Codon substitution models based on residue similarity and their applications

Codon models are now widely used to draw evolutionary inferences from alignments of homologous sequence data. Incorporating physicochemical properties of amino acids into codon models, two novel codon substitution models describing the evolution of protein-coding DNA sequences are presented based on the similarity scores of amino acids. To describe substitutions between codons a continue-time Markov process is used. Transition/transversion rate bias and nonsynonymous codon usage bias are allowed in the models. In our implementation, the parameters are estimated by maximum-likelihood (ML) method as in previous studies. Furthermore, instantaneous mutations involving more than one nucleotide position of a codon are considered in the second model. Then the two suggested models are applied to five real data sets. The analytic results indicate that the new codon models considering physicochemical properties of amino acids can provide a better fit to the data comparing with existing codon models, and then produce more reliable estimates of certain biologically important measures than existing methods.