Network Biology

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

Gerard Cagney, Andrew Emili

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.