UC Berkeley

Background

Statistical methods for identifying positively selected sites in protein coding regions are one of the most commonly used tools in evolutionary bioinformatics. However, they have been limited by not taking the physiochemical properties of amino acids into account.

Results

We develop a new codon-based likelihood model for detecting site-specific selection pressures acting on specific physicochemical properties. Nonsynonymous substitutions are divided into substitutions that differ with respect to the physicochemical properties of interest, and those that do not. The substitution rates of these two types of changes, relative to the synonymous substitution rate, are then described by two parameters, gamma and omega respectively. The new model allows us to perform likelihood ratio tests for positive selection acting on specific physicochemical properties of interest. The new method is first used to analyze simulated data and is shown to have good power and accuracy in detecting physicochemical selective pressure. We then re-analyze data from the class-I alleles of the human Major Histocompatibility Complex (MHC) and from the abalone sperm lysine.

Conclusion

Our new method allows a more flexible framework to identify selection pressure on particular physicochemical properties.