recognition algorithm with a simple text-mining approach. ProFAT extracts identified hits based on their biological background by keyword-mining of annotations, features and most importantly, literature associated with a sequence entry. A user-provided keyword list enables the user to specifically search for weak, but biologically relevant homologues of an input query. The ProFAT server has been evaluated using the complete set of proteins from three different domain families, including their weak relatives and could correctly identify between 90% and 100% of all domain family members studied in this context. ProFAT has furthermore been applied to a variety of proteins from different cellular contexts and we provide evidence on how ProFAT can help in functional prediction of proteins based on remotely conserved proteins.By employing sensitive database search programs as well as exploiting the functional information associated with database sequences, ProFAT can detect remote, but biologically relevant relationships between proteins and will assist researchers in the prediction of protein function based on remote homologies. Functional prediction of experimentally uncharacterized proteins is an important research area in bioinformatics. On the one hand, the functional prediction of a protein can help in advancing biological science by generating testable hypothesis for experimental research. On the other hand, it improves the annotation of sequenced genomes by assigning functional information to predicted genes. Functional prediction relies mostly on the similarity between sequences and standard sequence similarity search tools have been successfully applied in protein functional annotation, provided that the similarity between related proteins is significant enough for sequence-based detection. If, however, the similarity between related protein sequences is low, profile-based database search methods like PSI-BLAST or HMMer, as well as fold recognition tools have proven successful in detecting remote homologies and therefore can assist in predicting the function of uncharacterized proteins ,2).Experimentally characterized proteins have extensive functional information associated with their sequence records. This functional information includes published literature about a protein or gene, functional classifications as for instance provided by the Gene Ontology (GO-) annotations, conserved domains that potentially link a protein with a molecular function, and sometimes even a short summary about the proteins' function. Given detectable sequence similarity between a functionally characterized and an uncharacterized protein, this information can be used to predict the putative function of the unknown protein. Given the complexity of the output of sequence-, as well as structure-based search techniques, the exploitation of this functional knowledge is often tedious and involves extensive manual mining for the biological context of identified database sequences.With the advancement of experimental techniques in molecular biology, for example biochemical screens (co-immuno-precipitation experiments) and functional screens, so far uncharacterized proteins can often be put into the context of a cellular process while their molecular function may not become obvious by standard sequence similarity search tools. The combination of sensitive similarity search techniques with functional annotation of identified hits has already been successfully applied for the functional prediction of proteins, where the biological context of the protein of interest could be assigned based on the existing experimental information of remote homologues ,4. SimilWith ProFAT, we introduce a tool that combines a remote sequence similarity search tool  with foAnnotation and Threading Engine, as well as the HMMerThread pipeline, which is discussed below.ProFAT combines sensitive sequence similarity search tools with text-mining for post-filtering of identified hits according to a biological process defined in the user-provided keyword list. Individual domains of a query are submitted to the search engine, rather than the full-length sequence. To achieve this, ProFAT uses standard domain searches for detection of conserved domains, whose borders are then used to split the sequence into individual regions. The resulting regions can then be selected by the user and can be submitted in parallel to a profile-based sequence similarity search tool, as well as a fold recognition tool. Associated information concerning identified hits from both pipelines is text-mined to determine the occurrence of keywords of the user-provided keyword list. Keyword-positive hits have been experimentally associated with the biological process described in the keyword list and are then selectively shown to the user. The workflow of the ProFAT server is shown in Figure Domain searches are carried out using RPS-BLAST  against Annotation Engine for the HNF-1α, PABP and PLAT domain families were run using no stemming. Only those family members that identified at least 10 hits with a keyword from the respective keyword lists were scored as positive. HMMerThread bulk searches were run using a threading extension of various sizes  for HNF-1α, 5 and 8 for PABP and 0 for PLAT family members and a threading hit depth of 20.Bulk searches of the Multiple sequence alignments were done with ClustalX  and wereThe input of a ProFAT analysis is a protein sequence and a keyword list that describes the cellular process or putative function relevant for the protein under analysis.domain search or domain prediction, whereby identified conserved domains are used to split the input query for further processing with 2) the Annotation Engine and 3) the Threading Engine.The workflow of ProFAT can be divided into 3 parts Figure : a domaiNP_036228]. In this case, ProFAT identified a central PH-domain and a C-terminal PTB domain in the input query. If the domain search fails to identify conserved domains, the user can perform a domain prediction. In this case, RPS-BLAST is run using relaxed settings (E <= 100). Identified weak domain hits are subsequently submitted to a BLAST-search, whereby the resulting hits are mined for their biological relevance using the user-provided keyword list. Figure Initial domain searches (using RPS-BLAST against the CD-database (NCBI)) are carried out using a restrictive E-value cutoff (E <= 1E-04) by default. Identified conserved domains can be selected in the results page for further processing. Figure Annotation Engine, the Threading Engine, as well as to an HMMerThread run for a keyword-independent domain prediction.At this stage, selected domains can be in parallel submitted to the Annotation Engine sends the selected conserved domains and/or regions of the input query to a PSI-BLAST search against the non-redundant database. Associated information including GO-annotation, GenBank features, as well as associated publication abstracts of identified hits are subsequently text-mined for the occurrence of user-provided keywords. Figure Annotation Engine, where full-length Dip13α/APPL1 was used as the query sequence with a keyword list tailored for 'Endocytosis' ) and from the APC-binding protein EB1  was recently added to the PDB-database. We compared the structure of the Hook1 CH domain to the CH domain of EB1 using the DALI-server, which gave a Z-score of 8. When 1WIX was used to search the structure database for structural neighbors, it identified the CH domain of Calponin alpha as the first hit with a Z-score of 9.1. The N-terminal domain of Hook1 can therefore be considered to be significantly similar in fold to CH domains. ProFAT was therefore able to correctly identify the N-terminal domain of Hook3 as a CH domain.Hook3 is a member of the Hook family of proteins involved in intracellular trafficking and associates with its N-terminus to the cytoskeleton . We wereely ; for acely . We theSus scorfa was recently linked to an autosomal recessive disease in pigs that leads to immotile short-tail sperm ). Using the DALI server, we searched for similar structures to 1WHV. The closest hit is the structure of the central RRM of human La protein -specific ribonuclease PARN is a 3' exonuclease which is involved in the destruction of cellular mRNAs . Membersmain see . We selecken see . HMMerTh 90% see . The cryB:1S79], ), which HMMerThread and submitted the protein sequence of LOC84060 to ProFAT's Annotation and Threading Engine. For more accurate results, we invoked the option of splitting the input sequence using 150 amino acids. ProFAT's Annotation Engine identified among others the RRM domain in the poly (A)-binding protein PABPC from human [see HMMerThread found the RRM_1 domain of splicing factor U2AF as significantly similar [see The uncharacterized human protein LOC84060 has not been associated with any biological function. Domain searches using standard parameters did not reveal any conserved domains for this protein. However, when increasing the E-value in the RPS-BLAST search, an RRM domain is found with an E-value of 4.6. Assuming that this protein would be involved in RNA metabolism or regulation, we submitted the protein sequence of LOC84060 to the ProFAT server. The domain search pipeline of ProFAT did not find any conserved domain, while HMMer identified the presence of an RRM_1 domain in this protein see . We seleuman see . HMMerThilar see . Next weHMMerThread search with the protein sequence of LOC79969. HMMer detected a weakly conserved acetyltransferase domain within the DUF738 region [see HMMerThread, which identified the 3-dimensional structures of several acetyltransferases with a confidence of nearly 90% [see C. elegans. A conserved Tyrosine is however located 4 residues C-terminal to the proposed catalytic site. As our data suggest that LOC79969 adopts a GNAT-like fold, it will have to be tested experimentally, whether the Acetyltransf_1 domain is catalytically active.No functional information is so far available for the uncharacterized human protein LOC79969. Domain searches using RPS-BLAST or SMART predict the presence of a domain of unknown function, DUF738. As there was no hint on the biological context this protein could be associated with, we performed only an gion see . We sele 90% see . We next 90% see ) is mutaHMMerThread presents itself as a very powerful pipeline for accurate prediction of weakly conserved domains by looking for remote sequence similarity with conserved domain hits in combination with a subsequent threading step. HMMerThread in addition has the advantage of not relying on the user-provided keyword list and can be applied to proteins, which cannot be associated with any biological function. This module can therefore be used as a means of predicting weakly conserved domains with high accuracy.ProFAT finds its utility in several applications: 1) the ProFAT server should be used when standard similarity search programs fail to predict the function of a so-far uncharacterized protein that can be associated with a certain cellular process/molecular function. In this case, ProFAT would be used as an aid for post-filtering of complex Threading and PSI-BLAST outputs; 2) the user might be interested in whether a conserved domain shows remote sequence similarity or is structurally related to proteins from a specific cellular process/molecular function and can therefore use ProFAT to specifically search for weakly related sequences or structures that are found in the biological context of interest; 3) the domain prediction pipeline is applicable to regions of proteins with no obvious conserved domain. In this case, the combination of RPS-BLAST and a subsequent BLAST-search of weak domain hits with a text-mining step can strengthen evidence from subtle sequence similarity with additional biologically relevant evidence; 4) finally, ProFAT is a powerful tool for the uncovering of remote but biologically relevant relationships between sequences. While highly powerful tools are already available to discover subtle sequence similarity, for example profile-based database search methods and fold recognition techniques, only few methods so far exist that also provide a means to combine these search tools with a literature-mining step. In particular the text-mining of associated literature abstracts makes ProFAT unique in post-filtering database sequences based on biological features found in associated primary literature. While tools such as OntoBLAST and SAWTED use secondary annotation of sequences for post-filtering of database search results, ProFAT goes back to primary published information of sequence entries, which helps to circumvent the problem of sometimes error-prone functional information found in the annotation of sequences. The strength of ProFAT furthermore lies in the combination of sequence- and structure-based search tools that are able to reliably detect weak sequence relationships. Finally, ProFAT is highly flexible and allows the user to tailor a database search to his own biological interest.Program name: ProFAT• Project home page: • Operating Systems: platform independent• Programming language: Perl• other requirements: Web-browser, valid e-mail address• License: GNU public license• Any restrictions to use by non-academics: Commercial users are not able to use HMMerThread or the Threading Engine due to license restrictions for Threader 3.5• CRB is responsible for the scripting and implementation of most parts of the ProFAT web-server. He also designed and executed the high-throughput ProFAT pipeline for large-scale searches and applied the ProFAT server to the functional annotation of proteins mentioned in this manuscript.VS has helped with the design of the ProFAT web-server. He also has scripted the tool for the mapping of hits on GO-terms in the Gene Ontology tree and has helped with statistical evaluation of significant hits from high-throughput ProFAT searches.BH conceived of and supervised this study and helped with the design and testing of the ProFAT web-server. She participated in the alignment of the remotely conserved domains mentioned here, analyzed results from ProFAT's large-scale results and was drafting this manuscript.All authors read and approved the final manuscript.Table containing all keyword lists used within this study.Click here for fileAnnotation Engine and HMMerThread for all members of the HNF-1α, PABP and PLAT superfamilies.Detailed results of bulk searches using the Click here for fileOriginal ProFAT results for human protein Hook3, which was predicted as related to the CH domain family.Click here for fileProFAT results for KPL2, which was predicted as related to the CH domain family.Click here for fileOriginal ProFAT results for EPS8L3 protein, resulting in the detection of a SAM domain in the C-terminus of the protein.Click here for fileOriginal ProFAT results for human protein PARN which has a weakly conserved RRM domain.Click here for fileProFAT result for human protein LOC84060, which is predicted to have a weakly conserved RRM domain.Click here for fileOriginal ProFAT results for the human protein LOC79969, which has a predicted acetyltransferase domain.Click here for fileFigure legends for Additional files Click here for fileTable containing all accession numbers used for multiple sequence alignments in Figure Click here for file


Ulysses, a new software for the parallel analysis and display of protein interactions detected in various species, is described. .We developed Ulysses as a user-oriented system that uses a process called Interolog Analysis for the parallel analysis and display of protein interactions detected in various species. Ulysses was designed to perform such Interolog Analysis by the projection of model organism interaction data onto homologous human proteins, and thus serves as an accelerator for the analysis of uncharacterized human proteins. The relevance of projections was assessed and validated against published reference collections. All source code is freely available, and the Ulysses system can be accessed via a web interface  The catalogue of human protein-encoding genes is largely enumerated , but theThe increasing body of genomics data allows functions to be predicted using 'Guilt by Association' (GBA) methods. In GBA, the function of a gene is inferred from the functions of genes with which it interacts  or parallels . Observation of mutually consistent interactions in multiple species improves the predictive performance of GBA methods, a process named Interolog Analysis ,3. EarlyPrediction of human gene function based on Interolog Analysis requires an underlying set of bioinformatics resources and algorithms to make unified data accessible to the community. First, functional genomics data must be accessible through reference databases. Second, the relationships between homologous genes must be mapped by a suitable comparison procedure. Finally, the relationships must be rendered accessible to the broad community through an intuitive interface. A system incorporating these three components would be a powerful tool for laboratory investigators seeking to capitalize on existing genomics data.Saccharomyces cerevisiae [Drosophila melanogaster [Caenorhabditis elegans [Despite substantial success in sequencing genomes, large-scale functional studies have been reported for only a few common model organisms. Key reports have addressed protein-protein interactions in revisiae -6, Drosonogaster -9, and C elegans . In addi elegans , genetic elegans , and sub elegans . The div elegans -16. Larg elegans , DIP [18 elegans .To manage the combination of interaction data and genome annotation, data warehouses have emerged such as EnsMart , SeqHounIn order to draw conclusions about human genes from model organism data, it is essential to possess a map enumerating gene homology relationships among species. The fundamental assumption is that direct gene orthologs (genes separated only by speciation) typically occupy the same functional niche . LeadingBiological interpretation of integrated data is greatly aided by tools for visualization of properties. Multiple platforms for the visualization and manipulation of protein interaction networks -32 proviWe report the construction and assessment of a novel Interolog system for the exploration of human genes based on gene-gene interactions in yeast, fly, and worm Figure . The sysbona fide protein-protein interactions is problematic to evaluating the performance of predictive methods. Previous studies have assessed the quality of interaction data on the basis of protein interactor pairs sharing the same annotated GO-terms [The available pool of curated annotations of protein-protein interactions in reference databases is sparse, only a small subset of the interactome  is present. The Human Protein Reference Database (HPRD)  is the lGO-terms ,35,40,41GO-terms . While sTo gain a broader assessment of the relevance of mapping interactions from model organism proteins onto corresponding homologous human proteins, we elected to apply a compartment-based assessment of the Interolog Analysis. As protein interactions preferentially occur between proteins residing in the same sub-cellular compartment ,43, inteS. cerevisiae, D. melanogaster, and C. elegans proteins reported as interactors in the BIND database. For each model organism interactor mapping to the same HomoloGene as an HPRD human protein, the sub-cellular compartment (as defined by HPRD) was noted  to HomoloGene identifiers . Each HomoloGene interactor was assigned to one or more cell compartment(s) based on the curated HPRD annotations Table . As a cod Figure . For 28,To identify predictions of greater specificity, we determined the co-localization rates for proteins for which 'double linkage' interactions were observed, where 'double linkage' refers to interactions supported either by two different experimental methods for a single organism or in data from two different species Table . As for Because a curated reference collection of non-interacting human proteins is lacking and because pairs of proteins residing in different sub-cellular compartments are less likely to interact , we asseKEGG  and PINdYEATS4 gene, a poorly characterized gene known as glioma-amplified sequence 41, was linked to DMAP1, a DNA methyltransferase-associated protein. The DGCR14 gene from the DiGeorge Syndrome critical region was found to interact with VDP, a vesicle docking protein linked to the golgi. Table Based on the ability of the Ulysses system to identify candidates for inclusion in known networks, we sought to uncover interconnected networks within which each member is connected to at least two other members. Extracting all pairs of HomoloGene proteins supported by two or more datasets, for which there was at least one human homolog for each interactor, we were able to identify 127 distinct HomoloGenes involved in 82 interactions. Amongst these observed high confidence pairwise interactions  components , four U2 and U3 snRNP-specific proteins , a splicing factor (PRPF19), as well as a protein usually associated with the PRPF19 complex (CRNKL1) known to interact with the spliceosome .Two clusters were observed composed of proteins required for DNA replication and repair, as well as replication-dependent structural proteins. One cluster contained all five subunits  of an accessory factor for DNA replication, replication factor C (RF-C). The other cluster contained four nucleosomal proteins, three members of the H2A histone family , which were all connected to the nucleosome assembly protein 1-like 1 (NAP1L1).We also identified a network of 19 interconnected proteasome subunits. We found five core alpha  and four core beta subunits  from the 20S proteasome, as well as nine subunits from the 19S regulatory complex. We located the proteasome regulatory particle subunit PSMD6 interacting with PSMD3, a non-ATPase subunit of the 19S regulatory complex.These examples of functional networks among protein members of well conserved cellular complexes and pathways validate our approach to detect biologically meaningful protein interactions in human by overlaying and projecting interaction data originating from diverse model organisms.To date, the limiting factor for network discovery is the sparse protein interaction data. As more association data are generated for the core model organisms, the Ulysses Interolog analysis system will facilitate greater inference of network members.To bring the power of multi-organism network analysis to laboratory researchers, a web-based interface to the Ulysses system was implemented  Figure . A user Here we described an exploratory Interolog Analysis framework for the inference of protein function. We demonstrate, by overlaying protein interaction data sets, dramatic improvements in the specificity of projected 'dual-linkage' interactions compared to those based on a single study. Through a novel interface, we provide a means for the broad community of researchers to use Interolog Analysis for the directed study of specific pathways or processes.Ulysses represents a significant advance in the graphical display of protein interaction data for comparative genomics. Visualization tools for the study of protein and genetic networks have been available for many years, including Cytoscape , Osprey A limiting factor for inference of new protein clusters and extension of known clusters is the sparse existing coverage of interactions in genomics data. Even though proteome-scale analyses have been conducted for several organisms ,7,10, thThe homology mapping obtained from HomoloGene was convenient for the Ulysses system. Because homology mapping across organisms remains an issue of debate, however, future releases of Ulysses will offer an option to choose between different resources, possibly including well established systems ,26,27.de novo discovery of cellular networks, detection of known complexes and pathways demonstrate Ulysses' capacity to successfully identify biological networks. Ulysses is available without restriction as an internet-based resource or as downloadable code for developers [Even though the small size of the present body of functional genomics data does not allow for extended velopers . The novAll data were stored within the Atlas database system ,50. The Protein interaction data were obtained from BIND   of the query sequence. At the time of publication, homology mappings through HomoloGene were updated as of September 2005.For proteins from each of the three included model organisms , unique GenBank protein geninfo (gi) numbers were extracted from BIND. These identifiers were mapped to corresponding identifiers in the RefSeq collection and the RefSeq IDs were used to select homology sets in HomoloGene. For BIND sequences without a mapping to a RefSeq sequence, BLAST analysis was performed against a database of all RefSeq sequences represented in the HomoloGene system. Parameters were set to an e-value cutoff of 10The HPRD is a collection of hand-curated reports on human proteins extracted from the scientific literature . The HPRA total of 32,930 binary and protein complex interactions were obtained from BIND for which both interactors had been successfully mapped to HomoloGene homology groups. These interactions constitute the observed data and were assessed relative to the HPRD reference set.We generated 60,000 random pairings of all interactors (proteins) present in HPRD bearing a localization label. After eliminating redundancy, we used this set to determine the sub-cellular co-localization. Statistical significance was evaluated using the Fisher exact test.The Ulysses visualization system dynamically generates images for display in a web browser. The visualization problem was divided into two tasks: graph network layout and image rendering. The open source JUNG  Framework  was usedThe following additional data are available with the online version of this paper. Additional data file S. cerevisiae (Sc), C. elegans (Ce), and D. melanogaster (Dm). These candidate connections included genes linked to core biological functions. New candidate members were identified for protein degradation processes, including ubiquitin-dependent protein catabolism and protein degradation via the proteasome. Additionally, candidates were linked by interactions to the ribosome, maintenance, and nuclear export. New candidate components were linked to the highly conserved RNA polymerase complex. Finally, novel associations were predicted with membrane and vesicle-based targeting proteins. These examples illustrate the capacity of the Ulysses system to reveal starting points for the study of new complexes or to orient the exploration of new candidate members of known functional unitsSpecies include Click here for fileDouble linkage criteria (see Table Click here for file


Identification of protein interacting sites is an important task in computational molecular biology. As more and more protein sequences are deposited without available structural information, it is strongly desirable to predict protein binding regions by their sequences alone. This paper presents a pattern mining approach to tackle this problem. It is observed that a functional region of protein structures usually consists of several peptide segments linked with large wildcard regions. Thus, the proposed mining technology considers large irregular gaps when growing patterns, in order to find the residues that are simultaneously conserved but largely separated on the sequences. A derived pattern is called a cluster-like pattern since the discovered conserved residues are always grouped into several blocks, which each corresponds to a local conserved region on the protein sequence.The experiments conducted in this work demonstrate that the derived long patterns automatically discover the important residues that form one or several hot regions of protein-protein interactions. The methodology is evaluated by conducting experiments on the web server MAGIIC-PRO based on a well known benchmark containing 220 protein chains from 72 distinct complexes. Among the tested 218 proteins, there are 900 sequential blocks discovered, 4.25 blocks per protein chain on average. About 92% of the derived blocks are observed to be clustered in space with at least one of the other blocks, and about 66% of the blocks are found to be near the interface of protein-protein interactions. It is summarized that for about 83% of the tested proteins, at least two interacting blocks can be discovered by this approach.This work aims to demonstrate that the important residues associated with the interface of protein-protein interactions may be automatically discovered by sequential pattern mining. The detected regions possess high conservation and thus are considered as the computational hot regions. This information would be useful to characterizing protein sequences, predicting protein function, finding potential partners, and facilitating protein docking for drug discovery. Identification of functionally important regions directly from a protein sequence is a challenging problem in molecular biology -7. Inveshot spots; they give rise to a significant increase in the absolute binding energy when mutated to alanine  is not available in the PDB file and the protein chain  does not have enough homologues for pattern mining (< 5 homologues). As listed in Table The summary of the experimental results on the second dataset is provided in Table Here we define two indices to evaluate the quality of a pattern:Clustering propensity: the percentage of sequential blocks in a pattern P that interacts with at least one of the other blocks in P. The interaction between a pair of blocks is defined by the following criterion: there exists an atom from one block that is within 5 Å to an atom of the other block.1. Interface propensity: the percentage of sequential blocks in a pattern P that contacts another protein chain in the complex. The definition of contact is that any of the atoms from the block is within 7 Å to any atom of another protein chain in the complex.2. enzyme-inhibitor complexes slightly outperforms the other categories. It can be seen in Table The clustering propensity of a pattern reflects its reliability. We consider that a higher value of this index indicates that the pattern is more biologically meaningful, either from function or structure point of view. For each query protein, the clustering and interface propensities are calculated for its maximum-size pattern. The average values for different categories of protein complexes are provided in Table Similar conclusion can be made from Table In Table Conservation information is important in predicting hot regions involved in protein-protein binding. However, the conservation information at residue level is not sufficient in predicting hot regions because not all the reported residues are conserved for the same purpose (the one studied in this paper is to preserve the environment of interacting with another protein). The conservation information derived by the pattern mining approach is more precise than that generated by multiple sequence alignment followed by constructing the evolutionary tree. That is, the concurrence of conserved blocks among a subset of protein homologues is focused. The experiments conducted in this paper reveal that the derived conserved blocks tend to cluster together in space and most of the aggregated blocks are related with interacting interfaces. The detected regions possess high conservation and thus are considered as the computational hot regions. By using sequential pattern mining, it may be possible to predict hot spots of an interface without exhaustive mutagenesis and thermodynamic analysis and thus the link between protein functions and their primary sequences can be constructed much more rapidly.In this section, we provide the details about the procedures of discovering and selecting patterns for predicting hot regions.The residues associated with an interface are not necessarily found in one region of the sequence. Instead, it is usually observed that several remote segments of a protein sequence constitute a binding site -59. Sincmaximum relative flexibility. This constraint subsequently sets the upper and lower bounds of an inter-block gap with respect to the length of the gap observed on the query protein. The refinement of the constraint model reduces the complexity of the mining program and largely improves the accuracy of the derived patterns when functional motifs are desired. The idea of WildSpan was previously realized on the web server MAGIIC-PRO [The constraint model of MAGIIC has been refined in our recent work WildSpan  to enhanGIIC-PRO  to facilGIIC-PRO  or SwissGIIC-PRO  annotatiGIIC-PRO .A distinguishing characteristic of pattern mining from multiple sequence alignment in providing conservation information is that the residues in a pattern are simultaneously conserved among a certain amount of protein sequences in the training data. This property is appreciated from two points of view. First, a pattern collects a set of residues that are not necessarily the most highly conserved residues but are for sure to have been conserved simultaneously during evolution. Second, the pattern mining algorithm automatically identifies a subset of sequences from the training data that matches a particular pattern. Usually the resultant support rates are quite low, but it might still make sense since a sub-family could play family-specific functional roles ,6,14,52.i) stands for a gap of i arbitrary residues, and x, i <j, represents a wildcard region of at least i and at most j arbitrary residues. The shown pattern contains two conserved blocks "I-x-H-N" and "E-x(2)-L-x-K". The gaps within a block are called intra-block gaps, and the gaps in between two sequential blocks are called inter-block gaps. Concerning the efficiency of mining process, WildSpan specifies several constraints for these pattern components:We call a pattern generated by WildSpan a cluster-like pattern. The residues inside a pattern are always clustered into several sequential blocks. The gaps in between two blocks are usually large and irregular. Here comes an example: "I-x-H-N-x-E-x(2)-L-x-K-L". In this notation, a conserved residue is recorded by its amino acid symbol, 'x' denotes an arbitrary amino acid, x. After that, in the second phase, WildSpan discovers the complete set of closed long patterns satisfying the inter-block gap constraint by connecting frequent blocks found in the first phase with large irregular gaps. Both the first and second phases execute a procedure call named bounded-prefix-growth, which was developed based on the function prefix-growth of a well known sequential pattern mining algorithm, PrefixSpan [bounded-prefix-growth procedure takes our new constraint framework into account, in order to match both the effectiveness and efficiency considerations. It uses a number of pruning strategies during the mining process. First, it exploits some good properties of the constraints to filter out many unpromising patterns/candidates in the early mining stage aggressively. Second, it recursively projects a sequence database into a smaller search space and grows patterns only in each projected database. Both features contribute to favorable mining efficiency. At the end of the second phase, WildSpan outputs the complete set of patterns that satisfy all the constraints specified by the users. The readers can refer to [Constraint-based sequential pattern mining extracts frequent patterns from unaligned sequences that satisfy the user-specified constraints, where pattern components maintain their order in the sequential data . The algefixSpan . The bourefer to  for the refer to  for the The complete procedures for identifying interacting interfaces for a query protein are as follows:E-value cut-off of 0.01. If the homologues of query protein are not sufficient in Swiss-Prot database (< 5 homologues), the searching is executed one more time against the non-redundant (NR) database [1. Obtaining homologues of a query protein (150 at most): This is achieved by running PSI-BLAST  against database  posted o2. Executing pattern mining: The minimum support is initially set as 100% and decreased repeatedly until at least one pattern with five blocks is discovered. A sequential block must contain as least three conserved residues, and the maximum length of an intra-block gap is 3. The mining process is terminated once the mining period exceeds four minutes in a single run, which often happens when the setting of minimum support constraint is too low such that the number of patterns explodes. If no patterns with five blocks can be reported with the previous settings, MAGIIC-PRO is invoked iteratively with the constraint on minimum number of blocks relaxed by one at a time.conservation scores are represented in different colors. The color level of a residue x is defined as: L(x) = ceil(9 × R(x)), where the conservation score R(x) is calculated by the following equation:3. Emerging information from all the patterns with two or more blocks into one conservation plot: The derived patterns are collected together to create a conservation plot. The conservation plot provides a whole picture about the conserved residues of a query protein. In this plot, the conservation level of each residue is determined by the percentage of total number of supporting proteins merged from different patterns.Here, the The conservation plot is reported with the derived patterns to provide more detailed information when a pattern is examined.sticks representation. The conservation plots are generated based on the conservation scores calculated by the ConSurf server [Here we use an example to illustrate the value of a long pattern when compared to traditional conservation scores. Figure f server . In thisf server . It can The authors declare that they have no competing interests. See funding sources in Acknowledgements.CMH designed, performed all calculations and analyses, and drafted the manuscript. CYC provided guidance on design of the methodology, interpretation of the data, and manuscript preparation. BJL aided in the guidance on the study and provided financial support. CCH, CCL, LIAO, and TLW participated in the experiments.


Knowledge of protein domain boundaries is critical for the characterisation and understanding of protein function. The ability to identify domains without the knowledge of the structure – by using sequence information only – is an essential step in many types of protein analyses. In this present study, we demonstrate that the performance of DomainDiscovery is improved significantly by including the inter-domain linker index value for domain identification from sequence-based information. Improved DomainDiscovery uses a Support Vector Machine (SVM) approach and a unique training dataset built on the principle of consensus among experts in defining domains in protein structure. The SVM was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence.Improved DomainDiscovery is compared with other methods by benchmarking against a structurally non-redundant dataset and also CASP5 targets. Improved DomainDiscovery achieves 70% accuracy for domain boundary identification in multi-domains proteins.Improved DomainDiscovery compares favourably to the performance of other methods and excels in the identification of domain boundaries for multi-domain proteins as a result of introducing support vector machine with benchmark_2 dataset. Results are shown in Table 4 in . SummaryARS developed and implemented the algorithm, prepared datasets, programming in C++, Shell and Perl scripts, interpreted the results, performed testing and drafted the manuscript and AYZ edited the manuscript and introduced the problem initially.Comparison performance of domain definition methods for 50 protein chains. Word file.Click here for fileResults of domain boundary predictions for 21 CASP5 targets. Word file.Click here for file


Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways.Saccharomyces cerevisiae. We apply the global optimization tool from combinatorial optimization algorithms to cluster the yeast proteins based on the global protein interaction information. We then use this clustering information to help us predict protein functions. We use our algorithm together with the direct neighbor algorithm [Here we present a new approach utilizing the classic Traveling Salesman Problem to study the protein-protein interactions and to predict protein functions in budding yeast lgorithm  on charaOur method is a promising one to be used as a general tool to predict functions of uncharacterized proteins and a successful sample of using computer science knowledge and algorithms to study biological problems. With the development of the genome projects, the focus of research has shifted from studying a single gene or protein to analyzing groups of genes or proteins. In addition to the progress in genome sequencing, researchers have also made great progress in the area called proteomics where people study proteins on the genome level based on their sequence data and their interaction information on a large scale. Protein-protein interactions are very informative for protein function predictions. We speculate that proteins interacting with each other are within the same functional group or within closely related functional groups. By this reasoning, if we have adequate protein-protein interaction information, we can try to predict the functions of uncharacterized proteins based on their interacting neighbors that have been characterized. We can further predict the biological pathways of those proteins. With the rapid progress in identifying protein-protein interactions systematically using two-hybrid experiments and mass spectrometry, we have collected a wealth of information on protein interactions. Here we study in the field of yeast protein clustering and function prediction utilizing a combinatorial optimization tool. The results show that we can cluster the proteins based on their interaction patterns, and we can make predictions of their biological functions based on those clustering. The prediction method works better than the traditional method based only on the direct neighbors of the query protein because our method adopts a global view and hence makes better usage of the information available. With the success in predicting yeast protein functions with the interaction database currently available, we can anticipate continued success as we get more accurate and larger collections of protein-protein interaction information from additional experimental results. Also, we can apply the same methodology to other more complicated organisms, including humans.In a previously reported study , the autOther workers in the field have been trying to utilize protein interaction information to predict protein functions using a direct neighbor approach – for a particular protein they try to identify all its interacting neighbors and use a simple mechanism such as voting ("the majority rule") to determine which might be the most likely function . However.The Traveling Salesman Problem (TSP) is a classic problem in the field of graph theory and combinatorial optimization. The Traveling Salesman Problem can be described as following: Given n cities where the distances between any two cities are known, a traveling salesman wants to visit all n cities in a tour so that each city is visited exactly once and the total distance of traveling is minimal . The TraThe Traveling Salesman Problem has many applications in areas such as vehicle routing, job sequencing and data array clustering. The key to convert a data array clustering problem into a Traveling Salesman Problem is to think of the rows and columns as the intermediate cities visited ,5,6. In ij, and transform the clustering problem into a Traveling Salesman Problem by using an auxiliary matrix (see methods section.)In this approach we download the yeast protein interaction database , describWhen we input the auxiliary matrix to Concorde, we obtain the solution in the form of a permutation of the rows or columns. We re-arrange the rows and columns of the protein interaction matrix according to this permutation and find the permutation produces a matrix with much better patterns of clustering. To quantitatively define the clusters, we compute the difference scores between each two adjacent rows by counting how many corresponding cells are of different values. When we plot the difference scores along the permutated rows we find the scores have a distribution of dozens of peaks over a flat baseline Figure . For comWe analyze the distribution of the difference scores in Figure We download the protein function catalogue . To get The analysis above gives us a better understanding of the behaviors of the two approaches. To get the best of both worlds, we use a combination of the two approaches in our prediction. We try various combinations of the two using either the protein's degree or the protein's similarity with the query protein in terms of interaction patterns as the cut-off. We find the following rule simple and effective – when the protein's degree is lower than 4, we use the TSP approach; when the protein's degree is greater than or equal to 4, we use the direct neighbor approach. This way we get a prediction accuracy of 69.72% as compared to the 64.81% from the direct neighbor approach alone.These results show that our method can give better predictions of protein functions by incorporating the global optimization algorithms. This is especially useful when the protein has no or very few characterized neighbors directly interacting with it. Since we utilize global information of the interactions instead of concerning only the direct interactions in the neighborhood, the method is more robust with regard to local inaccuracies or incompleteness of the information.One concern of this method is the accuracy of the topology of the network. We need to take into account the false positives and false negatives of the protein-protein interactions obtained from experiments. Incorrect input data affect prediction accuracy and we expect to get better predictions when the input data has better quality.Another determinant of the method's prediction accuracy is the fineness of the classification. The more coarse-grained the classification, the fewer degrees of freedom there are in the network, and therefore the more likely we will have a correct prediction. On the other hand, to gain more insight from the predictions, we would like the method to be able to predict protein functions on a more fine-grained level.At this point, we use the first level GO (Gene Ontology) classification for our prediction based on the information we can get from the protein function catalogue. In the future, with more protein annotation and protein-protein interaction data available, we will apply our prediction system on different levels of classification and find a level at which we can predict with meaningful accuracy and the predictions will be most insightful for further biological studies.Certainly, when we predict the functions for uncharacterized proteins, the ultimate way to validate the predictions is to perform biological experiments. However, the predictions produced by computational methods can give us a good place to begin the experimental exploration and can hence reduce the amount of bench work needed. Instead of speculating wild guesses for an uncharacterized protein's functions, we can form some educated hypotheses and perform experiments to test those hypotheses. The clustering information can also give us some insights into biological pathways because proteins functioning in the same pathway tend to interact with each other and fall into the same cluster. Therefore the global optimization clustering method can help us either to better understand some pathways or to find the missing pieces in them.The combinatorial optimization tools we use here can easily be used on larger data sets. Given that the Concorde program has solved a 24,978-city TSP problem to the optimum [The reason why our TSP solver based clustering performs better than traditional gene clustering algorithms such as hierarchical clustering or nearest neighbor tree clustering is that the latter methods are essentially greedy bottom-up algorithms where they progressively combine the most similar nodes or clusters at each step till a tree of clusters is built. Greedy Most recently, Climer and Zhang have proposed the TSPCluster algorithm , which iThe success of the method relies on the insight that we need to get information, not only from the protein's immediate neighbors, but also from other components more remotely related. Our method is still a simple one in that we adopt a simple rule where we use the clustering information for proteins with small numbers of neighbors and use direct voting for proteins with more neighbors. If we try to perceive the protein interaction relationship with a more integrated view, we can see that a protein can have direct neighbors, indirect neighbors with a certain number of "bridge" proteins, non-neighbors in the same cluster, and non-neighbors in different clusters. If we assign different weights to those relationships according to the distances of how the proteins are related, and we fine-tune the weights based on our training sets, we hope to get a more sophisticated and more accurate prediction system.In this study we have performed yeast protein clustering and function prediction utilizing a combinatorial optimization tool. Our results show that we can cluster the proteins based on their interaction patterns, and that we can make predictions of the biological functions of uncharacterized proteins based on the clustering. The clustering reveals the global patterns of protein-protein interactions within and across functional classes. Although the clustering is not an exact replica of the protein-protein interactions in a proteome, it can be used as a base for protein function prediction. Our prediction method works better than the traditional method based only on the direct neighbors of the query protein in terms of prediction accuracy and prediction robustness with regard to local inaccuracies or incompleteness. The advantage is more prominent when the protein has very few characterized immediate neighbors or no such neighbors at all. The success of our method lies in the fact that it adopts a global view and hence makes better use of the information available.Our approach is the first one to use the Traveling Salesman Problem, a classical and well studied computer problem and a combinatorial optimization tool, to study the protein-protein interactions from a global point of view. The results show that this approach is a promising one to be used as a general tool to predict functions of uncharacterized proteins. This is a successful sample of using computer science knowledge and algorithms to study biological problems. With the success of being able to predict yeast protein functions more accurately based on the yeast protein database currently available, we can anticipate continued success as we get more complete protein-protein interaction information from additional experimental results. Also, we can apply the same methodology to other more complicated organisms, including humans.. We downloaded the Concorde TSP solver from Concorde Home . We ran the Concorde program on a Sun ® Ultra 10 work station with a total of 32 GB memory. Our protein function prediction algorithm was implemented in the Perl programming language and was run on an Intel® Xeon processor 2.80 GHz with 2.00 GB RAM installed with Microsoft Windows operating system. We used Microsoft Excel and the SAS® software package for data analysis.We downloaded the yeast protein interaction database and yeast protein function catalogue from the Comprehensive Yeast Genome Database Let ρ indicate the permutation of both the rows and the columns because the interaction matrix is a symmetric one. The Measure of Effectiveness (ME) represents the overall similarity and it is the objective function to be maximized. ME is calculated as following ,9:ME(ρ) = With the symmetry between the rows and columns, this function reduces toME(ρ) = Therefore, the network clustering problem becomes a combinatorial optimization problem where the optimal clustering corresponds to the configuration or permutation ρ where ME(ρ) is maximal. This amounts to a Traveling Salesman Problem looking for an optimized permutation ρ with the distance matrix being aaa@423B@. We use y matrix . The solThe author(s) declare that they have no competing interests.Olin Johnson conceived of the study, participated in its design and revised the manuscript. Jing Liu carried out the design, implementation and data analysis and drafted the manuscript. Both authors read and approved the final manuscript.


Incorrectly annotated sequence data are becoming more commonplace as databases increasingly rely on automated techniques for annotation. Hence, there is an urgent need for computational methods for checking consistency of such annotations against independent sources of evidence and detecting potential annotation errors. We show how a machine learning approach designed to automatically predict a protein's Gene Ontology (GO) functional class can be employed to identify potential gene annotation errors.inconsistent with the UniProt functions assigned to their human counterparts. In contrast, 97% of the predicted annotations generated using a machine learning approach were consistent with the UniProt annotations of the human counterparts, as well as with available annotations for these mouse protein kinases in the Mouse Kinome database.In a set of 211 previously annotated mouse protein kinases, we found that 201 of the GO annotations returned by AmiGO appear to be We conjecture that most of our predicted annotations are, therefore, correct and suggest that the machine learning approach developed here could be routinely used to detect potential errors in GO annotations generated by high-throughput gene annotation projects.Nature 2002, 420:563–73) have provided their response to Andorf et al, directly following the correspondence.Editors Note : Authors from the original publication (Okazaki et al.:  As more genomic sequences become available, functional annotation of genes presents one of the most important challenges in bioinformatics. Because experimental determination of protein structure and function is expensive and time-consuming, there is an increasing reliance on automated approaches to assignment of Gene Ontology (GO)  functionMost automated approaches to protein function annotation rely on transfer of annotations from previously annotated proteins, based on sequence or structural similarity. Such annotations are susceptible to several sources of error, including errors in the original annotations from which new annotations are inferred, errors in the algorithms, bugs in the programs or scripts used to process the data, clerical errors on the part of human curators, among others. The effect of such errors can be magnified because they can propagate from one set of annotated sequences to another through widespread use of automated techniques for genome-wide functional annotation of proteins -5. Once Previous work of several groups, including our own -19 has dHere we demonstrate that a machine learning approach, designed to predict GO functional classifications for proteins, can be used to identify and correct potential annotation errors. In this study, we focused on a small but clinically important subset of protein kinases, for which we "stumbled upon" potential annotation errors while evaluating the performance of protein function classification algorithms. We chose a set of protein kinases categorized under the GO class GO0004672, Protein Kinase Activity, which includes proteins with serine/threonine (Ser/Thr) kinase activity (GO0004674) and tyrosine (Tyr) kinase activity (GO0004713). Post-translational modification of proteins by phosphorylation plays an important regulatory role in virtually every signaling pathway in eukaryotic cells, modulating key biological processes associated with development and diseases including cancer, diabetes, hyperlipidemia and inflammation ,21. It ik-gram composition of a protein sequence, with a classifier that relies on transfer of annotation from PSI-BLAST hits (see Methods for details). A protein kinase classifier was trained on a set of 330 human protein kinases from the Ser/Thr protein kinase (GO0004674) and Tyr protein kinase (GO0004713) functional classes based on direct and indirect annotations assigned by AmiGO [human protein kinases, and ii) a dataset of 244 mouse protein kinases drawn from the same GO functional classes. The initial datasets were not filtered based on evidence codes or sequence identity cutoffs.The initial aim of our experiments was to evaluate the effectiveness of machine learning approaches to automate sequence-based classification of protein kinases into subfamilies. Because both the Ser/Thr and Tyr subfamilies contain highly divergent members, some of which share less than 10% sequence identity with other members, they offer a rigorous test case for evaluating the potential general utility of this approach. Previously, we developed HDTree , a two-sby AmiGO , a valuaUsing the AmiGO annotations as reference, the resulting HDTree classifier correctly distinguished between Ser/Thr kinases and Tyr kinases in the human kinase dataset with an overall accuracy of 89.1% and a kappa coefficient of 0.76. In striking contrast, the accuracy of the classifier on the mouse kinase dataset was only 15.1%; the correlation between the GO functional categories predicted by the classifier and the AmiGO reference labels was an alarming -0.40: 72 of the 244 mouse kinases were classified as Ser/Thr kinases, 105 as Tyr kinases, and 67 as "dual specificity" kinases (belonging to both GO0004674 and GO0004713 classes)  achieved a classification accuracy of 97.2%, with a kappa coefficient of 0.93, on the mouse kinase dataset. As illustrated in Figure The HDTree method uses a decision tree built from the output from eight individual classifiers. A decision tree is built by selecting, in a greedy fashion, the individual classifier that provides the maximum information about the class label at each step, . By examExamination of the Mouse Kinome Database  reveals Identifying potential annotation errors in a specific dataset such as the mouse kinase dataset solves only a part of a larger problem. Because annotation errors can propagate across multiple databases through the widespread – and often necessary – use of information derived from available annotations, it is important to track and correct errors in other databases that rely on the erroneous source. For example, using AmiGO, we retrieved 136 rat protein kinases for which annotations had been transferred from mouse protein kinases based on homology  with one of the 201 erroneously annotated mouse protein kinases. Examination of the UniProt records for these 136 rat protein kinases revealed that 94 of those labeled as "Ser/Thr" kinases by UniProt had AmiGO annotations of "Tyr" or "dual specificity" kinase, and 42 of those labeled as "Tyr" kinases by UniProt had AmiGO annotations of "Ser/Thr" or "dual specificity" kinase [see Additional files multi label classification problem), or more generally, to a subset of nodes in a directed-acyclic graph, e.g., the GO hierarchy, (the structured label classification problem). Fortunately, a number of research groups have developed machine learning algorithms for multi-label and structured label classification and demonstrated their application in large-scale protein function classification [A recent study found that the GO annotations with ISS  evidence code could have error rates as high as 49% . This arfication -33. We csequence-based machine learning methods for annotation of protein sequences. With the increasing availability of other types of data , there is a growing interest in machine learning and other computational methods for genome-wide prediction of protein function using diverse types of information [In this study, we have limited our attention to ormation -39. SuchThe increasing reliance on automated tools in genome-wide functional annotation of proteins has led to a corresponding increase in the risk of propagation of annotation errors across genome databases. Short of direct experimental validation of every annotation, it is impossible to ensure that the annotations are accurate. The results presented here and in recent related studies -11 underWe constructed an HDTree binary classifier, described below, for each of the three kinase families. The first two kinase families correspond to the GO labels GO0004674 (Ser/Thr kinases) or GO0004713 (Tyr kinases) but not both; the third family corresponds to dual-specificity kinases that belong to both GO0004674 and GO0004713. Classifier #1 distinguishes between Ser/Thr kinases and the rest . Similarly, classifier #2 distinguishes between Tyr kinases and the rest . Classifier #3 distinguishes dual-specificity kinases from the rest (those with only Ser/Thr or Tyr activity), based on the predictions generated by classifier #1 and classifier #2 as follows: If only classifier #1 generates a positive prediction, the corresponding sequence is classified as (exclusively) a Ser/Thr kinase. If only classifier #2 generates a positive prediction, the corresponding sequence is classified as (exclusively) Tyr kinase. If both classifiers generate a positive prediction or if both classifiers generate a negative prediction, the corresponding sequence is classified as a dual-specificity kinase. We interpret the disagreement between the classifiers as indicative of signaling evidence that the protein is neither exclusively Ser/Thr nor Tyr, and hence, likely to have dual specificity. More sophisticated evidence combination methods could be used instead. However, this simple technique worked sufficiently well in the case of this dataset . Thus, each HDTree classifier is a decision tree classifier that is trained to predict the binary class label of a query sequence based on the 8-tuple of class labels predicted by the eight individual classifiers. Because HDTree is a decision tree, it is easy to determine which individual classifier(s) provided the most information in regards to the predicted class label. In the resulting tree, nodes near the top of the tree provided the most information about the class label. Thus, HDTree can also facilitate identification of the determinative biological sequence signals. We used the Weka version 3.4.4 implementation [As noted above, an HDTree binary classifier  is constentation  (J4.8) oentation .We describe below, a class of probabilistic models for sequence classification.We start by introducing the general procedure for building a classifier from a probabilistic generative model.α for sequences defined over some alphabet Σ . The model α specifies for any sequence s1, ..., sn, the probability Pα of generating the sequence cj are generated by the probabilistic generative model α (cj).Suppose we can specify a probabilistic model cj. Therefore, given the probabilistic generative model for each of the classes in C  for sequences over the alphabet Σ, we can compute the most likely class label c used in the modelsi = the ithelement in the sequence cj = the jth class in the class set CThe Naïve Bayes classifier assumes that each element of the sequence is independent of the other elements given the class label. Consequently,bag of letters. We now consider two Naive Bayes-like models based on k-grams.Note that the Naive Bayes classifier for sequences treats each sequence as though it were simply a k-grams (NB k-grams) [k along each sequence to generate a bag of k-grams representation of the sequence. Much like in the case of the Naive Bayes classifier described above treats each k-gram in the bag to be independent of the others given the class label for the sequence. Given this probabilistic model, the standard method for classification using a probabilistic model can be applied. The probability model associated with Naïve Bayes k-grams:The Naive Bayes k-grams) ,13,41 mek-grams approach is that successive k-grams extracted from a sequence share k-1 elements in common. This grossly and systematically violates the independence assumption of Naive Bayes.A problem with the NB k) or the NB(k) model [k-grams in a sequence. We represent the dependencies in a graphical form by drawing edges between the elements that are directly dependent on each other.We introduce the Naive Bayes (k) model ,13,41 toα that captures the dependencies among overlapping k-grams is given by:Using the Junction Tree Theorem for graphical models , it can Now, given this probabilistic model, we can use the standard approach to classification given a probabilistic model. It is easily seen that when k = 1, Naive Bayes 1-grams as well as Naive Bayes (1) reduce to the Naive Bayes model.The relevant probabilities required for specifying the above models can be estimated using standard techniques for estimation of probabilities using Laplace estimators .c is 1 if the class label for the top scoring hit is c. Otherwise, it is 0. An e-value cut-off of 0.0001 was used for PSI-BLAST, with all other parameters set to their default values.We used PSI-BLAST (from the latest release of BLAST)  to constThe performance measures  used to CA conceived of and designed the study, carried out the data analysis and visualization, developed the Java computer code, and drafted the manuscript. DD and VH contributed to the design of the study, analysis and interpretation of results, and writing of the manuscript. All authors read and approved the final manuscript.1,4, David Hill2,5, Judith Blake2,5, Richard Baldarelli2, Piero Carninci3,4, and Yoshihide Hayashizaki 1,3,4Masaaki Furuno1Functional RNA Research Program, RIKEN Frontier Research System, RIKEN Wako Institute, Wako, Japan. 2Mouse Genome Informatics Consortium, The Jackson Laboratory, Bar Harbor, Maine, United States of America. 3Genome Science Laboratory, Discovery Research Institute, RIKEN Wako Institute, Wako, Japan. 4Laboratory for Genome Exploration Research Group, RIKEN Genomic Sciences Center, Yokohama Institute, Kanagawa, Japan. 5Gene Ontology Consortium, The Jackson Laboratory, Bar Harbor, Maine, United States of America)Addresses  annotation errors using a machine learning approach. The authors' method identified a set of errors in GO annotations that relate to a very small subset of results from the 2001/2002 FANTOM2 analysis. These have subsequently been corrected.We agree with the authors point about the importance of detecting the annotation errors. However, we believe that the errors the authors describe are exaggerated in importance as a result of the selection of datasets that they used and for the small set of genes that they studied. We will explain why they obtained these results, and we have identified a data curation change that has been implemented. However, we note such updates and revisions are a daily part of the work of large bio-informatics resources and of the work of the genome informatics community.The strategy employed in FANTOM2 was appropriate and reflected the best strategy for mining large-scale functional information available at the time. In the computational analysis published in 2002 by the FANTOM2 Consortium, protein sequences were compared to other protein sequences and GO annotations were inferred from identical or highly similar proteins. GO annotations were also inferred from InterPro domains that were found in the coding regions of the proteins. The advanced analysis resulted in GO predictions for many proteins we knew nothing about at that time. A subset of the results of this landmark analysis were integrated into Mouse Genome Informatics after the FANTOM2 publication. This data set is important because it was the first analysis of this scale and complexity performed in mouse.et al restricted themselves to the subset of aggressively predicted FANTOM2 GO annotations while not considering high-quality FANTOM2 GO annotations that are represented in MGI using other automated methods. This is because AmiGO by policy does not display annotations inferred from automated methods. Much of the FANTOM data does not appear in AmiGO because it entered the regular MGI annotation stream and receives regular refreshing. As a result, this analysis casts a small subset of the FANTOM2 GO annotations in an unfair light. To obtain a fair analysis of all GO terms annotated at the time of FANTOM2, the original FANTOM2 data are available [By retrieving annotations from AmiGO, Andorf vailable .et al remind us that conclusions based on a particular data set must be viewed in the context of a thorough understanding of how the data was generated and what is being represented in the set that is used for the analysis. The errors in GO annotation found by the authors are not due to general poor quality of FANTOM2 annotation. Rather, unique annotations from FANTOM2, as data associated with a publication, were not being comprehensively updated. We are reminded that any annotations based on computational methods must be regularly re-evaluated. MGI curators have now screened and updated the annotations for genes associated with protein tyrosine and protein serine/threonine kinase activities.The results reported by Andorf Supplementary Table 1: Evidence Codes for AmiGO annotations. A table displaying the Evidence Codes for AmiGO annotations of the mouse protein kinases used in this study.Click here for fileSupplementary Table 2: AmiGO annotations versus UniProt annotations (with UniProt Evidence). A table comparing the annotations found in the AmiGO server with the annotations found in UniProt.Click here for fileSupplementary Table 3: AmiGO labels, UniProt labels, and Predicted Labels for each mouse kinase protein. A table comparing the predicted annotations from our three machine learning classifiers with the annotations of AmiGO and UniProt.Click here for fileSupplementary Data: Machine learning approaches to predict Gene Ontology and/or UniProt Functional labels. The data provided represent the results and performance of all the machine learning approaches used in this study.Click here for fileSupplementary Table 4: Mouse kinases having a human ortholog. A table displaying the human orthologs for the mouse kinases used in this study. The table also displays the identity between these orthologs.Click here for fileSupplementary Table 5: Number of mouse kinases having a specified level of sequence identity with their human orthologs. A table displaying the summary statistics of Supplementary Table Click here for fileSupplementary Note. Because there is only a non-curated reference to the work done on "Rat ISS GO annotations from MGI's mouse gene data," we provide the abstract and a link to the original reference report in this file.Click here for fileSupplementary Table 6: The UniProt and AmiGO annotations for the rat kinase proteins with mouse orthologs. This table displays the UniProt and AmiGO annotations for rat kinase proteins that were annotated based on a mouse ortholog.Click here for fileSupplementary Table 7: Distribution of protein classes for human and mouse proteins annotated by AmiGO, UniProt, and HDTree. This table is a representation of the data used in Figure Click here for file