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A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae

Author

Listed:
  • Peter Uetz

    (Departments of Genetics and Medicine)

  • Loic Giot

    (CuraGen Corporation)

  • Gerard Cagney

    (Departments of Genetics and Medicine)

  • Traci A. Mansfield

    (CuraGen Corporation)

  • Richard S. Judson

    (CuraGen Corporation)

  • James R. Knight

    (CuraGen Corporation)

  • Daniel Lockshon

    (Departments of Genetics and Medicine)

  • Vaibhav Narayan

    (CuraGen Corporation)

  • Maithreyan Srinivasan

    (CuraGen Corporation)

  • Pascale Pochart

    (CuraGen Corporation)

  • Alia Qureshi-Emili

    (Departments of Genetics and Medicine
    Howard Hughes Medical Institute, University of Washington)

  • Ying Li

    (CuraGen Corporation)

  • Brian Godwin

    (CuraGen Corporation)

  • Diana Conover

    (Departments of Genetics and Medicine
    Howard Hughes Medical Institute, University of Washington)

  • Theodore Kalbfleisch

    (CuraGen Corporation)

  • Govindan Vijayadamodar

    (CuraGen Corporation)

  • Meijia Yang

    (CuraGen Corporation)

  • Mark Johnston

    (Departments of Genetics and Medicine
    Washington University Medical School)

  • Stanley Fields

    (Departments of Genetics and Medicine
    Howard Hughes Medical Institute, University of Washington)

  • Jonathan M. Rothberg

    (CuraGen Corporation)

Abstract

Two large-scale yeast two-hybrid screens were undertaken to identify protein–protein interactions between full-length open reading frames predicted from the Saccharomyces cerevisiae genome sequence. In one approach, we constructed a protein array of about 6,000 yeast transformants, with each transformant expressing one of the open reading frames as a fusion to an activation domain. This array was screened by a simple and automated procedure for 192 yeast proteins, with positive responses identified by their positions in the array. In a second approach, we pooled cells expressing one of about 6,000 activation domain fusions to generate a library. We used a high-throughput screening procedure to screen nearly all of the 6,000 predicted yeast proteins, expressed as Gal4 DNA-binding domain fusion proteins, against the library, and characterized positives by sequence analysis. These approaches resulted in the detection of 957 putative interactions involving 1,004 S. cerevisiae proteins. These data reveal interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes. The results of these screens are shown here.

Suggested Citation

  • Peter Uetz & Loic Giot & Gerard Cagney & Traci A. Mansfield & Richard S. Judson & James R. Knight & Daniel Lockshon & Vaibhav Narayan & Maithreyan Srinivasan & Pascale Pochart & Alia Qureshi-Emili & Y, 2000. "A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae," Nature, Nature, vol. 403(6770), pages 623-627, February.
  • Handle: RePEc:nat:nature:v:403:y:2000:i:6770:d:10.1038_35001009
    DOI: 10.1038/35001009
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    Cited by:

    1. Bingjie Hao & István A. Kovács, 2023. "A positive statistical benchmark to assess network agreement," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Erica W. Carter & Orlene Guerra Peraza & Nian Wang, 2023. "The protein interactome of the citrus Huanglongbing pathogen Candidatus Liberibacter asiaticus," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Lv, Changchun & Yuan, Ziwei & Si, Shubin & Duan, Dongli & Yao, Shirui, 2022. "Cascading failure in networks with dynamical behavior against multi-node removal," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    4. Mu Gao & Davi Nakajima An & Jerry M. Parks & Jeffrey Skolnick, 2022. "AF2Complex predicts direct physical interactions in multimeric proteins with deep learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    5. Duan, Dongli & Yan, Qi & Rong, Yisheng & Hou, Gege, 2022. "Predicting the cascading failure of dynamical networks based on a new dimension reduction method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).

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