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.

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

  • 3,749
    views
  • 811
    downloads
  • 53
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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)

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

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

  1. José Ignacio Garzón
  2. Lei Deng
  3. Diana Murray
  4. Sagi Shapira
  5. Donald Petrey
  6. Barry Honig
(2016)
A computational interactome and functional annotation for the human proteome
eLife 5:e18715.
https://doi.org/10.7554/eLife.18715

Share this article

https://doi.org/10.7554/eLife.18715

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Eric V Strobl, Eric Gamazon
    Research Article

    Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.

    1. Computational and Systems Biology
    Liqi Kang, Banghao Wu ... Liang Hong
    Research Article

    Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance, and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.