TOPDOM: database of domains and motifs with conservative location in transmembrane proteins

The TOPDOM database is a collection of domains and sequence motifs located consistently on the same side of the membrane in alpha-helical transmembrane proteins. The database was created by scanning well-annotated transmembrane protein sequences in the UniProt database by specific domain or motif detecting algorithms. The identified domains or motifs were added to the database if they were uniformly annotated on the same side of the membrane of the various proteins in the UniProt database. The information about the location of the collected domains and motifs can be incorporated into constrained topology prediction algorithms, like HMMTOP, increasing the prediction accuracy. AVAILABILITY: The TOPDOM database and the constrained HMMTOP prediction server are available on the page http://topdom.enzim.hu CONTACT: tusi@enzim.hu; lkalmar@enzim.hu.     

The Human PAX6 Mutation Database.

The Human PAX6 Mutation Database contains details of 94 mutations of the PAX6 gene. A Microsoft Access program is used by the Curator to store, update and search the database entries. Mutations can be entered directly by the Curator, or imported from submissions made via the World Wide Web. The PAX6 Mutation Database web page at URL http://www.hgu.mrc.ac.uk/Softdata/PAX6/ provides information about PAX6, as well as a fill-in form through which new mutations can be submitted to the Curator. A search facility allows remote users to query the database. A plain text format file of the data can be downloaded via the World Wide Web. The Curation program contains prior knowledge of the genetic code and of the PAX6 gene including cDNA sequence, location of intron/exon boundaries, and protein domains, so that the minimum of information need be provided by the submitter or Curator.     

Improving subcellular localization prediction using text classification and the gene ontology

MOTIVATION: Each protein performs its functions within some specific locations in a cell. This subcellular location is important for understanding protein function and for facilitating its purification. There are now many computational techniques for predicting location based on sequence analysis and database information from homologs. A few recent techniques use text from biological abstracts: our goal is to improve the prediction accuracy of such text-based techniques. We identify three techniques for improving text-based prediction: a rule for ambiguous abstract removal, a mechanism for using synonyms from the Gene Ontology (GO) and a mechanism for using the GO hierarchy to generalize terms. We show that these three techniques can significantly improve the accuracy of protein subcellular location predictors that use text extracted from PubMed abstracts whose references are recorded in Swiss-Prot.     

Automated protein subfamily identification and classification

Function prediction by homology is widely used to provide preliminary functional annotations for genes for which experimental evidence of function is unavailable or limited. This approach has been shown to be prone to systematic error, including percolation of annotation errors through sequence databases. Phylogenomic analysis avoids these errors in function prediction but has been difficult to automate for high-throughput application. To address this limitation, we present a computationally efficient pipeline for phylogenomic classification of proteins. This pipeline uses the SCI-PHY (Subfamily Classification in Phylogenomics) algorithm for automatic subfamily identification, followed by subfamily hidden Markov model (HMM) construction. A simple and computationally efficient scoring scheme using family and subfamily HMMs enables classification of novel sequences to protein families and subfamilies. Sequences representing entirely novel subfamilies are differentiated from those that can be classified to subfamilies in the input training set using logistic regression. Subfamily HMM parameters are estimated using an information-sharing protocol, enabling subfamilies containing even a single sequence to benefit from conservation patterns defining the family as a whole or in related subfamilies. SCI-PHY subfamilies correspond closely to functional subtypes defined by experts and to conserved clades found by phylogenetic analysis. Extensive comparisons of subfamily and family HMM performances show that subfamily HMMs dramatically improve the separation between homologous and non-homologous proteins in sequence database searches. Subfamily HMMs also provide extremely high specificity of classification and can be used to predict entirely novel subtypes. The SCI-PHY Web server at http://phylogenomics.berkeley.edu/SCI-PHY/ allows users to upload a multiple sequence alignment for subfamily identification and subfamily HMM construction. Biologists wishing to provide their own subfamily definitions can do so. Source code is available on the Web page. The Berkeley Phylogenomics Group PhyloFacts resource contains pre-calculated subfamily predictions and subfamily HMMs for more than 40,000 protein families and domains at http://phylogenomics.berkeley.edu/phylofacts/.     

TSGDB: a database system for tumor suppressor genes

A Web-based database system was constructed and implemented that contains 174 tumor suppressor genes. The database homepage was created to accommodate these genes in a pull-down window so that each gene can be viewed individually in a separate Web page. Information displayed on each page includes gene name, aliases, source organism, chromosome location, expression cells/tissues, gene structure, protein size, gene functions and major reference sources. Queries to the database can be conducted through a user-friendly interface, and query results are returned in the HTML format on dynamically generated web pages. AVAILABILITY: The database is available at http://www.cise.ufl.edu/~yy1/HTML-TSGDB/Homepage.html (data files also at http://www.patcar.org/Databases/Tumor_Suppressor_Genes)     

GeneSilico protein structure prediction meta-server

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta.     

STARS: statistics on inter-atomic distances and torsion angles in protein secondary structures

SUMMARY: A graphics package has been developed for performing statistics on interatomic distances and torsion angles in protein secondary structures (STARS) from a protein crystal structure database. It allows one to obtain both the graphical view and the text format of distributions of the distances and angles for atoms located in 10 types of protein secondary structures. STARS will facilitate assignment of ambiguous NOESY peaks, structure determination by nuclear magnetic resonance, structure validation and comparison of protein folds. AVAILABILITY: All data, documents and execute files are freely downloadable at http://stars.zhengyuhome.com. The software works appropriately on Windows system, without any compilation or installation. CONTACT: dbsydw@nus.edu.sg.     

The PEDANT genome database

The PEDANT genome database (http://pedant.gsf.de) provides exhaustive automatic analysis of genomic sequences by a large variety of established bioinformatics tools through a comprehensive Web-based user interface. One hundred and seventy seven completely sequenced and unfinished genomes have been processed so far, including large eukaryotic genomes (mouse, human) published recently. In this contribution, we describe the current status of the PEDANT database and novel analytical features added to the PEDANT server in 2002. Those include: (i) integration with the BioRS data retrieval system which allows fast text queries, (ii) pre-computed sequence clusters in each complete genome, (iii) a comprehensive set of tools for genome comparison, including genome comparison tables and protein function prediction based on genomic context, and (iv) computation and visualization of protein-protein interaction (PPI) networks based on experimental data. The availability of functional and structural predictions for 650 000 genomic proteins in well organized form makes PEDANT a useful resource for both functional and structural genomics.     

The MEMPACK alpha-helical transmembrane protein structure prediction server

MOTIVATION: The experimental difficulties of alpha-helical transmembrane protein structure determination make this class of protein an important target for sequence-based structure prediction tools. The MEMPACK prediction server allows users to submit a transmembrane protein sequence and returns transmembrane topology, lipid exposure, residue contacts, helix-helix interactions and helical packing arrangement predictions in both plain text and graphical formats using a number of novel machine learning-based algorithms. AVAILABILITY: The server can be accessed as a new component of the PSIPRED portal by at http://bioinf.cs.ucl.ac.uk/psipred/.     

Saccharomyces Genome Database (SGD) provides biochemical and structural information for budding yeast proteins

The Saccharomyces Genome Database (SGD: http://genome-www.stanford.edu/Saccharomyces/) has recently developed new resources to provide more complete information about proteins from the budding yeast Saccharomyces cerevisiae. The PDB Homologs page provides structural information from the Protein Data Bank (PDB) about yeast proteins and/or their homologs. SGD has also created a resource that utilizes the eMOTIF database for motif information about a given protein. A third new resource is the Protein Information page, which contains protein physical and chemical properties, such as molecular weight and hydropathicity scores, predicted from the translated ORF sequence.     

PMUT: a web-based tool for the annotation of pathological mutations on proteins

PMUT allows the fast and accurate prediction (approximately 80% success rate in humans) of the pathological character of single point amino acidic mutations based on the use of neural networks. The program also allows the fast scanning of mutational hot spots, which are obtained by three procedures: (1) alanine scanning, (2) massive mutation and (3) genetically accessible mutations. A graphical interface for Protein Data Bank (PDB) structures, when available, and a database containing hot spot profiles for all non-redundant PDB structures are also accessible from the PMUT server.     

XFINGER: A tool for searching and visualising protein fingerprints and patterns

A tool for searching pattern and fingerprint databases is described.Fingerprints are groups of motifs excised from conserved regionsof sequence alignments and used for iterative database scanning.The constituent motifs are thus encoded as small alignmentsin which sequence information is maximised with each databasepass; they therefore differ from regular-expression patterns,in which alignments are reduced to single consensus sequences.Different database formats have evolved to store these disparatetypes of information, namely the PROSITE dictionary of patternsand the PRINTS fingerprint database, but programs have not beenavailable with the flexibility to search them both. We havedeveloped a facility to do this: the system allows query sequencesto be scanned against either PROSITE, the full PRINTS database,or against individual fingerprints. The results of fingerprintsearches are displayed simultaneously in both text and graphicalwindows to render them more tangible to the user. Where structuralcoordinates are available, identified motifs may be visualisedin a 3D context. The program runs on Silicon Graphics machinesusing GL graphics libraries and on machines with X servers supportingthe PEX extension: its use is illustrated here by depictingthe location of low-density lipoprotein-binding (LDL) motifsand leucine-rich repeats in a mosaic G-protein-coupled receptor(GPCR).     

OntoBlast function: From sequence similarities directly to potential functional annotations by ontology terms

OntoBlast allows one to find information about potential functions of proteins by presenting a weighted list of ontology entries associated with similar sequences from completely sequenced genomes identified in a BLAST search. It combines, in a single analysis step, the search for sequence similarities in several species with the association of information stored in ontologies. From each identified ontology term a list of genes, which share the functional annotation, can be retrieved. The OntoBlast function is an integral part of the 'Ontologies TO GenomeMatrix' tool which provides an alternative entry point from ontology terms to the Genome-Matrix database. OntoBlast's web interface is accessible on the 'Ontologies TO GenomeMatrix Gate' page at http://functionalgenomics.de/ontogate/.     

LOC3D: annotate sub-cellular localization for protein structures

LOC3D (http://cubic.bioc.columbia.edu/db/LOC3d/) is both a weekly-updated database and a web server for predictions of sub-cellular localization for eukaryotic proteins of known three-dimensional (3D) structure. Localization is predicted using four different methods: (i) PredictNLS, prediction of nuclear proteins through nuclear localization signals; (ii) LOChom, inferring localization through sequence homology; (iii) LOCkey, inferring localization through automatic text analysis of SWISS-PROT keywords; and (iv) LOC3Dini, ab initio prediction through a system of neural networks and vector support machines. The final prediction is based on the method that predicts localization with the highest confidence. The LOC3D database currently contains predictions for >8700 eukaryotic protein chains taken from the Protein Data Bank (PDB). The web server can be used to predict sub-cellular localization for proteins for which only a predicted structure is available from threading servers. This makes the resource of particular interest to structural genomics initiatives.     

The Integr8 project--a resource for genomic and proteomic data

Integr8 (http://www.ebi.ac.uk/integr8/) is providing an integration layer for the exploitation of genomic and proteomic data by drawing on databases maintained at major bioinformatics centres in Europe. Main aims are to store the relationships of biological entities to each other and to entries in other databases, to provide a framework that allows for new kinds of data to be integrated, and to offer an entity-centric view of complete genomes and proteomes. Basic tools for data integration comprise the Proteome Analysis database, the International Protein Index (IPI), the Universal Protein sequence archive (UniParc) and the Genome Reviews. Entry points for the Integr8 portal depend on the users entity of interest: from browsing the taxonomy or with a predetermined species of interest, the species page can be used, and a simple search page leads to different applications when looking for certain protein sequences or genes. Customisable statistics data are available from the BioMart application, and pre-prepared data can be downloaded from the FTP site.     

Phyto diab care: Phytoremedial database for antidiabetics

Diabetes, a chronic disease debilitating to normal healthy lifestyle, onsets due to insufficient amount of insulin production or ineffective utilization of the amount produced. Although, pharmaceutical research has brought up remedial drugs and numerous candidates in various phases of clinical trials, off-target effects and unwanted physiological actions are a constant source of concern and contra indicatory in case of diabetic patients. Here we present a phytoremedial database, Phyto Diab Care, broadly applicable to any known anti-diabetic medicinal plant and phytochemicals sourced from them. Utilization of the traditional medicine knowledge for combating diabetes without creating unwanted physiological actions is our major emphasis. Data collected from peer-reviewed publications and phytochemicals were added to the customizable database by means of an extended relational design. The strength of this resource is in providing rapid retrieval of data from large volumes of text at a high degree of accuracy. Enhanced web interface allows multi-criteria based information filtering. Furthermore, the availability of 2D and 3D structures from molecular docking studies with any efficacy on the insulin signaling pathway makes the resource searchable and comparable in an intuitive manner. Phyto Diab Care compendium is publicly available and can be found in online.

Availability

http://www.gbpuat-cbsh.ac.in/departments/bi/database/phytodiabcare/HOME%20PAGE/Home%20page.html     

GWFASTA: server for FASTA search in eukaryotic and microbial genomes

Similarity searches are a powerful method for solving important biological problems such as database scanning, evolutionary studies, gene prediction, and protein structure prediction. FASTA is a widely used sequence comparison tool for rapid database scanning. Here we describe the GWFASTA server that was developed to assist the FASTA user in similarity searches against partially and/or completely sequenced genomes. GWFASTA consists of more than 60 microbial genomes, eight eukaryote genomes, and proteomes of annotatedgenomes. Infact, it provides the maximum number of databases for similarity searching from a single platform. GWFASTA allows the submission of more than one sequence as a single query for a FASTA search. It also provides integrated post-processing of FASTA output, including compositional analysis of proteins, multiple sequences alignment, and phylogenetic analysis. Furthermore, it summarizes the search results organism-wise for prokaryotes and chromosome-wise for eukaryotes. Thus, the integration of different tools for sequence analyses makes GWFASTA a powerful toolfor biologists.     

ProPred1: prediction of promiscuous MHC Class-I binding sites

SUMMARY: ProPred1 is an on-line web tool for the prediction of peptide binding to MHC class-I alleles. This is a matrix-based method that allows the prediction of MHC binding sites in an antigenic sequence for 47 MHC class-I alleles. The server represents MHC binding regions within an antigenic sequence in user-friendly formats. These formats assist user in the identification of promiscuous MHC binders in an antigen sequence that can bind to large number of alleles. ProPred1 also allows the prediction of the standard proteasome and immunoproteasome cleavage sites in an antigenic sequence. This server allows identification of MHC binders, who have the cleavage site at the C terminus. The simultaneous prediction of MHC binders and proteasome cleavage sites in an antigenic sequence leads to the identification of potential T-cell epitopes. AVAILABILITY: Server is available at http://www.imtech.res.in/raghava/propred1/. Mirror site of this server is available at http://bioinformatics.uams.edu/mirror/propred1/ Supplementary information: Matrices and document on server are available at http://www.imtech.res.in/raghava/propred1/page2.html     

MHCPEP, a database of MHC-binding peptides: update 1997.

MHCPEP (http://wehih.wehi.edu.au/mhcpep/) is a curated database comprising over 13 000 peptide sequences known to bind MHC molecules. Entries are compiled from published reports as well as from direct submissions of experimental data. Each entry contains the peptide sequence, its MHC specificity and where available, experimental method, observed activity, binding affinity, source protein and anchor positions, as well as publication references. The present format of the database allows text string matching searches but can easily be converted for use in conjunction with sequence analysis packages. The database can be accessed via Internet using WWW or FTP.