1. Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Genetics, Harvard Medical School, 44 Binney Street, Boston, Massachusetts 02115, USA
2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Ave, Boston, Massachusetts 02115, USA
3. Unité de Recherche en Biologie Moléculaire, Facultés Notre-Dame de la Paix, 61 Rue de Bruxelles, 5000 Namur, Belgium
4. Howard Hughes Medical Institute, and Departments of Pediatrics, Neurology, Neuroscience, and Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030, USA
5. Arcbay, Inc., 6 Whittier Place, Suite 7J, Boston, Massachusetts 01915, USA
6. Agencourt Bioscience Corporation, 500 Cummings Center, Suite 2450, Beverly, Massachusetts 01915, USA
7. †Present addresses: ArQule, Inc., 19 Presidential Way, Woburn, Massachusetts 01081, USA (S.L.); Departments of Cancer Biology, and Otolaryngology, Head and Neck Surgery, University of California Davis, 2521 Stockton Blvd, Suite 7200, Sacramento, California 95817, USA (J.S.A.)
8. *These authors contributed equally to this work

Correspondence to: David E. Hill¹Frederick P. Roth²Marc Vidal¹ Correspondence and requests for materials should be addressed to M.V. (Email: marc_vidal@dfci.harvard.edu), F.P.R. (Email: fritz_roth@hms.harvard.edu) or D.E.H. (Email: david_hill@dfci.harvard.edu).

Abstract

Systematic mapping of protein–protein interactions, or 'interactome' mapping, was initiated in model organisms, starting with defined biological processes^{1, 2} and then expanding to the scale of the proteome^{3, 4, 5, 6, 7}. Although far from complete, such maps have revealed global topological and dynamic features of interactome networks that relate to known biological properties^{8, 9}, suggesting that a human interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein–protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of 8,100 currently available Gateway-cloned open reading frames and detected 2,800 interactions. This data set, called CCSB-HI1, has a verification rate of 78% as revealed by an independent co-affinity purification assay, and correlates significantly with other biological attributes. The CCSB-HI1 data set increases by 70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. This work represents an important step towards a systematic and comprehensive human interactome project.

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