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      PTMD: A Database of Human Disease-associated Post-translational Modifications

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          Abstract

          Various posttranslational modifications (PTMs) participate in nearly all aspects of biological processes by regulating protein functions, and aberrant states of PTMs are frequently implicated in human diseases. Therefore, an integral resource of PTM–disease associations (PDAs) would be a great help for both academic research and clinical use. In this work, we reported PTMD, a well-curated database containing PTMs that are associated with human diseases. We manually collected 1950 known PDAs in 749 proteins for 23 types of PTMs and 275 types of diseases from the literature. Database analyses show that phosphorylation has the largest number of disease associations, whereas neurologic diseases have the largest number of PTM associations. We classified all known PDAs into six classes according to the PTM status in diseases and demonstrated that the upregulation and presence of PTM events account for a predominant proportion of disease-associated PTM events. By reconstructing a disease–gene network, we observed that breast cancers have the largest number of associated PTMs and AKT1 has the largest number of PTMs connected to diseases. Finally, the PTMD database was developed with detailed annotations and can be a useful resource for further analyzing the relations between PTMs and human diseases. PTMD is freely accessible at http://ptmd.biocuckoo.org.

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          Most cited references30

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          The protein kinase complement of the human genome.

          G. Manning (2002)
          We have catalogued the protein kinase complement of the human genome (the "kinome") using public and proprietary genomic, complementary DNA, and expressed sequence tag (EST) sequences. This provides a starting point for comprehensive analysis of protein phosphorylation in normal and disease states, as well as a detailed view of the current state of human genome analysis through a focus on one large gene family. We identify 518 putative protein kinase genes, of which 71 have not previously been reported or described as kinases, and we extend or correct the protein sequences of 56 more kinases. New genes include members of well-studied families as well as previously unidentified families, some of which are conserved in model organisms. Classification and comparison with model organism kinomes identified orthologous groups and highlighted expansions specific to human and other lineages. We also identified 106 protein kinase pseudogenes. Chromosomal mapping revealed several small clusters of kinase genes and revealed that 244 kinases map to disease loci or cancer amplicons.
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            Reorganizing the protein space at the Universal Protein Resource (UniProt)

            The mission of UniProt is to support biological research by providing a freely accessible, stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces. 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. A key development at UniProt is the provision of complete, reference and representative proteomes. UniProt is updated and distributed every 4 weeks and can be accessed online for searches or download at http://www.uniprot.org.
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              DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions.

              I Xenarios (2002)
              The Database of Interacting Proteins (DIP: http://dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein-protein interactions. It provides the scientific community with an integrated set of tools for browsing and extracting information about protein interaction networks. As of September 2001, the DIP catalogs approximately 11 000 unique interactions among 5900 proteins from >80 organisms; the vast majority from yeast, Helicobacter pylori and human. Tools have been developed that allow users to analyze, visualize and integrate their own experimental data with the information about protein-protein interactions available in the DIP database.
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                Author and article information

                Contributors
                Journal
                Genomics Proteomics Bioinformatics
                Genomics Proteomics Bioinformatics
                Genomics, Proteomics & Bioinformatics
                Elsevier
                1672-0229
                2210-3244
                21 September 2018
                August 2018
                21 September 2018
                : 16
                : 4
                : 244-251
                Affiliations
                [1 ]Department of Bioinformatics & Systems Biology, MOE Key Laboratory of Molecular Biophysics, College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
                [2 ]Department of Biomedical Informatics, School of Basic Medical Sciences, MOE Key Laboratory of Molecular Cardiovascular Sciences, Center for Non-coding RNA Medicine, Peking University, Beijing 100191, China
                Author notes
                [a]

                ORCID: 0000-0003-2086-3893.

                [b]

                ORCID: 0000-0002-3388-1663.

                [c]

                ORCID: 0000-0002-1177-5480.

                [d]

                ORCID: 0000-0002-5052-9151.

                [e]

                ORCID: 0000-0002-1249-3741.

                [f]

                ORCID: 0000-0003-3018-5221.

                [g]

                ORCID: 0000-0002-9403-6869.

                Article
                S1672-0229(18)30318-8
                10.1016/j.gpb.2018.06.004
                6205080
                30244175
                66ef2016-0896-4a75-b3d4-274c552e7d0b
                © 2018 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 12 March 2018
                : 4 June 2018
                : 25 June 2018
                Categories
                Database

                posttranslational modification,phosphorylation,ptm–disease association,disease–gene network,akt1

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