IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v415y2002i6868d10.1038_415180a.html
   My bibliography  Save this article

Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry

Author

Listed:
  • Yuen Ho

    (MDS Proteomics)

  • Albrecht Gruhler

    (MDS Proteomics)

  • Adrian Heilbut

    (MDS Proteomics)

  • Gary D. Bader

    (Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital
    University of Toronto)

  • Lynda Moore

    (MDS Proteomics)

  • Sally-Lin Adams

    (MDS Proteomics)

  • Anna Millar

    (MDS Proteomics)

  • Paul Taylor

    (MDS Proteomics)

  • Keiryn Bennett

    (MDS Proteomics)

  • Kelly Boutilier

    (MDS Proteomics)

  • Lingyun Yang

    (MDS Proteomics)

  • Cheryl Wolting

    (MDS Proteomics)

  • Ian Donaldson

    (MDS Proteomics)

  • Søren Schandorff

    (MDS Proteomics)

  • Juanita Shewnarane

    (MDS Proteomics)

  • Mai Vo

    (MDS Proteomics
    Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital)

  • Joanne Taggart

    (MDS Proteomics
    Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital)

  • Marilyn Goudreault

    (MDS Proteomics
    Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital)

  • Brenda Muskat

    (MDS Proteomics)

  • Cris Alfarano

    (MDS Proteomics)

  • Danielle Dewar

    (Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital)

  • Zhen Lin

    (Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital)

  • Katerina Michalickova

    (Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital
    University of Toronto)

  • Andrew R. Willems

    (Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital
    University of Toronto)

  • Holly Sassi

    (Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital)

  • Peter A. Nielsen

    (MDS Proteomics)

  • Karina J. Rasmussen

    (MDS Proteomics)

  • Jens R. Andersen

    (MDS Proteomics)

  • Lene E. Johansen

    (MDS Proteomics)

  • Lykke H. Hansen

    (MDS Proteomics)

  • Hans Jespersen

    (MDS Proteomics)

  • Alexandre Podtelejnikov

    (MDS Proteomics)

  • Eva Nielsen

    (MDS Proteomics)

  • Janne Crawford

    (MDS Proteomics)

  • Vibeke Poulsen

    (MDS Proteomics)

  • Birgitte D. Sørensen

    (MDS Proteomics)

  • Jesper Matthiesen

    (MDS Proteomics)

  • Ronald C. Hendrickson

    (MDS Proteomics)

  • Frank Gleeson

    (MDS Proteomics)

  • Tony Pawson

    (Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital
    University of Toronto)

  • Michael F. Moran

    (MDS Proteomics)

  • Daniel Durocher

    (Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital
    University of Toronto)

  • Matthias Mann

    (MDS Proteomics)

  • Christopher W. V. Hogue

    (MDS Proteomics
    Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital
    University of Toronto)

  • Daniel Figeys

    (MDS Proteomics)

  • Mike Tyers

    (Programme in Molecular Biology and Cancer, Samuel Lunenfeld Research Institute, Mount Sinai Hospital
    University of Toronto)

Abstract

The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function1. 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 expression2,3,4. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale5,6. 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 studies3,4. 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.

Suggested Citation

  • Yuen Ho & Albrecht Gruhler & Adrian Heilbut & Gary D. Bader & Lynda Moore & Sally-Lin Adams & Anna Millar & Paul Taylor & Keiryn Bennett & Kelly Boutilier & Lingyun Yang & Cheryl Wolting & Ian Donalds, 2002. "Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry," Nature, Nature, vol. 415(6868), pages 180-183, January.
  • Handle: RePEc:nat:nature:v:415:y:2002:i:6868:d:10.1038_415180a
    DOI: 10.1038/415180a
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/415180a
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/415180a?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jie Zhao & Xiujuan Lei & Fang-Xiang Wu, 2017. "Predicting Protein Complexes in Weighted Dynamic PPI Networks Based on ICSC," Complexity, Hindawi, vol. 2017, pages 1-11, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:415:y:2002:i:6868:d:10.1038_415180a. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.