-------------------------------------------------------------------- COLLOQUIUM OF THE COMPUTATIONAL MATERIALS SCIENCE CENTER AND THE SCHOOL OF PHYSICS, ASTRONOMY, & COMPUTATIONAL SCIENCES (CSI 898-Sec 001) -------------------------------------------------------------------- Evolving Local Minima in the Protein Energy Surface Brian Olson Computer Science Department, George Mason University, Fairfax, VA Proteins are the molecular tools of living cells and the path to unraveling their function is through modeling and understanding their structure. Many diseases occur when a protein loses its intended function due to inability to form the appropriate structure with which it binds to other molecules. A holistic approach to protein modeling would characterize all possible structural states accessible by a protein under native conditions. However, this task is infeasible. The question then becomes, how can we model the subset of these structural states most relevant to the function or disfunction of a protein? We propose a computational framework to obtain an expansive view of the protein conformational space relevant for function while controlling computational cost. The framework employs the knowledge that functionally-relevant conformations are those low in energy and the framework incorporates the latest understanding of protein structure and energy from biophysics. The proposed search framework employs a hybrid or memetic approach for explicit sampling of local minima in the protein energy surface. This hybrid search framework combines a global evolutionary search approach with a local search component to take advantage of the latest advances from the computational biology community. By combining advanced algorithmic components with the latest understanding of protein biophysics, the proposed search framework is able to more effectively model functionally-relevant conformational states. A direct comparison between the proposed framework and a state-of-the-art coarse-grained sampling algorithm shows that the enhanced sampling strategies lead to a more comprehensive picture of the underlying protein energy surface. By taking this more comprehensive view, the framework is able to capture the protein native state as well as or better than methods relying primarily on protein-specific sampling strategies. September 9, 2013 4:30 pm Room 3301, Exploratory Hall, Fairfax Campus Refreshments will be served at 4:15 PM. ---------------------------------------------------------------------- Find the schedule at http://www.cmasc.gmu.edu/seminars.htm