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MBE Advance Access published online on November 16, 2006
Molecular Biology and Evolution, doi:10.1093/molbev/msl175
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© 2006 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Accepted November 2, 2006

Research Article

A Combined Empirical and Mechanistic Codon Model

Adi Doron-Faigenboim 1 and Tal Pupko 1 *

1 Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel

* To whom correspondence should be addressed.
Tal Pupko, E-mail: talp{at}post.tau.ac.il


  Abstract

The evolutionary selection forces acting on a protein are commonly inferred using evolutionary codon models by contrasting the rate of synonymous to nonsynonymous substitutions. Most widely used models are based on theoretical assumptions and ignore the empirical observation that distinct amino acids differ in their replacement rates. In this paper, we develop a general method that allows assimilation of empirical amino-acid replacement probabilities into a codon substitution matrix. In this way, the resulting codon model takes into account not only the transition-transversion bias and the nonsynonymous/synonymous ratio, but also the different amino-acid replacement probabilities as specified in empirical amino-acid matrices. Different empirical amino-acid replacement matrices, such as secondary-structure-specific matrices or organelle-specific-matrices (e.g., mitochondria, chloroplasts), can be incorporated into the model, making it context dependent. Using a diverse set of coding DNA sequences we show that the novel model better fits biological data as compared with either mechanistic or empirical codon models. Using the suggested model, we further analyze HIV-1 protease sequences obtained from drug treated patients, and reveal positive selection in sites which are known to confer drug resistance to the virus.

Keywords: evolutionary models; positive selection; purifying selection; empirical amino-acid replacement matrices; Bayesian inference; Ka/Ks; codon models.
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