ENERGY SMOOTHING METHODS FOR CONFORMATIONAL SEARCH

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The twin challenges in computer modeling of biopolymers are achieving chemical accuracy in the description of energetics, and adequately searching conformational space for large flexible systems. This project proposes to improve and extend the application of potential energy smoothing techniques for conformational search. Current search methods, such as simulated annealing, are critical to much of the ongoing research in structural biology. The search problem can be largely overcome by sampling on mathematically transformed potential energy surfaces which are smoother than the original surfaces. In this project, ideas derived from work by Scheraga on the Diffusion Equation Method and Straub s work on Gaussian Density Annealing form the basis of new Potential Smoothing Search (PSS) algorithms exhibiting a much larger range of convergence in conformational search applications. Analytical expressions for course-grained, smoothable energy functions and solvation models are derived. Incorporation of better local search heuristics and anisotropic atomic probability distributions should result in a combined method superior to widely used annealing protocols. For many systems, PSS methods can produce the global energy minimum in a single deterministic calculation. A related algorithm, Conformational Scanning, finds sets of structures representing unique low energy basins spanning the potential surfaces of organic molecules, peptides and small proteins. These smoothing algorithms will be applied to problems in conformational search and structure refinement, packing of small molecule crystals, flexible ligand docking to protein binding sites, and the characterization and clustering of molecular conformations. Emphasis will be placed on developing models for helical transmembrane proteins such as glycophorin, bacteriorhodopsin, rhodopsin and the family of G-protein coupled receptors. Interactions between transmembrane helices are central determinants of structure and function for many integral membrane proteins. Full understanding of these systems requires atomic resolution structural knowledge which is difficult to obtain via crystallographic of NMR methods. PSS methods represent an appropriate and efficient search paradigm for modeling these biologically important systems.