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      PredictProtein—an open resource for online prediction of protein structural and functional features

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          Abstract

          PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org.

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          Most cited references47

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          Gene Ontology: tool for the unification of biology

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            The PredictProtein server.

            PredictProtein (http://www.predictprotein.org) is an Internet service for sequence analysis and the prediction of protein structure and function. Users submit protein sequences or alignments; PredictProtein returns multiple sequence alignments, PROSITE sequence motifs, low-complexity regions (SEG), nuclear localization signals, regions lacking regular structure (NORS) and predictions of secondary structure, solvent accessibility, globular regions, transmembrane helices, coiled-coil regions, structural switch regions, disulfide-bonds, sub-cellular localization and functional annotations. Upon request fold recognition by prediction-based threading, CHOP domain assignments, predictions of transmembrane strands and inter-residue contacts are also available. For all services, users can submit their query either by electronic mail or interactively via the World Wide Web.
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              OPM: orientations of proteins in membranes database.

              The Orientations of Proteins in Membranes (OPM) database provides a collection of transmembrane, monotopic and peripheral proteins from the Protein Data Bank whose spatial arrangements in the lipid bilayer have been calculated theoretically and compared with experimental data. The database allows analysis, sorting and searching of membrane proteins based on their structural classification, species, destination membrane, numbers of transmembrane segments and subunits, numbers of secondary structures and the calculated hydrophobic thickness or tilt angle with respect to the bilayer normal. All coordinate files with the calculated membrane boundaries are available for downloading. http://opm.phar.umich.edu.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                01 July 2014
                05 May 2014
                05 May 2014
                : 42
                : Web Server issue
                : W337-W343
                Affiliations
                [1 ]Department of Informatics, Bioinformatics & Computational Biology i12, TUM (Technische Universität München), Garching/Munich 85748, Germany
                [2 ]Biosof LLC, New York, NY 10001, USA
                [3 ]TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), TUM (Technische Universität München), Garching/Munich 85748, Germany
                [4 ]New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, New York, NY 10032, USA
                [5 ]Department of Genome Oriented Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, Freising 85354, Germany
                [6 ]The Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel
                [7 ]Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK
                [8 ]European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridgeshire, CB10 1SD, UK
                [9 ]Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
                [10 ]Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ 08901, USA
                [11 ]Luxembourg University & Luxembourg Centre for Systems Biomedicine, 4362 Belval, Luxembourg
                [12 ]CMBI, NCMLS, Radboudumc Nijmegen Medical Centre, 6525 GA Nijmegen, The Netherlands
                [13 ]Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, 10065 NY, USA
                [14 ]The Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, 69978 Tel Aviv, Israel
                [15 ]Department of Biochemistry and Molecular Biophysics & New York Consortium on Membrane Protein Structure (NYCOMPS), Columbia University, New York, NY 10032, USA
                [16 ]Institute for Advanced Study (TUM-IAS), Garching/Munich 85748, Germany
                [17 ]Institute for Food and Plant Sciences WZW-Weihenstephan, Alte Akademie 8, Freising 85350, Germany
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +49 (89) 289-17811; Fax: +49 (89) 289-19414; Email: gyachdav@ 123456rostlab.org
                Article
                10.1093/nar/gku366
                4086098
                24799431
                445d2f12-656f-4cbe-b73b-8f7d29e8787a
                © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

                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.

                Page count
                Pages: 7
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                1 July 2014

                Genetics
                Genetics

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