61
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment

      research-article
      1 , 1 , 2 , *
      Bioinformatics
      Oxford University Press

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Motivation: Exploitation of locally similar 3D patterns of physicochemical properties on the surface of a protein for detection of binding sites that may lack sequence and global structural conservation.

          Results: An algorithm, ProBiS is described that detects structurally similar sites on protein surfaces by local surface structure alignment. It compares the query protein to members of a database of protein 3D structures and detects with sub-residue precision, structurally similar sites as patterns of physicochemical properties on the protein surface. Using an efficient maximum clique algorithm, the program identifies proteins that share local structural similarities with the query protein and generates structure-based alignments of these proteins with the query. Structural similarity scores are calculated for the query protein's surface residues, and are expressed as different colors on the query protein surface. The algorithm has been used successfully for the detection of protein–protein, protein–small ligand and protein–DNA binding sites.

          Availability: The software is available, as a web tool, free of charge for academic users at http://probis.cmm.ki.si

          Contact: dusa@ 123456cmm.ki.si

          Supplementary information: Supplementary data are available at Bioinformatics online.

          Related collections

          Most cited references30

          • Record: found
          • Abstract: found
          • Article: not found

          Amino acid substitution matrices from protein blocks.

          Methods for alignment of protein sequences typically measure similarity by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites.

            Identifying the location of ligand binding sites on a protein is of fundamental importance for a range of applications including molecular docking, de novo drug design and structural identification and comparison of functional sites. Here, we describe a new method of ligand binding site prediction called Q-SiteFinder. It uses the interaction energy between the protein and a simple van der Waals probe to locate energetically favourable binding sites. Energetically favourable probe sites are clustered according to their spatial proximity and clusters are then ranked according to the sum of interaction energies for sites within each cluster. There is at least one successful prediction in the top three predicted sites in 90% of proteins tested when using Q-SiteFinder. This success rate is higher than that of a commonly used pocket detection algorithm (Pocket-Finder) which uses geometric criteria. Additionally, Q-SiteFinder is twice as effective as Pocket-Finder in generating predicted sites that map accurately onto ligand coordinates. It also generates predicted sites with the lowest average volumes of the methods examined in this study. Unlike pocket detection, the volumes of the predicted sites appear to show relatively low dependence on protein volume and are similar in volume to the ligands they contain. Restricting the size of the pocket is important for reducing the search space required for docking and de novo drug design or site comparison. The method can be applied in structural genomics studies where protein binding sites remain uncharacterized since the 86% success rate for unbound proteins appears to be only slightly lower than that of ligand-bound proteins. Both Q-SiteFinder and Pocket-Finder have been made available online at http://www.bioinformatics.leeds.ac.uk/qsitefinder and http://www.bioinformatics.leeds.ac.uk/pocketfinder
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              ConSurf: identification of functional regions in proteins by surface-mapping of phylogenetic information.

              We recently developed algorithmic tools for the identification of functionally important regions in proteins of known three dimensional structure by estimating the degree of conservation of the amino-acid sites among their close sequence homologues. Projecting the conservation grades onto the molecular surface of these proteins reveals patches of highly conserved (or occasionally highly variable) residues that are often of important biological function. We present a new web server, ConSurf, which automates these algorithmic tools. ConSurf may be used for high-throughput characterization of functional regions in proteins. The ConSurf web server is available at:http://consurf.tau.ac.il. A set of examples is available at http://consurf.tau.ac.il under 'GALLERY'.
                Bookmark

                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                1 May 2010
                19 March 2010
                19 March 2010
                : 26
                : 9
                : 1160-1168
                Affiliations
                1 National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana and 2 University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies, Glagoljaška 8, 6000 Koper, Slovenia
                Author notes
                * To whom correspondence should be addressed.

                Associate Editor: Anna Tramontano

                Article
                btq100
                10.1093/bioinformatics/btq100
                2859123
                20305268
                67d6d70c-6c38-463e-ac46-9a3581078086
                © The Author(s) 2010. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 2 December 2009
                : 7 February 2010
                : 27 February 2010
                Categories
                Original Papers
                Structural Bioinformatics

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

                Comments

                Comment on this article