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      An empirical framework for binary interactome mapping

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      Nature methods

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          Several attempts have been made at systematically mapping protein-protein interaction, or “interactome” networks. However, it remains difficult to assess the quality and coverage of existing datasets. We describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human are superior in precision to literature-curated interactions supported by only a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains ~130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the human genome project, estimates of protein interaction data quality and interactome size are critical to establish the magnitude of the task of comprehensive human interactome mapping and to illuminate a path towards this goal.

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          Most cited references 30

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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: 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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            Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae.

            Defining protein complexes is critical to virtually all aspects of cell biology. Two recent affinity purification/mass spectrometry studies in Saccharomyces cerevisiae have vastly increased the available protein interaction data. The practical utility of such high throughput interaction sets, however, is substantially decreased by the presence of false positives. Here we created a novel probabilistic metric that takes advantage of the high density of these data, including both the presence and absence of individual associations, to provide a measure of the relative confidence of each potential protein-protein interaction. This analysis largely overcomes the noise inherent in high throughput immunoprecipitation experiments. For example, of the 12,122 binary interactions in the general repository of interaction data (BioGRID) derived from these two studies, we marked 7504 as being of substantially lower confidence. Additionally, applying our metric and a stringent cutoff we identified a set of 9074 interactions (including 4456 that were not among the 12,122 interactions) with accuracy comparable to that of conventional small scale methodologies. Finally we organized proteins into coherent multisubunit complexes using hierarchical clustering. This work thus provides a highly accurate physical interaction map of yeast in a format that is readily accessible to the biological community.
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              Large-scale mapping of human protein–protein interactions by mass spectrometry

              Mapping protein–protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein–protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24 540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein–protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations.

                Author and article information

                Nat Methods
                Nature methods
                21 April 2010
                7 December 2008
                January 2009
                18 May 2010
                : 6
                : 1
                : 83-90
                [1 ] Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, 1 Jimmy Fund Way, Boston, Massachusetts 02115, USA
                [2 ] Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA
                [3 ] Center for Complex Network Research and Department of Physics, University of Notre Dame, 225 Nieuwland Science Hall, Notre Dame, Indiana 46556, USA
                [4 ] The Simons Center for Systems Biology, Institute for Advanced Study, Einstein Drive, Princeton, New Jersey 08540, USA
                [5 ] Max Delbrück Center for Molecular Medicine (MDC), Robert-Roessle-Straße 10, D-13125 Berlin, Germany
                [6 ] Otto-Warburg Laboratory, Max Planck Institute for Molecular Genetics (MPI-MG), Ihnestraße 63-73, D-14195 Berlin, Germany
                [7 ] Department of Medical Protein Research, VIB, and Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Albert Baertsoenkaai 3, 9000 Ghent, Belgium
                [8 ] Banting and Best Department of Medical Research and Department of Molecular Genetics, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario M5S 3E1, Canada
                [9 ] Department of Physics, Korea University, 1 Anam-dong 5-ga, Seongbuk-gu, Seoul 136-713, Korea
                [10 ] School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Cambridge, Massachusetts 02138, USA
                [11 ] Department of Biochemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Avenue, Boston, Massachusetts 02115, USA
                Author notes
                Correspondence and requests for materials should be addressed to M.V. ( marc_vidal@ ), A.-L.B. ( a.barabasi@ ), E.E.W ( ewanker@ ), or J.T. ( jan.tavernier@ )

                Present addresses: Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139, USA (K.V.); Department of Cell Biology, Harvard Medical School, Boston, 240 Longwood Avenue, Massachusetts 02115, USA (J.-F.R.); Centre for Cancer Therapeutics, The Institute of Cancer Research, 15 Cotswold Road, Sutton, SM2 5NG, UK (K.H.); University College Dublin, School of Biomolecular and Biomedical Science, Belfield, Dublin 4, Ireland (S.C.); Genome Exploration Research Group, RIKEN Genomic Sciences Center, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan (C.S.); Bioinformatics Program, Boston University, 24 Cummington Street, Boston, Massachusetts 02215, USA (N.K.); Department of Biological Sciences, Clemson University, 132 Long Hall, Clemson, South Carolina 29634, USA (H.B.); Protein Chemistry/Proteomics/Peptide Synthesis and Array Unit, Biomedicum Helsinki, University of Helsinki, Haartmaninkatu 8, FI-00014 Helsinki, Finland (M.L.); Center for Complex Network Research and Departments of Physics, Biology and Computer Sciences, Northeastern University, 360 Huntington Avenue, Boston, Massachusetts 02115, USA (A.-L.B.).


                These authors contributed equally to this work.

                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: R01 HG001715-12 ||HG
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: P50 HG004233-03 ||HG

                Life sciences


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