Bayesian Protein Structure Prediction

An important role for statisticians in the age of the Human Genome Project has developed in the emerging area of “structural bioinformatics”. Sequence analysis and structure prediction for biopolymers is a crucial step on the path to turning newly sequenced genomic data into biologically and pharmaceutically relevant information in support of molecular medicine. We describe our work on Bayesian models for prediction of protein structure from sequence, based on analysis of a database of experimentally determined protein structures. We have previously developed segment-based models of protein secondary structure which capture fundamental aspects of the protein folding process. These models provide predictive performance at the level of the best available methods in the field (Schmidler et al., 2000). Here we show that this Bayesian framework is naturally generalized to incorporate information based on non-local sequence interactions. We demonstrate this idea by presenting a simple model for β-strand pairing and a Markov chain Monte Carlo (MCMC) algorithm for inference. We apply the approach to prediction of 3-dimensional contacts for two example proteins.