Wednesday, September 30, 2009
3:00 - 4:00
Room 457 Blocker
Department of Statistical Science
Bayesian Shape Analysis and Protein Structure Alignment
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. Large-scale experimental efforts are collecting increasingly large numbers of high-resolution structural data.
We present a Bayesian approach to protein structure alignment and analysis of 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 the study of conservation and variability, examination of uncertainty in structural alignments, algorithms for flexible matching, and the impact of alignment uncertainty on phylogenetic tree reconstruction.