IWS: Integrated web server for protein sequence and structure analysis

Rapid increase in protein sequence information from genome sequencing projects demand the intervention of bioinformatics tools to recognize interesting gene-products and associated function. Often, multiple algorithms need to be employed to improve accuracy in predictions and several structure prediction algorithms are on the public domain. Here, we report the availability of an Integrated Web-server as a bioinformatics online package dedicated for in-silico analysis of protein sequence and structure data (IWS). IWS provides web interface to both in-house and widely accepted programs from major bioinformatics groups, organized as 10 different modules. IWS also provides interactive images for Analysis Work Flow, which will provide transparency to the user to carry out analysis by moving across modules seamlessly and to perform their predictions in a rapid manner

3PFDB - A database of Best Representative PSSM Profiles (BRPs) of Protein Families generated using a novel data mining approach

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HORI: a web server to compute Higher Order Residue Interactions in protein structures

Background: Folding of a protein into its three dimensional structure is influenced by both local and global interactions within a protein. Higher order residue interactions, like pairwise, triplet and quadruplet ones, play a vital role in attaining the stable conformation of the protein structure. It is generally agreed that higher order interactions make significant contribution to the potential energy landscape of folded proteins and therefore it is important to identify them to estimate their contributions to overall stability of a protein structure. Results: We developed HORI [Higher order residue interactions in proteins], a web server for the calculation of global and local higher order interactions in protein structures. The basic algorithm of HORI is designed based on the classical concept of four-body nearest-neighbour propensities of amino-acid residues. It has been proved that higher order residue interactions up to the level of quadruple interactions plays a major role in the three-dimensional structure of proteins and is an important feature that can be used in protein structure analysis. Conclusion: HORI server will be a useful resource for the structural bioinformatics community to perform analysis on protein structures based on higher order residue interactions. HORI server is a highly interactive web server designed in three modules that enables the user to analyse higher order residue interactions in protein structures. HORI server is available from the URL: http://caps. ncbs.res.in/hori

PURE: A webserver for the prediction of domains in unassigned regions in proteins

<p>Abstract</p> <p>Background</p> <p>Protein domains are the structural and functional units of proteins. The ability to parse proteins into different domains is important for effective classification, understanding of protein structure, function, and evolution and is hence biologically relevant. Several computational methods are available to identify domains in the sequence. Domain finding algorithms often employ stringent thresholds to recognize sequence domains. Identification of additional domains can be tedious involving intense computation and manual intervention but can lead to better understanding of overall biological function. In this context, the problem of identifying new domains in the unassigned regions of a protein sequence assumes a crucial importance.</p> <p>Results</p> <p>We had earlier demonstrated that accumulation of domain information of sequence homologues can substantially aid prediction of new domains. In this paper, we propose a computationally intensive, multi-step bioinformatics protocol as a web server named as <b>PURE </b>(<b>P</b>rediction of <b>U</b>nassigned <b>RE</b>gions in proteins) for the detailed examination of stretches of unassigned regions in proteins. Query sequence is processed using different automated filtering steps based on length, presence of coiled-coil regions, transmembrane regions, homologous sequences and percentage of secondary structure content. Later, the filtered sequence segments and their sequence homologues are fed to PSI-BLAST, cd-hit and Hmmpfam. Data from the various programs are integrated and information regarding the probable domains predicted from the sequence is reported.</p> <p>Conclusion</p> <p>We have implemented PURE protocol as a web server for rapid and comprehensive analysis of unassigned regions in the proteins. This server integrates data from different programs and provides information about the domains encoded in the unassigned regions.</p

PeptideMine - A webserver for the design of peptides for protein-peptide binding studies derived from protein-protein interactomes

<p>Abstract</p> <p>Background</p> <p>Signal transduction events often involve transient, yet specific, interactions between structurally conserved protein domains and polypeptide sequences in target proteins. The identification and validation of these associating domains is crucial to understand signal transduction pathways that modulate different cellular or developmental processes. Bioinformatics strategies to extract and integrate information from diverse sources have been shown to facilitate the experimental design to understand complex biological events. These methods, primarily based on information from high-throughput experiments, have also led to the identification of new connections thus providing hypothetical models for cellular events. Such models, in turn, provide a framework for directing experimental efforts for validating the predicted molecular rationale for complex cellular processes. In this context, it is envisaged that the rational design of peptides for protein-peptide binding studies could substantially facilitate the experimental strategies to evaluate a predicted interaction. This rational design procedure involves the integration of protein-protein interaction data, gene ontology, physico-chemical calculations, domain-domain interaction data and information on functional sites or critical residues.</p> <p>Results</p> <p>Here we describe an integrated approach called "PeptideMine" for the identification of peptides based on specific functional patterns present in the sequence of an interacting protein. This approach based on sequence searches in the interacting sequence space has been developed into a webserver, which can be used for the identification and analysis of peptides, peptide homologues or functional patterns from the interacting sequence space of a protein. To further facilitate experimental validation, the PeptideMine webserver also provides a list of physico-chemical parameters corresponding to the peptide to determine the feasibility of using the peptide for <it>in vitro </it>biochemical or biophysical studies.</p> <p>Conclusions</p> <p>The strategy described here involves the integration of data and tools to identify potential interacting partners for a protein and design criteria for peptides based on desired biochemical properties. Alongside the search for interacting protein sequences using three different search programs, the server also provides the biochemical characteristics of candidate peptides to prune peptide sequences based on features that are most suited for a given experiment. The PeptideMine server is available at the URL: <url>http://caps.ncbs.res.in/peptidemine</url></p

STIF: Identification of stress-upregulated transcription factor binding sites in Arabidopsis thaliana

The expressions of proteins in the cell are carefully regulated by transcription factors that interact with their downstream targets in specific signal transduction cascades. Our understanding of the regulation of functional genes responsive to stress signals is still nascent. Plants like Arabidopsis thaliana, are convenient model systems to study fundamental questions related to regulation of the stress transcriptome in response to stress challenges. Microarray results of the Arabidopsis transcriptome indicate that several genes could be upregulated during multiple stresses, such as cold, salinity, drought etc. Experimental biochemical validations have proved the involvement of several transcription factors could be involved in the upregulation of these stress responsive genes. In order to follow the intricate and complicated networks of transcription factors and genes that respond to stress situations in plants, we have developed a computer algorithm that can identify key transcription factor binding sites upstream of a gene of interest. Hidden Markov models of the transcription factor binding sites enable the identification of predicted sites upstream of plant stress genes. The search algorithm, STIF, performs very well, with more than 90% sensitivity, when tested on experimentally validated positions of transcription factor binding sites on a dataset of 60 stress upregulated genes