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Abstract

Identification of protein–protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization–time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein–protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein–protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.

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Acknowledgements

We thank M. Chow, N. Mohammad, C. Chung and V. Fong for their assistance with the creation of the web resources. We are grateful to J. van Helden and S. Brohée for sharing information on their comparison of clustering methods before publication. This research was supported by grants from Genome Canada and the Ontario Genomics Institute (to J.F.G. and A.E.), the Canadian Institutes of Health Research (to A.E., N.J.K., J.F.G., S.J.W., S.P. and C.J.I.), the National Cancer Institute of Canada with funds from the Canadian Cancer Society (to J.F.G.), the Howard Hughes Medical Institute (to J.S.W. and E.O.), the McLaughlin Centre for Molecular Medicine (to S.J.W. and S.P.), the Hospital for Sick Children (to J.M.P.-A.), the National Sciences and Engineering Research Council (to N.J.K., T.R.H. and A.E.) and the National Institutes of Health (to A.S., M.G., A.P. and H.Y.).

Author information

Author notes

    • Nevan J. Krogan

    †Present address: Department of Cellular and Molecular Pharmacology, UCSF, San Francisco, California 94143, USA

    • Nevan J. Krogan
    •  & Gerard Cagney

    *These authors contributed equally to this work

Affiliations

  1. Banting and Best Department of Medical Research, Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College St, Toronto, Ontario M5S 3E1, Canada

    • Nevan J. Krogan
    • , Gerard Cagney
    • , Gouqing Zhong
    • , Xinghua Guo
    • , Alexandr Ignatchenko
    • , Joyce Li
    • , Nira Datta
    • , Aaron P. Tikuisis
    • , Thanuja Punna
    • , Michael Shales
    • , Xin Zhang
    • , Michael Davey
    • , Mark D. Robinson
    • , James E. Bray
    • , Anthony Sheung
    • , Atanas Lalev
    • , Peter Wong
    • , Andrei Starostine
    • , Myra M. Canete
    • , Shamanta Chandran
    • , Robin Haw
    • , Jennifer J. Rilstone
    • , Kiran Gandi
    • , Natalie J. Thompson
    • , Gabe Musso
    • , Peter St Onge
    • , Shaun Ghanny
    • , Mandy H. Y. Lam
    • , Gareth Butland
    • , C. James Ingles
    • , Timothy R. Hughes
    • , Andrew Emili
    •  & Jack F. Greenblatt
  2. Department of Medical Genetics and Microbiology, University of Toronto, 1 Kings College Circle, Toronto, Ontario M5S 1A8, Canada

    • Nevan J. Krogan
    • , Mandy H. Y. Lam
    • , C. James Ingles
    • , Timothy R. Hughes
    • , Andrew Emili
    •  & Jack F. Greenblatt
  3. Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland

    • Gerard Cagney
  4. Department of Molecular Biophysics and Biochemistry, 266 Whitney Avenue, Yale University, PO Box 208114, New Haven, Connecticut 06520, USA

    • Haiyuan Yu
    • , Alberto Paccanaro
    •  & Mark Gerstein
  5. Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M4K 1X8, Canada

    • Shuye Pu
    • , José M. Peregrín-Alvarez
    • , James Vlasblom
    • , Samuel Wu
    • , Chris Orsi
    • , John Parkinson
    •  & Shoshana J. Wodak
  6. Affinium Pharmaceuticals, 100 University Avenue, Toronto, Ontario M5J 1V6, Canada

    • Bryan Beattie
    • , Dawn P. Richards
    • , Veronica Canadien
    •  & Frank Mena
  7. Howard Hughes Medical Institute, Department of Cellular and Molecular Pharmacology, UCSF, Genentech Hall S472C, 600 16th St, San Francisco, California 94143, USA

    • Sean R. Collins
    •  & Jonathan S. Weissman
  8. Comparative Genomics Laboratory, Nara Institute of Science and Technology 8916-5, Takayama, Ikoma, Nara 630-0101, Japan

    • Amin M. Altaf-Ul
    •  & Shigehiko Kanaya
  9. Department of Biochemistry, Saint Louis University School of Medicine, 1402 South Grand Boulevard, St Louis, Missouri 63104, USA

    • Ali Shilatifard
  10. Howard Hughes Medical Institute, Department of Molecular and Cellular Biology, Harvard University, 7 Divinity Avenue, Cambridge, Massachusetts 02138, USA

    • Erin O'Shea

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Competing interests

Protein interaction information from this paper has been provided to the BioGRID database (http://thebiogrid.org), as well as the International Molecular Interaction Exchange consortium (IMEx, http://imex.sf.net) consisting of BIND, DIP, IntAct, MINT and Mpact (MIPS). Reprints and permissions information is available at npg.nature.com/reprintsandpermissions. The authors declare no competing financial interests.

Corresponding authors

Correspondence to Andrew Emili or Jack F. Greenblatt.

Supplementary information

PDF files

  1. 1.

    Supplementary Notes

    This file contains Supplementary Discussion and Supplementary Methpds on generating the interaction network, visualization, and quality assessment.

  2. 2.

    Supplementary Figures 1–5

    Supplementary Figure 1 details the co-localization of MIPS, Gavin, Ho, Core, Extended Core datasets. Supplementary Figure 2 details the semantic similarity (GO biological processes) for all. Supplementary Figure 3 details the cytoscape view indicating comparison with MIPS. Supplementary Figure 4 details the essentiality versus conservation, degree of connectivity and betweenness. Supplementary Figure 5 details the IWR1 complex data.

  3. 3.

    Supplementary Figure 6

    Guide to the yeast interactome database

  4. 4.

    Supplementary Table Legends

    This file contains a detailed text guide to the contents of the Supplementary Tables.

Excel files

  1. 1.

    Supplementary Tables 1–3

    Supplementary Table 1 is a list of all the 4562 proteins whose purification was attempted. Supplementary Table 2 is a list of all the 2357 proteins whose purification was successful. Supplementary Table 3 is a list of 4087 proteins that were identified via MS.

  2. 2.

    Supplementary Tables 4–6

    Supplementary Table 4 is a list of 71 proteins that were identified in more than 3% of all the successful protein purifications. Supplementary Table 5 is a list of 2357 protein-protein interactions in the intersection dataset. Supplementary Table 6 is a list of 5496 protein-protein interactions in the merged dataset.

  3. 3.

    Supplementary Table 9

    Complete list of all the putative S. cerevisiae protein-protein interactions identified in this study.

Text files

  1. 1.

    Supplementary Tables 7, 8 and 10

    Supplementary Table 7 is a list of 7123 protein-protein interactions in the core dataset. Supplementary Table 8 is a list of 14317 protein-protein interactions in the extended dataset. Supplementary Table 10 is a list of protein complexes and their component subunits as identified by the Markov Cluster Algorithm.

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DOI

https://doi.org/10.1038/nature04670

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