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      IsoCleft Finder – a web-based tool for the detection and analysis of protein binding-site geometric and chemical similarities

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          Abstract

          IsoCleft Finder is a web-based tool for the detection of local geometric and chemical similarities between potential small-molecule binding cavities and a non-redundant dataset of ligand-bound known small-molecule binding-sites. The non-redundant dataset developed as part of this study is composed of 7339 entries representing unique Pfam/PDB-ligand (hetero group code) combinations with known levels of cognate ligand similarity. The query cavity can be uploaded by the user or detected automatically by the system using existing PDB entries as well as user-provided structures in PDB format. In all cases, the user can refine the definition of the cavity interactively via a browser-based Jmol 3D molecular visualization interface. Furthermore, users can restrict the search to a subset of the dataset using a cognate-similarity threshold. Local structural similarities are detected using the IsoCleft software and ranked according to two criteria (number of atoms in common and Tanimoto score of local structural similarity) and the associated Z-score and p-value measures of statistical significance. The results, including predicted ligands, target proteins, similarity scores, number of atoms in common, etc., are shown in a powerful interactive graphical interface. This interface permits the visualization of target ligands superimposed on the query cavity and additionally provides a table of pairwise ligand topological similarities. Similarities between top scoring ligands serve as an additional tool to judge the quality of the results obtained. We present several examples where IsoCleft Finder provides useful functional information. IsoCleft Finder results are complementary to existing approaches for the prediction of protein function from structure, rational drug design and x-ray crystallography. IsoCleft Finder can be found at: http://bcb.med.usherbrooke.ca/isocleftfinder.

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          Most cited references24

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          The Pfam protein families database.

          Pfam is a large collection of protein families and domains. Over the past 2 years the number of families in Pfam has doubled and now stands at 6190 (version 10.0). Methodology improvements for searching the Pfam collection locally as well as via the web are described. Other recent innovations include modelling of discontinuous domains allowing Pfam domain definitions to be closer to those found in structure databases. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://Pfam.cgb.ki.se/).
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            The Universal Protein Resource (UniProt) in 2010

            The primary mission of UniProt is to support biological research by maintaining a stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces freely accessible to the scientific community. UniProt is produced by the UniProt Consortium which consists of groups from the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is comprised of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. UniProt is updated and distributed every 3 weeks and can be accessed online for searches or download at http://www.uniprot.org.
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              Least-squares fitting of two 3-d point sets.

              Two point sets {pi} and {p'i}; i = 1, 2,..., N are related by p'i = Rpi + T + Ni, where R is a rotation matrix, T a translation vector, and Ni a noise vector. Given {pi} and {p'i}, we present an algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 × 3 matrix. This new algorithm is compared to two earlier algorithms with respect to computer time requirements.
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                Author and article information

                Journal
                F1000Res
                F1000Res
                F1000Research
                F1000Research
                F1000Research (London, UK )
                2046-1402
                2 May 2013
                2013
                : 2
                : 117
                Affiliations
                [1 ]European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SD, UK
                [2 ]Department of Biochemistry, Université de Sherbrooke, Sherbrooke, J1H 5N4, Canada
                [1 ]Computational Chemical Biology, EMBL European Bioinformatics Institute, Hinxton, Cambridge, UK
                [1 ]Albert Einstein College of Medicine , Yeshiva University, Bronx, NY, USA
                [1 ]Mayo Clinic, Scottsdale, AZ, USA
                Author notes

                RN devised the work, developed the ICFDB dataset and created the search scripts. NK developed the web interface. MC tested the system. RN, NK, MC and MIZ wrote the manuscript.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Article
                10.12688/f1000research.2-117.v2
                3892921
                9f6cb578-6379-4882-8b46-5d8520eef01f
                Copyright: © 2013 Kurbatova N et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

                History
                : 30 April 2013
                Funding
                The author(s) declared that no grants were involved in supporting this work.
                Categories
                Web Tool
                Articles
                Biomacromolecule-Ligand Interactions
                Drug Discovery & Design
                Theory & Simulation

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