Browse
Submit Data
Databases
API
Help

Dataset Information

42 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

DOMIRE: a web server for identifying structural domains and their neighbors in proteins.

ABSTRACT:

Summary

The DOMIRE web server implements a novel, automatic, protein structural domain assignment procedure based on 3D substructures of the query protein which are also found within structures of a non-redundant protein database. These common 3D substructures are transformed into a co-occurrence matrix that offers a global view of the protein domain organization. Three different algorithms are employed to define structural domain boundaries from this co-occurrence matrix. For each query, a list of structural neighbors and their alignments are provided. DOMIRE, by displaying the protein structural domain organization, can be a useful tool for defining protein common cores and for unravelling the evolutionary relationship between different proteins.

Availability

http://genome.jouy.inra.fr/domire

Contact

jean.garnier@jouy.inra.fr.

SUBMITTER: Samson F

PROVIDER: S-EPMC3315711 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

DOMIRE: a web server for identifying structural domains and their neighbors in proteins.

Samson Franck F Shrager Richard R Tai Chin-Hsien CH Sam Vichetra V Lee Byungkook B Munson Peter J PJ Gibrat Jean-François JF Garnier Jean J

Bioinformatics (Oxford, England) 20120215 7

<h4>Summary</h4>The DOMIRE web server implements a novel, automatic, protein structural domain assignment procedure based on 3D substructures of the query protein which are also found within structures of a non-redundant protein database. These common 3D substructures are transformed into a co-occurrence matrix that offers a global view of the protein domain organization. Three different algorithms are employed to define structural domain boundaries from this co-occurrence matrix. For each query ...[more]

PMID: 22345617

Similar Datasets

RNAbor: a web server for RNA structural neighbors.

Project description:RNAbor provides a new tool for researchers in the biological and related sciences to explore important aspects of RNA secondary structure and folding pathways. RNAbor computes statistics concerning delta-neighbors of a given input RNA sequence and structure (the structure can, for example, be the minimum free energy (MFE) structure). A delta-neighbor is a structure that differs from the input structure by exactly delta base pairs, that is, it can be obtained from the input structure by adding and/or removing exactly delta base pairs. For each distance delta RNAbor computes the density of delta-neighbors, the number of delta-neighbors, and the MFE structure, or MFE (delta) structure, among all delta-neighbors. RNAbor can be used to study possible folding pathways, to determine alternate low-energy structures, to predict potential nucleation sites and to explore structural neighbors of an intermediate, biologically active structure. The web server is available at http://bioinformatics.bc.edu/clotelab/RNAbor.

| S-EPMC1933207 | biostudies-literature

DIAL: a web-based server for the automatic identification of structural domains in proteins.

Project description:DIAL is a web server for the automatic identification of structural domains given the 3D coordinates of a protein. Delineation of the structural domains and their exact boundaries are the starting points for the better realization of distantly related members of the domain families, for the rational design of the experiments and for clearer understanding of the biological function. The current server can examine crystallographic multiple chains and provide structural domain solutions that can also describe domain swapping events. The server can be accessed from http://www.ncbs.res.in/~faculty/mini/DIAL/home.html. The Supplementary data can be accessed from http://www.ncbs.res.in/~faculty/mini/DIAL/supplement.html.

| S-EPMC1160188 | biostudies-literature

Identifying structural domains of proteins using clustering.

Project description:BackgroundProtein structures are comprised of modular elements known as domains. These units are used and re-used over and over in nature, and usually serve some particular function in the structure. Thus it is useful to be able to break up a protein of interest into its component domains, prior to similarity searching for example. Numerous computational methods exist for doing so, but most operate only on a single protein chain and many are limited to making a series of cuts to the sequence, while domains can and do span multiple chains.ResultsThis study presents a novel clustering-based approach to domain identification, which works equally well on individual chains or entire complexes. The method is simple and fast, taking only a few milliseconds to run, and works by clustering either vectors representing secondary structure elements, or buried alpha-carbon positions, using average-linkage clustering. Each resulting cluster corresponds to a domain of the structure. The method is competitive with others, achieving 70% agreement with SCOP on a large non-redundant data set, and 80% on a set more heavily weighted in multi-domain proteins on which both SCOP and CATH agree.ConclusionsIt is encouraging that a basic method such as this performs nearly as well or better than some far more complex approaches. This suggests that protein domains are indeed for the most part simply compact regions of structure with a higher density of buried contacts within themselves than between each other. By representing the structure as a set of points or vectors in space, it allows us to break free of any artificial limitations that other approaches may depend upon.

| S-EPMC3534501 | biostudies-literature

fastSCOP: a fast web server for recognizing protein structural domains and SCOP superfamilies.

Project description:The fastSCOP is a web server that rapidly identifies the structural domains and determines the evolutionary superfamilies of a query protein structure. This server uses 3D-BLAST to scan quickly a large structural classification database (SCOP1.71 with <95% identity with each other) and the top 10 hit domains, which have different superfamily classifications, are obtained from the hit lists. MAMMOTH, a detailed structural alignment tool, is adopted to align these top 10 structures to refine domain boundaries and to identify evolutionary superfamilies. Our previous works demonstrated that 3D-BLAST is as fast as BLAST, and has the characteristics of BLAST (e.g. a robust statistical basis, effective search and reliable database search capabilities) in large structural database searches based on a structural alphabet database and a structural alphabet substitution matrix. The classification accuracy of this server is approximately 98% for 586 query structures and the average execution time is approximately 5. This server was also evaluated on 8700 structures, which have no annotations in the SCOP; the server can automatically assign 7311 (84%) proteins (9420 domains) to the SCOP superfamilies in 9.6 h. These results suggest that the fastSCOP is robust and can be a useful server for recognizing the evolutionary classifications and the protein functions of novel structures. The server is accessible at http://fastSCOP.life.nctu.edu.tw.

| S-EPMC1933144 | biostudies-literature

PSSweb: protein structural statistics web server.

Project description:With the increasing number of protein structures available, there is a need for tools capable of automating the comparison of ensembles of structures, a common requirement in structural biology and bioinformatics. PSSweb is a web server for protein structural statistics. It takes as input an ensemble of PDB files of protein structures, performs a multiple sequence alignment and computes structural statistics for each position of the alignment. Different optional functionalities are proposed: structure superposition, Cartesian coordinate statistics, dihedral angle calculation and statistics, and a cluster analysis based on dihedral angles. An interactive report is generated, containing a summary of the results, tables, figures and 3D visualization of superposed structures. The server is available at http://pssweb.org.

| S-EPMC4987900 | biostudies-literature

HorA web server to infer homology between proteins using sequence and structural similarity.

Project description:The biological properties of proteins are often gleaned through comparative analysis of evolutionary relatives. Although protein structure similarity search methods detect more distant homologs than purely sequence-based methods, structural resemblance can result from either homology (common ancestry) or analogy (similarity without common ancestry). While many existing web servers detect structural neighbors, they do not explicitly address the question of homology versus analogy. Here, we present a web server named HorA (Homology or Analogy) that identifies likely homologs for a query protein structure. Unlike other servers, HorA combines sequence information from state-of-the-art profile methods with structure information from spatial similarity measures using an advanced computational technique. HorA aims to identify biologically meaningful connections rather than purely 3D-geometric similarities. The HorA method finds approximately 90% of remote homologs defined in the manually curated database SCOP. HorA will be especially useful for finding remote homologs that might be overlooked by other sequence or structural similarity search servers. The HorA server is available at http://prodata.swmed.edu/horaserver.

| S-EPMC2703895 | biostudies-literature

PhANNs, a fast and accurate tool and web server to classify phage structural proteins.

Project description:For any given bacteriophage genome or phage-derived sequences in metagenomic data sets, we are unable to assign a function to 50-90% of genes, or more. Structural protein-encoding genes constitute a large fraction of the average phage genome and are among the most divergent and difficult-to-identify genes using homology-based methods. To understand the functions encoded by phages, their contributions to their environments, and to help gauge their utility as potential phage therapy agents, we have developed a new approach to classify phage ORFs into ten major classes of structural proteins or into an "other" category. The resulting tool is named PhANNs (Phage Artificial Neural Networks). We built a database of 538,213 manually curated phage protein sequences that we split into eleven subsets (10 for cross-validation, one for testing) using a novel clustering method that ensures there are no homologous proteins between sets yet maintains the maximum sequence diversity for training. An Artificial Neural Network ensemble trained on features extracted from those sets reached a test F1-score of 0.875 and test accuracy of 86.2%. PhANNs can rapidly classify proteins into one of the ten structural classes or, if not predicted to fall in one of the ten classes, as "other," providing a new approach for functional annotation of phage proteins. PhANNs is open source and can be run from our web server or installed locally.

| S-EPMC7660903 | biostudies-literature

FRASS: the web-server for RNA structural comparison.

Project description:BACKGROUND: The impressive increase of novel RNA structures, during the past few years, demands automated methods for structure comparison. While many algorithms handle only small motifs, few techniques, developed in recent years, (ARTS, DIAL, SARA, SARSA, and LaJolla) are available for the structural comparison of large and intact RNA molecules. RESULTS: The FRASS web-server represents a RNA chain with its Gauss integrals and allows one to compare structures of RNA chains and to find similar entries in a database derived from the Protein Data Bank. We observed that FRASS scores correlate well with the ARTS and LaJolla similarity scores. Moreover, the-web server can also reproduce satisfactorily the DARTS classification of RNA 3D structures and the classification of the SCOR functions that was obtained by the SARA method. CONCLUSIONS: The FRASS web-server can be easily used to detect relationships among RNA molecules and to scan efficiently the rapidly enlarging structural databases.

| S-EPMC2902451 | biostudies-literature

Engineering Proteins for Thermostability with iRDP Web Server.

Project description:Engineering protein molecules with desired structure and biological functions has been an elusive goal. Development of industrially viable proteins with improved properties such as stability, catalytic activity and altered specificity by modifying the structure of an existing protein has widely been targeted through rational protein engineering. Although a range of factors contributing to thermal stability have been identified and widely researched, the in silico implementation of these as strategies directed towards enhancement of protein stability has not yet been explored extensively. A wide range of structural analysis tools is currently available for in silico protein engineering. However these tools concentrate on only a limited number of factors or individual protein structures, resulting in cumbersome and time-consuming analysis. The iRDP web server presented here provides a unified platform comprising of iCAPS, iStability and iMutants modules. Each module addresses different facets of effective rational engineering of proteins aiming towards enhanced stability. While iCAPS aids in selection of target protein based on factors contributing to structural stability, iStability uniquely offers in silico implementation of known thermostabilization strategies in proteins for identification and stability prediction of potential stabilizing mutation sites. iMutants aims to assess mutants based on changes in local interaction network and degree of residue conservation at the mutation sites. Each module was validated using an extensively diverse dataset. The server is freely accessible at http://irdp.ncl.res.in and has no login requirements.

| S-EPMC4593602 | biostudies-literature

GASS-WEB: a web server for identifying enzyme active sites based on genetic algorithms.

Project description:Enzyme active sites are important and conserved functional regions of proteins whose identification can be an invaluable step toward protein function prediction. Most of the existing methods for this task are based on active site similarity and present limitations including performing only exact matches on template residues, template size restraints, despite not being capable of finding inter-domain active sites. To fill this gap, we proposed GASS-WEB, a user-friendly web server that uses GASS (Genetic Active Site Search), a method based on an evolutionary algorithm to search for similar active sites in proteins. GASS-WEB can be used under two different scenarios: (i) given a protein of interest, to match a set of specific active site templates; or (ii) given an active site template, looking for it in a database of protein structures. The method has shown to be very effective on a range of experiments and was able to correctly identify >90% of the catalogued active sites from the Catalytic Site Atlas. It also managed to achieve a Matthew correlation coefficient of 0.63 using the Critical Assessment of protein Structure Prediction (CASP 10) dataset. In our analysis, GASS was ranking fourth among 18 methods. GASS-WEB is freely available at http://gass.unifei.edu.br/.

| S-EPMC5570142 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data