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MBE Advance Access published online on June 8, 2005
Molecular Biology and Evolution, doi:10.1093/molbev/msi183
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© The Author 2005. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org
Accepted May 27, 2005

Research Article

A Model-Based Approach for Detecting Co-evolving Positions in a Molecule

Julien Dutheil 1*, Tal Pupko 2, Alain Jean-Marie 3, and Nicolas Galtier 1

1 CNRS UMR 5171 Laboratoire "Génome, Populations, Interactions, Adaptation", Université Montpellier II, FRANCE
2 The Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
3 CNRS UMR 5506 Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, Université Montpellier II, FRANCE

* To whom correspondence should be addressed.
Julien Dutheil, E-mail: Julien.Dutheil{at}univ-montp2.fr


  Abstract

We present a new method for detecting co-evolving sites in molecules. The method relies on a set of aligned sequences (nucleic acid or protein) and uses Markov models of evolution to map the substitutions that occurred at each site onto the branches of the underlying phylogenetic tree. This mapping takes into account uncertainty over ancestral states and among-site rate variation. We then build, for each site, a "substitution vector" containing the posterior estimates of the number of substitutions in each branch. The amount of co-evolution for a pair of sites is then measured as the Pearson correlation coefficient between the two corresponding substitution vectors, and compared to the expectation under the null hypothesis of independence. We applied the method to a 79-species bacterial rRNA dataset, for which extensive structural characterization has been done over the last 30 years. More than 95% of the intra-molecular predicted pairs of sites correspond to known interacting site pairs.

Keywords: co-evolution; RNA structure prediction; correlated substitutions; intra-molecular and inter-molecular interaction; Markov models.
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