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      ELM—the database of eukaryotic linear motifs

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          Abstract

          Linear motifs are short, evolutionarily plastic components of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. Given their importance, our understanding of motifs is surprisingly limited, largely as a result of the difficulty of discovery, both experimentally and computationally. The Eukaryotic Linear Motif (ELM) resource at http://elm.eu.org provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences. The current update of the ELM database comprises 1800 annotated motif instances representing 170 distinct functional classes, including approximately 500 novel instances and 24 novel classes. Several older motif class entries have been also revisited, improving annotation and adding novel instances. Furthermore, addition of full-text search capabilities, an enhanced interface and simplified batch download has improved the overall accessibility of the ELM data. The motif discovery portion of the ELM resource has added conservation, and structural attributes have been incorporated to aid users to discriminate biologically relevant motifs from stochastically occurring non-functional instances.

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          SMART 6: recent updates and new developments

          Simple modular architecture research tool (SMART) is an online tool (http://smart.embl.de/) for the identification and annotation of protein domains. It provides a user-friendly platform for the exploration and comparative study of domain architectures in both proteins and genes. The current release of SMART contains manually curated models for 784 protein domains. Recent developments were focused on further data integration and improving user friendliness. The underlying protein database based on completely sequenced genomes was greatly expanded and now includes 630 species, compared to 191 in the previous release. As an initial step towards integrating information on biological pathways into SMART, our domain annotations were extended with data on metabolic pathways and links to several pathways resources. The interaction network view was completely redesigned and is now available for more than 2 million proteins. In addition to the standard web access to the database, users can now query SMART using distributed annotation system (DAS) or through a simple object access protocol (SOAP) based web service.
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            Phospho.ELM: a database of phosphorylation sites—update 2011

            The Phospho.ELM resource (http://phospho.elm.eu.org) is a relational database designed to store in vivo and in vitro phosphorylation data extracted from the scientific literature and phosphoproteomic analyses. The resource has been actively developed for more than 7 years and currently comprises 42 574 serine, threonine and tyrosine non-redundant phosphorylation sites. Several new features have been implemented, such as structural disorder/order and accessibility information and a conservation score. Additionally, the conservation of the phosphosites can now be visualized directly on the multiple sequence alignment used for the score calculation. Finally, special emphasis has been put on linking to external resources such as interaction networks and other databases.
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              The HUPO PSI's molecular interaction format--a community standard for the representation of protein interaction data.

              A major goal of proteomics is the complete description of the protein interaction network underlying cell physiology. A large number of small scale and, more recently, large-scale experiments have contributed to expanding our understanding of the nature of the interaction network. However, the necessary data integration across experiments is currently hampered by the fragmentation of publicly available protein interaction data, which exists in different formats in databases, on authors' websites or sometimes only in print publications. Here, we propose a community standard data model for the representation and exchange of protein interaction data. This data model has been jointly developed by members of the Proteomics Standards Initiative (PSI), a work group of the Human Proteome Organization (HUPO), and is supported by major protein interaction data providers, in particular the Biomolecular Interaction Network Database (BIND), Cellzome (Heidelberg, Germany), the Database of Interacting Proteins (DIP), Dana Farber Cancer Institute (Boston, MA, USA), the Human Protein Reference Database (HPRD), Hybrigenics (Paris, France), the European Bioinformatics Institute's (EMBL-EBI, Hinxton, UK) IntAct, the Molecular Interactions (MINT, Rome, Italy) database, the Protein-Protein Interaction Database (PPID, Edinburgh, UK) and the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, EMBL, Heidelberg, Germany).
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                Author and article information

                Journal
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                January 2012
                January 2012
                21 November 2011
                21 November 2011
                : 40
                : D1 , Database issue
                : D242-D251
                Affiliations
                1Structural and Computational Biology, European Molecular Biology Laboratory, Heidelberg, Germany, 2Laboratory of Bioinformatics and Systems Biology, M. Sklodowska-Curie Cancer Center and Institute of Oncology, WK Roentgena 5, 02-781 Warsaw, Poland, 3Genoscope (CEA – Institut de Génomique), 2 rue Gaston Cremieux CP5706, 91057 Evry, 4Group Oncoproteins, Unité CNRS-UDS UMR 7242, Institut de Recherche de l'Ecole de Biotechnologie de Strasbourg, 1, Bd Sébastien Brant, BP 10413, 67412 Illkirch – Cedex, France, 5Biocomputing Group, Department of Physics, Sapienza University of Rome, P.le Aldo Moro 5, Rome, Italy, 6School of Biological Sciences, University of Edinburgh, Mayfield Road, Edinburgh EH9 3JR, UK, 7School of Medicine and Medical Science, University College, Dublin, Ireland, 8Centre for Biological Sciences, Institute for Life Sciences, University of Southampton, UK, 9Biomolecular Research, Paul Scherrer Institut, CH-5232 Villigen PSI, Switzerland, 10Bioinformatik, Institut für Biochemie, Friedrich-Alexander-Universität, Fahrstraße 17, 91054 Erlangen-Nürnberg and 11Molecular Health GmbH Belfortstr. 2, 69115 Heidelberg, Germany
                Author notes
                *To whom correspondence should be addressed. Tel: +49 (0) 6221 3878398; Fax: +49 (0) 6221 387517; Email: gibson@ 123456embl-heidelberg.de
                Article
                gkr1064
                10.1093/nar/gkr1064
                3245074
                22110040
                0aba70d5-f984-405d-a25b-af7fad0be92a
                © The Author(s) 2011. 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/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 September 2011
                : 27 October 2011
                : 27 October 2011
                Page count
                Pages: 10
                Categories
                Articles

                Genetics
                Genetics

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