Scott Schmidler (Duke University)

Bayesian Shape Matching for Protein Structure Alignment and Phylogeny

Understanding protein structure and function remains one of the great post-genome challenges of biology and molecular medicine. The 3D structure of a protein provides fundamental insights into its
biological function, mechanism, and interactions, and plays a key role in drug design. We have developed a Bayesian approach to modeling protein structure families, using methods adapted from the statistical
theory of shape. Our approach provides natural solutions to a variety of problems in the field, including pairwise and multiple alignment for the study of conservation and variability, algorithms for flexible
matching, and the impact of alignment uncertainty on phylogenetic tree reconstruction. Our recent efforts focus on extension to full evolutionary stochastic process models, which significantly improve upon
sequence-based phylogeny when divergence times are large.