1. Computational and Systems Biology

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
https://doi.org/10.7554/eLife.18715
Share this article
Cite this article
  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
  2. Download BibTeX
  3. Download .RIS

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

Version 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

  • 3,645
    views
  • 786
    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

Categories and tags

Research organism

Further reading

    1. Cell Biology
    2. Computational and Systems Biology

    circHIPK3 nucleates IGF2BP2 and functions as a competing endogenous RNA

    Trine Line Hauge Okholm, Andreas Bjerregaard Kamstrup ... Christian Kroun Damgaard
    Research Article

    Circular RNAs represent a class of endogenous RNAs that regulate gene expression and influence cell biological decisions with implications for the pathogenesis of several diseases. Here, we disclose a novel gene-regulatory role of circHIPK3 by combining analyses of large genomics datasets and mechanistic cell biological follow-up experiments. Using time-course depletion of circHIPK3 and specific candidate RNA-binding proteins, we identify several perturbed genes by RNA sequencing analyses. Expression-coupled motif analyses identify an 11-mer motif within circHIPK3, which also becomes enriched in genes that are downregulated upon circHIPK3 depletion. By mining eCLIP datasets and combined with RNA immunoprecipitation assays, we demonstrate that the 11-mer motif constitutes a strong binding site for IGF2BP2 in bladder cancer cell lines. Our results suggest that circHIPK3 can sequester IGF2BP2 as a competing endogenous RNA (ceRNA), leading to target mRNA stabilization. As an example of a circHIPK3-regulated gene, we focus on the STAT3 mRNA as a specific substrate of IGF2BP2 and validate that manipulation of circHIPK3 regulates IGF2BP2-STAT3 mRNA binding and, thereby, STAT3 mRNA levels. Surprisingly, absolute copy number quantifications demonstrate that IGF2BP2 outnumbers circHIPK3 by orders of magnitude, which is inconsistent with a simple 1:1 ceRNA hypothesis. Instead, we show that circHIPK3 can nucleate multiple copies of IGF2BP2, potentially via phase separation, to produce IGF2BP2 condensates. Our results support a model where a few cellular circHIPK3 molecules can induce IGF2BP2 condensation, thereby regulating key factors for cell proliferation.

    1. Cell Biology
    2. Computational and Systems Biology

    Multi-omic analysis of bat versus human fibroblasts reveals altered central metabolism

    N Suhas Jagannathan, Javier Yu Peng Koh ... Lisa Tucker-Kellogg
    Research Article

    Bats have unique characteristics compared to other mammals, including increased longevity and higher resistance to cancer and infectious disease. While previous studies have analyzed the metabolic requirements for flight, it is still unclear how bat metabolism supports these unique features, and no study has integrated metabolomics, transcriptomics, and proteomics to characterize bat metabolism. In this work, we performed a multi-omics data analysis using a computational model of metabolic fluxes to identify fundamental differences in central metabolism between primary lung fibroblast cell lines from the black flying fox fruit bat (Pteropus alecto) and human. Bat cells showed higher expression levels of Complex I components of electron transport chain (ETC), but, remarkably, a lower rate of oxygen consumption. Computational modeling interpreted these results as indicating that Complex II activity may be low or reversed, similar to an ischemic state. An ischemic-like state of bats was also supported by decreased levels of central metabolites and increased ratios of succinate to fumarate in bat cells. Ischemic states tend to produce reactive oxygen species (ROS), which would be incompatible with the longevity of bats. However, bat cells had higher antioxidant reservoirs (higher total glutathione and higher ratio of NADPH to NADP) despite higher mitochondrial ROS levels. In addition, bat cells were more resistant to glucose deprivation and had increased resistance to ferroptosis, one of the characteristics of which is oxidative stress. Thus, our studies revealed distinct differences in the ETC regulation and metabolic stress responses between human and bat cells.

    1. Computational and Systems Biology
    2. Neuroscience

    Myelin dystrophy impairs signal transmission and working memory in a multiscale model of the aging prefrontal cortex

    Sara Ibañez, Nilapratim Sengupta ... Christina M Weaver
    Research Article

    Normal aging leads to myelin alterations in the rhesus monkey dorsolateral prefrontal cortex (dlPFC), which are positively correlated with degree of cognitive impairment. It is hypothesized that remyelination with shorter and thinner myelin sheaths partially compensates for myelin degradation, but computational modeling has not yet explored these two phenomena together systematically. Here, we used a two-pronged modeling approach to determine how age-related myelin changes affect a core cognitive function: spatial working memory. First, we built a multicompartment pyramidal neuron model fit to monkey dlPFC empirical data, with an axon including myelinated segments having paranodes, juxtaparanodes, internodes, and tight junctions. This model was used to quantify conduction velocity (CV) changes and action potential (AP) failures after demyelination and subsequent remyelination. Next, we incorporated the single neuron results into a spiking neural network model of working memory. While complete remyelination nearly recovered axonal transmission and network function to unperturbed levels, our models predict that biologically plausible levels of myelin dystrophy, if uncompensated by other factors, can account for substantial working memory impairment with aging. The present computational study unites empirical data from ultrastructure up to behavior during normal aging, and has broader implications for many demyelinating conditions, such as multiple sclerosis or schizophrenia.