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MBE Advance Access originally published online on June 8, 2005
Molecular Biology and Evolution 2005 22(9):1919-1928; 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

Research Article

A Model-Based Approach for Detecting Coevolving Positions in a Molecule

Julien Dutheil*, Tal Pupko{dagger}, Alain Jean-Marie{ddagger} and Nicolas Galtier*

* CNRS UMR 5171 Laboratoire "Génome, Populations, Interactions, Adaptation," Université Montpellier II, Montpellier Cedex, France; {dagger} The Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel; and {ddagger} CNRS UMR 5506 Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, Université Montpellier II, Montpellier Cedex, France

E-mail: julien.dutheil{at}univ-montp2.fr.

We present a new method for detecting coevolving 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 the 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 coevolution 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 ribosomal RNA data set, for which extensive structural characterization has been done over the last 30 years. More than 95% of the intramolecular predicted pairs of sites correspond to known interacting site pairs.

Key Words: coevolution • RNA structure prediction • correlated substitutions • intramolecular and intermolecular interaction • Markov models


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