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      The BioGRID interaction database: 2017 update

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          Abstract

          The Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org) is an open access database dedicated to the annotation and archival of protein, genetic and chemical interactions for all major model organism species and humans. As of September 2016 (build 3.4.140), the BioGRID contains 1 072 173 genetic and protein interactions, and 38 559 post-translational modifications, as manually annotated from 48 114 publications. This dataset represents interaction records for 66 model organisms and represents a 30% increase compared to the previous 2015 BioGRID update. BioGRID curates the biomedical literature for major model organism species, including humans, with a recent emphasis on central biological processes and specific human diseases. To facilitate network-based approaches to drug discovery, BioGRID now incorporates 27 501 chemical–protein interactions for human drug targets, as drawn from the DrugBank database. A new dynamic interaction network viewer allows the easy navigation and filtering of all genetic and protein interaction data, as well as for bioactive compounds and their established targets. BioGRID data are directly downloadable without restriction in a variety of standardized formats and are freely distributed through partner model organism databases and meta-databases.

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          Most cited references 52

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          A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae.

          Two large-scale yeast two-hybrid screens were undertaken to identify protein-protein interactions between full-length open reading frames predicted from the Saccharomyces cerevisiae genome sequence. In one approach, we constructed a protein array of about 6,000 yeast transformants, with each transformant expressing one of the open reading frames as a fusion to an activation domain. This array was screened by a simple and automated procedure for 192 yeast proteins, with positive responses identified by their positions in the array. In a second approach, we pooled cells expressing one of about 6,000 activation domain fusions to generate a library. We used a high-throughput screening procedure to screen nearly all of the 6,000 predicted yeast proteins, expressed as Gal4 DNA-binding domain fusion proteins, against the library, and characterized positives by sequence analysis. These approaches resulted in the detection of 957 putative interactions involving 1,004 S. cerevisiae proteins. These data reveal interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes. The results of these screens are shown here.
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            The role of Atg proteins in autophagosome formation.

            Macroautophagy is mediated by a unique organelle, the autophagosome, which encloses a portion of cytoplasm for delivery to the lysosome. Autophagosome formation is dynamically regulated by starvation and other stresses and involves complicated membrane reorganization. Since the discovery of yeast Atg-related proteins, autophagosome formation has been dissected at the molecular level. In this review we describe the molecular mechanism of autophagosome formation with particular focus on the function of Atg proteins and the long-standing discussion regarding the origin of the autophagosome membrane.
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              Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

              The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.
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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
                04 January 2017
                14 December 2016
                14 December 2016
                : 45
                : Database issue , Database issue
                : D369-D379
                Affiliations
                [1 ]Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Quebec H3T 1J4, Canada
                [2 ]Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
                [3 ]The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
                [4 ]Centre Hospitalier de l'Université Laval (CHUL), Québec, Québec G1V 4G2, Canada
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +1 514 343 6668; Fax: +1 514 343 5839; Email: md.tyers@ 123456umontreal.ca
                Article
                10.1093/nar/gkw1102
                5210573
                27980099
                © The Author(s) 2016. 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-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                Page count
                Pages: 11
                Product
                Categories
                Database Issue
                Custom metadata
                04 January 2017

                Genetics

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