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MBE Advance Access published online on July 17, 2007
Molecular Biology and Evolution, doi:10.1093/molbev/msm142
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© The Author 2007. 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@oxfordjournals.org

Research Article

Detecting the Coevolution of Biosequences – An Example of RNA Interaction Prediction

Chen-Hsiang Yeang1, Jeremy F.J. Darot2, Harry F. Noller3 and David Haussler4

1 Simons Center for Systems Biology, Institute for Advanced Study
2 EMBL - European Bioinformatics Institute
3 Center for Molecular Biology of RNA and Department of MCD Biology, UC Santa Cruz
4 Center for Biomolecular Science and Engineering, UC Santa Cruz

E-mail: chyeang{at}soe.ucsc.edu

Received for publication December 26, 2006. Revision received June 4, 2007. Revision received July 10, 2007. Accepted for publication July 12, 2007.

A probabilistic graphical model is proposed in order to detect the coevolution between different sites in biological sequences. The model extends the continuous-time Markov process of sequence substitution for single nucleic or amino acids and imposes general constraints regarding simultaneous changes on the substitution rate matrix. Given a multiple sequence alignment for each molecule of interest and a phylogenetic tree, the model can predict potential interactions within or between nucleic acids and proteins. Initial validation of the model is carried out using tRNA and 16S rRNA sequence data. The model accurately identifies the secondary interactions of tRNA as well as several known tertiary interactions. In addition, results on 16S rRNA data indicate this general and simple coevolutionary model outperforms several other parametric and non-parametric methods in predicting secondary interactions. Furthermore, the majority of the putative predictions exhibit either direct contact or proximity of the nucleotide pairs in the 3D structure of the T. thermophilus ribosomal small subunit. The results on RNA data suggest a general model of coevolution might be applied to other types of interactions between protein, DNA and RNA molecules.

Key Words: coevolution • continuous-time Markov models • RNA tertiary interactions • RNA secondary interactions


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