Ensemble predictions of Protein Structures
In this talk, I will describe my work in the area of computational structural biology. I will describe new ensemble modeling techniques which can analyze and predict an entire landscape of structural and evolutionary solutions, rather than simple single answer optimizations. This philosophy has a broad impact on our understanding of protein and RNA molecules -- Both of which I have applied this approach to.
I will introduce a new family of algorithms for investigating the folding landscape of transmembrane beta-barrel proteins based only on sequence information, broad investigator knowledge, and a statistical-mechanical approach using the Boltzmann partition function. This provides predictions of all possible structural conformations that might arise in-vivo, along with their relative likelihood of occurrence. Using a parameterizable grammatical model, these algorithms incorporate high-level information, such as membrane thickness, with an energy function based on stacked amino-acid pair statistical potentials to predict ensemble properties, such as the likelihood of two residues pairing in a beta-sheet, or the per-residue X-ray crystal structure B-value.