BACKGROUND: Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments. RESULTS: The Bayesian semiparametric approach is illustrated with simulated data and the abalone lysin sperm data. Our method identifies groups of properties which, for this particular dataset, have a similar effect on evolution. The model also provides nonparametric site-specific estimates for the strength of conservation of these properties. CONCLUSIONS: The model described here is distinguished by its ability to handle a large number of amino acid properties simultaneously, while taking into account that such data can be correlated. The multi-level clustering ability of the model allows for appealing interpretations of the results in terms of properties that are roughly equivalent from the standpoint of molecular evolution.

MOTIVATION: A number of methods for detecting positive selection in protein coding DNA sequences are based on whether each site/region has a non-synonymous to synonymous substitution rates ratio ω greater than one. However, a site/region may show a relatively large number of non-synonymous mutations that conserve a particular property. Recent methods have proposed to consider as evidence for molecular adaptations how conserving, or radically different, non-synonymous mutations are with respect to some key amino acid properties. While such methods have been useful in providing a qualitative assessment of molecular adaptation, they rely on independent statistical analyses for each amino acid property and typically do not properly adjust for multiple comparisons when selection needs to be assessed at several sites. RESULTS: We consider a Bayesian hierarchical model that allows us to jointly determine if a set of amino acid properties are being conserved or radically changed while simultaneously adjusting for multiple comparisons at the codon level. We illustrate how this model can be used to characterize molecular adaptation in two datasets: an alignment from six class I alleles of the human major histocompatibility complex and a sperm lysin alignment from 25 abalone species. We compare the results obtained with the proposed hierarchical models to those obtained with alternative methods. Our analyses indicate that a more complete quantitative and qualitative characterization of molecular adaptation is achieved by taking into account changes in amino acid properties. AVAILABILITY: The R code for implementing the hierarchical models is freely available at http://www.ams.ucsc.edu/∼raquel/software/.