45
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes

      research-article

      Read this article at

          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

          Macromolecular protein complexes carry out many of the essential functions of cells, and many genetic diseases arise from disrupting the functions of such complexes. Currently, there is great interest in defining the complete set of human protein complexes, but recent published maps lack comprehensive coverage. Here, through the synthesis of over 9,000 published mass spectrometry experiments, we present hu. MAP, the most comprehensive and accurate human protein complex map to date, containing > 4,600 total complexes, > 7,700 proteins, and > 56,000 unique interactions, including thousands of confident protein interactions not identified by the original publications. hu. MAP accurately recapitulates known complexes withheld from the learning procedure, which was optimized with the aid of a new quantitative metric ( k‐cliques) for comparing sets of sets. The vast majority of complexes in our map are significantly enriched with literature annotations, and the map overall shows improved coverage of many disease‐associated proteins, as we describe in detail for ciliopathies. Using hu. MAP, we predicted and experimentally validated candidate ciliopathy disease genes in vivo in a model vertebrate, discovering CCDC138, WDR90, and KIAA1328 to be new cilia basal body/centriolar satellite proteins, and identifying ANKRD55 as a novel member of the intraflagellar transport machinery. By offering significant improvements to the accuracy and coverage of human protein complexes, hu. MAP ( http://proteincomplexes.org) serves as a valuable resource for better understanding the core cellular functions of human proteins and helping to determine mechanistic foundations of human disease.

          Related collections

          Most cited references43

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

          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            LIBSVM: A library for support vector machines

            LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Detecting overlapping protein complexes in protein-protein interaction networks.

              We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.
                Bookmark

                Author and article information

                Contributors
                marcotte@icmb.utexas.edu
                Journal
                Mol Syst Biol
                Mol. Syst. Biol
                10.1002/(ISSN)1744-4292
                MSB
                msb
                Molecular Systems Biology
                John Wiley and Sons Inc. (Hoboken )
                1744-4292
                09 June 2017
                June 2017
                : 13
                : 6 ( doiID: 10.1002/msb.v13.6 )
                : 932
                Affiliations
                [ 1 ] Center for Systems and Synthetic Biology Institute for Cellular and Molecular BiologyUniversity of Texas at Austin Austin TXUSA
                [ 2 ] Department of Molecular BiosciencesUniversity of Texas at Austin Austin TXUSA
                [ 3 ] The Otolaryngology HospitalThe First Affiliated Hospital of Sun Yat‐sen University Sun Yat‐sen University GuangzhouChina
                [ 4 ]Present address: Recursion Pharmaceuticals Inc. Salt Lake City UTUSA
                Author notes
                [*] [* ]Corresponding author. Tel: +1 512 471 5435; E‐mail: marcotte@ 123456icmb.utexas.edu
                Author information
                http://orcid.org/0000-0002-1260-4413
                http://orcid.org/0000-0001-8808-180X
                Article
                MSB167490
                10.15252/msb.20167490
                5488662
                28596423
                fff847c4-d173-4014-ac1c-3e0e63d41501
                © 2017 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 07 December 2016
                : 02 May 2017
                : 12 May 2017
                Page count
                Figures: 12, Tables: 0, Pages: 21, Words: 16030
                Funding
                Funded by: Welch Foundation
                Award ID: F‐1515
                Funded by: HHS | National Institutes of Health (NIH)
                Award ID: F32 GM112495
                Award ID: 1R01 HL117164
                Award ID: R21 GM119021
                Award ID: R01 HD085901
                Award ID: DP1 GM106408
                Award ID: R01 DK110520
                Award ID: R35 GM122480
                Funded by: National Science Foundation (NSF)
                Award ID: # 1237975
                Funded by: Cancer Prevention and Research Institute of Texas (CPRIT)
                Categories
                Article
                Articles
                Custom metadata
                2.0
                msb167490
                June 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.1.2 mode:remove_FC converted:28.06.2017

                Quantitative & Systems biology
                cilia,ciliopathy,human interactome,mass spectrometry,protein complexes,proteomics,genome-scale & integrative biology,network biology,post-translational modifications, proteolysis & proteomics

                Comments

                Comment on this article