Statistical Phylogenetic Models for the Inference of Functionally Important Regions in Proteins

Abstract

<p>An important question in biology is the identification of functionally important sites and regions in proteins. A variety of statistical phylogenetic models have been developed to predict functionally important protein sites, e.g. ligand binding sites or protein-protein interaction interfaces, by comparing sequences from different species. However, most of the existing methods ignore the spatial clustering of functionally important sites in protein tertiary/primary structures, which significantly reduces their power to identify functionally important regions in proteins. In this thesis, we present several new statistical phylogenetic models for inferring functionally important protein regions in which Gaussian processes or hidden Markov models are used as prior distributions to model the spatial correlation of evolutionary patterns in protein tertiary/ primary structures. Both simulation studies and empirical data analyses suggest that these new models outperform classic phylogenetic models. Therefore, these new models may be useful tools for extracting functional insights from protein sequences and for guiding mutagenesis experiments. Furthermore, the new methodologies developed in these models may also be used in the development of new statistical models to answer other important questions in phylogenetics and molecular evolution.</p>