Systematic mapping of protein–protein interactions, or ‘interactome’ mapping, was initiated in model organisms, starting with defined biological processes1,2 and then expanding to the scale of the proteome3,4,5,6,7. Although far from complete, such maps have revealed global topological and dynamic features of interactome networks that relate to known biological properties8,9, 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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This paper is dedicated to the memory of Stan Korsmeyer. We thank members of the Vidal laboratory and the participants of the ORFeome Meeting for discussions; the sequencing staff at Agencourt Biosciences for technical assistance; E. Smith for his help with the figures; C. McCowan, A. Bird, T. Clingingsmith and C. You for administrative assistance; and E. Benz, S. Korsmeyer, D. Livingston, P. McCue, J. Song, B. Rollins and the DFCI Strategic Planning Initiative for support. Our human interactome project is supported by the DFCI High-Tech Fund (S. Korsmeyer), an Ellison Foundation grant awarded to M.V., an NIH/NCI grant awarded to S. Korsmeyer, S. Orkin, G. Gilliland and M.V., an ‘interactome mapping’ grant from NIH/NHGRI and NIH/NIGMS awarded to F.P.R. and M.V., and a W.M. Keck Foundation grant awarded to E. Benz, J. Marto, F.P.R. and M.V. Other support includes Taplin Funds for Discovery (F.P.R., F.D.G. and G.F.B), a 2003 NSF Fellowship (D.S.G) and funding from the Fonds National de la Recherche Scientifique, Belgium (M.D.). Author Contributions Experiments and data analyses were coordinated by J.F.R., T.H. and K.V. High-throughput ORF cloning and yeast two-hybrid screens were performed by J.F.R., T.H.K., A.D., N.L., N.A.G., J.R. and J.L. J.F.R developed the high-throughput yeast two-hybrid strategy. Computational analyses were performed by T.H., K.V., G.F.B., F.D.G., N.K., P.L., D.S.G., L.V.Z., S.L.W. and G.F. Co-affinity purification experiments were performed by M.D., C.S., J.F.R., S.M., M.B., S.L. and J.S.A. C.F., E.L., S.C. and C.B. provided laboratory support. R.S.S., J.V., H.Y.Z., A.S. and M.E.C. helped with the overall interpretation of the data. DNA sequencing was performed by S.B., R.S. and L.D.S. The manuscript was written by J.F.R., K.V., M.E.C., D.E.H., F.P.R. and M.V. The project was conceived by M.V. and co-directed by D.E.H., F.P.R. and M.V.

Author information

Author notes

    • Siming Li
    •  & Joanna S. Albala

    †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.)

    • Jean-François Rual
    •  & Kavitha Venkatesan

    *These authors contributed equally to this work


  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

    • Jean-François Rual
    • , Kavitha Venkatesan
    • , Tong Hao
    • , Tomoko Hirozane-Kishikawa
    • , Amélie Dricot
    • , Ning Li
    • , Matija Dreze
    • , Nono Ayivi-Guedehoussou
    • , Niels Klitgord
    • , Christophe Simon
    • , Mike Boxem
    • , Stuart Milstein
    • , Jennifer Rosenberg
    • , Siming Li
    • , Joanna S. Albala
    • , Carlene Fraughton
    • , Estelle Llamosas
    • , Sebiha Cevik
    • , Camille Bex
    • , Philippe Lamesch
    • , Alex Smolyar
    • , Michael E. Cusick
    • , David E. Hill
    •  & Marc Vidal
  2. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 250 Longwood Ave, Boston, Massachusetts 02115, USA

    • Gabriel F. Berriz
    • , Francis D. Gibbons
    • , Debra S. Goldberg
    • , Lan V. Zhang
    • , Sharyl L. Wong
    • , Giovanni Franklin
    •  & Frederick P. Roth
  3. Unité de Recherche en Biologie Moléculaire, Facultés Notre-Dame de la Paix, 61 Rue de Bruxelles, 5000 Namur, Belgium

    • Matija Dreze
    • , Philippe Lamesch
    •  & Jean Vandenhaute
  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

    • Janghoo Lim
    •  & Huda Y. Zoghbi
  5. Arcbay, Inc., 6 Whittier Place, Suite 7J, Boston, Massachusetts 01915, USA

    • Robert S. Sikorski
  6. Agencourt Bioscience Corporation, 500 Cummings Center, Suite 2450, Beverly, Massachusetts 01915, USA

    • Stephanie Bosak
    • , Reynaldo Sequerra
    •  & Lynn Doucette-Stamm


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Competing interests

Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests.

Corresponding authors

Correspondence to David E. Hill or Frederick P. Roth or Marc Vidal.

Supplementary information

Word documents

  1. 1.

    Supplementary Data

    This file contains expanded information regarding various concepts discussed in the main paper, plus a Methods section. In addition, this file has a Supplementary Methods section and legends for the Supplementary Figures and Tables.

PDF files

  1. 1.

    Supplementary Figure S1a

    Filtering and quality assessment of Y2H interactions.

  2. 2.

    Supplementary Figure S1b

    Filtering and quality assessment of Y2H interactions.

  3. 3.

    Supplementary Figure S1c

    Filtering and quality assessment of Y2H interactions.

  4. 4.

    Supplementary Figure S2

    Bias in network neighborhoods for either CCSB-HI1 or LCI interactions.

  5. 5.

    Supplementary Figure S3

    Occurrence of CCSB-HI1-associated, LCI-associated associated gene pairs in Pubmed or Google Scholar searches.

  6. 6.

    Supplementary Figure S4a

    Correlation of interaction data with other gene- or protein-pair characteristics.

  7. 7.

    Supplementary Figure S4b

    Correlation of interaction data with other gene- or protein-pair characteristics.

  8. 8.

    Supplementary Figure S5a

    Network analyses of CCSB-HI1.

  9. 9.

    Supplementary Figure S5b

    Network analyses of CCSB-HI1.

  10. 10.

    Supplementary Figure S5c

    Network analyses of CCSB-HI1.

  11. 11.

    Supplementary Figure S5d

    Network analyses of CCSB-HI1.

  12. 12.

    Supplementary Figure S6a

    Sub-networks of putative biological modules.

  13. 13.

    Supplementary Figure S6b

    Sub-networks of putative biological modules.

  14. 14.

    Supplementary Figure S6c

    Sub-networks of putative biological modules.

Excel files

  1. 1.

    Supplementary Table S1

    List of all human ORFs in Space-I that were tested for Y2H interactions.

  2. 2.

    Supplementary Table S2

    List of CCSB-HI1 and LCI binary interactions along with annotation.

  3. 3.

    Supplementary Table S3

    List of CCSB-HI1 and LCI interactions that were tested in co-AP experiments.

  4. 4.

    Supplementary Table S4

    List of over-represented and under-represented Pfam-A domains in CCSB-HI1 and LCI data sets.

  5. 5.

    Supplementary Table S5

    Analysis of overlap between CCSB-HI1 or LCI-interacting protein-pairs with other shared gene- or protein-pair characteristics.

  6. 6.

    Supplementary Table S6

    Statistics of CCSB-HI1interactions between proteins in different evolutionary classes.

  7. 7.

    Supplementary Table S7

    List of 172 MCODE-generated clusters from the CCSB-HI1 network and the combined CCSB-HI1/LCI and CCSB-HI1/LC networks.

  8. 8.

    Supplementary Table S8

    Potentially novel associations of proteins with genetic disorders as revealed by the CCSB-HI1 interaction data set.


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