Ensemble predictions of RNA and Protein Structures

First, 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.

Then, I will present recent algorithmic advances we have made in the techniques of exploration and analysis of RNA sequence/structure maps, an abstract framework which allows us to bridge different aspects of the sequence/structure relationship.
In particular, we have successfully applied these techniques to discover deleterious mutations that radically modify the structure in the Hepatitis C virus cis-acting replication element. At a higher level, we provided evidence that the complete sequence of the 3'UTR of the GB RNA virus C has been optimized to preserve the secondary structure of the evolutionarily conserved stem regions from the destabilizing effect of pointwise mutations.