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Using Coevolution to Predict Protein–Protein Interactions

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Network Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 781))

Abstract

Bioinformatic methods to predict protein–protein interactions (PPI) via coevolutionary analysis have ­positioned themselves to compete alongside established in vitro methods, despite a lack of understanding for the underlying molecular mechanisms of the coevolutionary process. Investigating the alignment of coevolutionary predictions of PPI with experimental data can focus the effective scope of prediction and lead to better accuracies. A new rate-based coevolutionary method, MMM, preferentially finds obligate interacting proteins that form complexes, conforming to results from studies based on coimmunoprecipitation coupled with mass spectrometry. Using gold-standard databases as a benchmark for accuracy, MMM surpasses methods based on abundance ratios, suggesting that correlated evolutionary rates may yet be better than coexpression at predicting interacting proteins. At the level of protein domains, ­coevolution is difficult to detect, even with MMM, except when considering small-scale experimental data involving proteins with multiple domains. Overall, these findings confirm that coevolutionary ­methods can be confidently used in predicting PPI, either independently or as drivers of coimmunoprecipitation experiments.

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Acknowledgements

This research was funded in part by the Ontario Ministry of Health and Long Term Care. The views expressed do not necessarily reflect those of the Ministry. Funding was also provided by a fellowship from the CIHR Training Program in Protein Folding: Principles and Diseases to GWC, an NSERC USRA to JMY and an NSERC discovery grant to ERMT. ERMT holds a Canada Research Chair in Analytical Genomics.

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Authors and Affiliations

  1. Department of Medical Biophysics, University of Toronto, Campbell Family Institute for Cancer Research, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada

    Gregory W. Clark, Vaqaar-un-Nisa Dar, Alexandr Bezginov, **ghao M. Yang, Robert L. Charlebois & Elisabeth R. M. Tillier

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  1. Gregory W. Clark
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  2. Vaqaar-un-Nisa Dar
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  3. Alexandr Bezginov
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  4. **ghao M. Yang
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  5. Robert L. Charlebois
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  6. Elisabeth R. M. Tillier
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Corresponding author

Correspondence to Elisabeth R. M. Tillier .

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Editors and Affiliations

  1. Conway Institute, Sch. Biomolecular & Biomedical Science, University College Dublin, Belfield, Dublin, 4, Ireland

    Gerard Cagney

  2. Donnelly Ctr Cell. & Biomolecular Res., Banting & Best Dept. Medical Research, University of Toronto, College St. 160, Toronto, M5S 3E1, Ontario, Canada

    Andrew Emili

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Clark, G.W., Dar, VuN., Bezginov, A., Yang, J.M., Charlebois, R.L., Tillier, E.R.M. (2011). Using Coevolution to Predict Protein–Protein Interactions. In: Cagney, G., Emili, A. (eds) Network Biology. Methods in Molecular Biology, vol 781. Humana Press. https://doi.org/10.1007/978-1-61779-276-2_11

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  • DOI: https://doi.org/10.1007/978-1-61779-276-2_11

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  • Print ISBN: 978-1-61779-275-5

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