Dataset Information

Firestar--prediction of functionally important residues using structural templates and alignment reliability.

ABSTRACT: Here we present firestar, an expert system for predicting ligand-binding residues in protein structures. The server provides a method for extrapolating from the large inventory of functionally important residues organized in the FireDB database and adds information about the local conservation of potential-binding residues. The interface allows users to make queries by protein sequence or structure. The user can access pairwise and multiple alignments with structures that have relevant functionally important binding sites. The results are presented in a series of easy to read displays that allow users to compare binding residue conservation across homologous proteins. The binding site residues can also be viewed with molecular visualization tools. One feature of firestar is that it can be used to evaluate the biological relevance of small molecule ligands present in PDB structures. With the server it is easy to discern whether small molecule binding is conserved in homologous structures. We found this facility particularly useful during the recent assessment of CASP7 function prediction.http://firedb.bioinfo.cnio.es/Php/FireStar.php.

SUBMITTER: Lopez G

PROVIDER: S-EPMC1933227 | biostudies-literature | 2007 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

firestar--prediction of functionally important residues using structural templates and alignment reliability.

López Gonzalo G Valencia Alfonso A Tress Michael L ML

Nucleic acids research 20070621 Web Server issue

<h4>Unlabelled</h4>Here we present firestar, an expert system for predicting ligand-binding residues in protein structures. The server provides a method for extrapolating from the large inventory of functionally important residues organized in the FireDB database and adds information about the local conservation of potential-binding residues. The interface allows users to make queries by protein sequence or structure. The user can access pairwise and multiple alignments with structures that have ...[more]

PMID: 17584799

Similar Datasets

Project description:BackgroundWell-performing automated protein function recognition approaches usually comprise several complementary techniques. Beside constructing better consensus, their predictive power can be improved by either adding or refining independent modules that explore orthogonal features of proteins. In this work, we demonstrated how the exploration of global atomic distributions can be used to indicate functionally important residues.ResultsUsing a set of carefully selected globular proteins, we parametrized continuous probability density functions describing preferred central distances of individual protein atoms. Relative preferred burials were estimated using mixture models of radial density functions dependent on the amino acid composition of a protein under consideration. The unexpectedness of extraordinary locations of atoms was evaluated in the information-theoretic manner and used directly for the identification of key amino acids. In the validation study, we tested capabilities of a tool built upon our approach, called SurpResi, by searching for binding sites interacting with ligands. The tool indicated multiple candidate sites achieving success rates comparable to several geometric methods. We also showed that the unexpectedness is a property of regions involved in protein-protein interactions, and thus can be used for the ranking of protein docking predictions. The computational approach implemented in this work is freely available via a Web interface at http://www.bioinformatics.org/surpresi.ConclusionsProbabilistic analysis of atomic central distances in globular proteins is capable of capturing distinct orientational preferences of amino acids as resulting from different sizes, charges and hydrophobic characters of their side chains. When idealized spatial preferences can be inferred from the sole amino acid composition of a protein, residues located in hydrophobically unfavorable environments can be easily detected. Such residues turn out to be often directly involved in binding ligands or interfacing with other proteins.

Dataset Information

Firestar--prediction of functionally important residues using structural templates and alignment reliability.

Publications

firestar--prediction of functionally important residues using structural templates and alignment reliability.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets