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      • Record: found
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      ProBiS-ligands: a web server for prediction of ligands by examination of protein binding sites

      research-article
      Author(s): 1 , * , 2 , *
      Publication date (Electronic):
      Journal: Nucleic Acids Research
      Publisher: Oxford University Press

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          Abstract

          The ProBiS-ligands web server predicts binding of ligands to a protein structure. Starting with a protein structure or binding site, ProBiS-ligands first identifies template proteins in the Protein Data Bank that share similar binding sites. Based on the superimpositions of the query protein and the similar binding sites found, the server then transposes the ligand structures from those sites to the query protein. Such ligand prediction supports many activities, e.g. drug repurposing. The ProBiS-ligands web server, an extension of the ProBiS web server, is open and free to all users at http://probis.cmm.ki.si/ligands.

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          Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment.

          Jianyi Yang, Ambrish Roy, Yang Zhang (2013)
          Identification of protein-ligand binding sites is critical to protein function annotation and drug discovery. However, there is no method that could generate optimal binding site prediction for different protein types. Combination of complementary predictions is probably the most reliable solution to the problem. We develop two new methods, one based on binding-specific substructure comparison (TM-SITE) and another on sequence profile alignment (S-SITE), for complementary binding site predictions. The methods are tested on a set of 500 non-redundant proteins harboring 814 natural, drug-like and metal ion molecules. Starting from low-resolution protein structure predictions, the methods successfully recognize >51% of binding residues with average Matthews correlation coefficient (MCC) significantly higher (with P-value <10(-9) in student t-test) than other state-of-the-art methods, including COFACTOR, FINDSITE and ConCavity. When combining TM-SITE and S-SITE with other structure-based programs, a consensus approach (COACH) can increase MCC by 15% over the best individual predictions. COACH was examined in the recent community-wide COMEO experiment and consistently ranked as the best method in last 22 individual datasets with the Area Under the Curve score 22.5% higher than the second best method. These data demonstrate a new robust approach to protein-ligand binding site recognition, which is ready for genome-wide structure-based function annotations. http://zhanglab.ccmb.med.umich.edu/COACH/
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            COFACTOR: an accurate comparative algorithm for structure-based protein function annotation

            Ambrish Roy, Jianyi Yang, Yang Zhang (2012)
            We have developed a new COFACTOR webserver for automated structure-based protein function annotation. Starting from a structural model, given by either experimental determination or computational modeling, COFACTOR first identifies template proteins of similar folds and functional sites by threading the target structure through three representative template libraries that have known protein–ligand binding interactions, Enzyme Commission number or Gene Ontology terms. The biological function insights in these three aspects are then deduced from the functional templates, the confidence of which is evaluated by a scoring function that combines both global and local structural similarities. The algorithm has been extensively benchmarked by large-scale benchmarking tests and demonstrated significant advantages compared to traditional sequence-based methods. In the recent community-wide CASP9 experiment, COFACTOR was ranked as the best method for protein–ligand binding site predictions. The COFACTOR sever and the template libraries are freely available at http://zhanglab.ccmb.med.umich.edu/COFACTOR.
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              JSmol and the Next-Generation Web-Based Representation of 3D Molecular Structure as Applied toProteopedia

              Robert Hanson, Jaime Prilusky, Zhou Renjian … (2013)
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (hwp): nar
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): 01 July 2014
                Publication date (Electronic): 26 May 2014
                Publication date PMC-release: 26 May 2014
                Volume: 42
                Issue: Web Server issue
                Pages: W215-W220
                Affiliations
                [1 ]National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
                [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. Tel: +38614760273; Fax: +38614760300; Email: konc@ 123456cmm.ki.si
                Correspondence may also be addressed to Dušanka Janežič. Tel: +38656117659; Fax: +38656117571; Email: dusanka.janezic@ 123456upr.si
                Article
                DOI: 10.1093/nar/gku460
                PMC ID: 4086080
                PubMed ID: 24861616
                SO-VID: 8ae3d6f2-0ca2-4d56-882a-44fba5163b43
                Copyright © © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date accepted : 08 May 2014
                Date revision received : 08 May 2014
                Date received : 12 February 2014
                Page count
                Pages: 6
                Categories
                Subject: Article
                Custom metadata
                cover-date 1 July 2014

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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