Abstract

BackgroundWhen biomolecules physically interact, natural selection operates on them jointly. Contacting positions in protein and RNA structures exhibit correlated patterns of sequence evolution due to constraints imposed by the interaction, and molecular arms races can develop between interacting proteins in pathogens and their hosts. To evaluate how well methods developed to detect coevolving residues within proteins can be adapted for cross-species, inter-protein analysis, we used statistical criteria to quantify the performance of these methods in detecting inter-protein residues within 8 angstroms of each other in the co-crystal structures of 33 bacterial protein interactions. We also evaluated their performance for detecting known residues at the interface of a host-virus protein complex with a partially solved structure.ResultsOur quantitative benchmarking showed that all coevolutionary methods clearly benefit from alignments with many sequences. Methods that aim to detect direct correlations generally outperform other approaches. However, faster mutual information based methods are occasionally competitive in small alignments and with relaxed false positive rates. Two commonly used null distributions are anti-conservative and have high false positive rates in some scenarios, although the empirical distribution of scores performs reasonably well with deep alignments.ConclusionsWe conclude that coevolutionary analysis of cross-species protein interactions holds great promise but requires sequencing many more species pairs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0677-y) contains supplementary material, which is available to authorized users.

Highlights

  • When biomolecules physically interact, natural selection operates on them jointly

  • To systematically determine the parameters that affect performance, we focused on the well-characterized interaction between bacterial histidine kinase A (HisKA) and its response regulator (RR), for which a co-crystal structure and thousands of sequences are available

  • Performance benchmarking of coevolution methods The coevolutionary methods benchmarked in our analyses fall into three general groups (Table 1)

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Summary

Introduction

Contacting positions in protein and RNA structures exhibit correlated patterns of sequence evolution due to constraints imposed by the interaction, and molecular arms races can develop between interacting proteins in pathogens and their hosts. Avila-Herrera and Pollard BMC Bioinformatics (2015) 16:268 use evidence of coevolving alignment columns to detect physical interactions within and between biomolecules. These methods draw inspiration from diverse techniques in molecular phylogenetics, inverse statistical mechanics, Bayesian graphical modeling, information theory, sparse inference, and spectral theory (reviewed in [3, 4]). Coevolution methods have used a variety of approaches to counter the dependence induced by shared phylogeny, including removing closely related sequences from alignments to reduce non-independence [5, 6], differential weighting of sequences when computing statistics [7,8,9], and null distributions that directly model or indirectly account for phylogeny [10,11,12,13]

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