1. Computational and Systems Biology
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A computational interactome and functional annotation for the human proteome

  1. José Ignacio Garzón
  2. Lei Deng
  3. Diana Murray
  4. Sagi Shapira
  5. Donald Petrey
  6. Barry Honig  Is a corresponding author
  1. Columbia University, United States
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Cite this article as: eLife 2016;5:e18715 doi: 10.7554/eLife.18715

Abstract

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein's function. We provide annotations for most human proteins, including many annotated as having unknown function.

Article and author information

Author details

  1. José Ignacio Garzón

    Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Lei Deng

    Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Diana Murray

    Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Sagi Shapira

    Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Donald Petrey

    Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Barry Honig

    Center for Computational Biology and Bioinformatics, Department of Systems Biology, Columbia University, New York, United States
    For correspondence
    bh6@cumc.columbia.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2480-6696

Funding

National Institutes of Health (GM030518)

  • Barry Honig

National Institutes of Health (S10OD012351)

  • José Ignacio Garzón
  • Lei Deng
  • Diana Murray
  • Sagi Shapira
  • Donald Petrey
  • Barry Honig

National Institutes of Health (S10OD021764)

  • José Ignacio Garzón
  • Lei Deng
  • Diana Murray
  • Sagi Shapira
  • Donald Petrey
  • Barry Honig

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Nir Ben-Tal, Tel Aviv University, Israel

Publication history

  1. Received: June 11, 2016
  2. Accepted: October 19, 2016
  3. Accepted Manuscript published: October 22, 2016 (version 1)
  4. Version of Record published: November 18, 2016 (version 2)

Copyright

© 2016, Garzón et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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