1. Computational and Systems Biology
Download icon

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
Tools and Resources
  • Cited 32
  • Views 3,187
  • Annotations
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.

Data availability

The following previously published data sets were used

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.

Metrics

  • 3,187
    Page views
  • 736
    Downloads
  • 32
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Davide Spalla et al.
    Research Article

    Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it in certain regimes.

    1. Computational and Systems Biology
    2. Neuroscience
    Dhruva Raman, Timothy O'Leary
    Research Article

    Synaptic connections in many brain circuits fluctuate, exhibiting substantial turnover and remodelling over hours to days. Surprisingly, experiments show that most of this flux in connectivity persists in the absence of learning or known plasticity signals. How can neural circuits retain learned information despite a large proportion of ongoing and potentially disruptive synaptic changes? We address this question from first principles by analysing how much compensatory plasticity would be required to optimally counteract ongoing fluctuations, regardless of whether fluctuations are random or systematic. Remarkably, we find that the answer is largely independent of plasticity mechanisms and circuit architectures: compensatory plasticity should be at most equal in magnitude to fluctuations, and often less, in direct agreement with previously unexplained experimental observations. Moreover, our analysis shows that a high proportion of learning-independent synaptic change is consistent with plasticity mechanisms that accurately compute error gradients.