BUILDING SELF-AVOIDING LATTICE MODELS OF PROTEINS USING A SELF-CONSISTENT-FIELD OPTIMIZATION

We present an algorithm to build self-avoiding lattice models of chain molecules with low RMS deviation from their actual 3D structures, To find the optimal coordinates for the lattice chain model, we minimize a function that consists of three terms: (1) the sum of squared deviat ions of link coordinates on a lattice from their off-lattice values, ( 2) the sum of ''short-range'' terms, penalizing violation of chain con nectivity, and (3) the sum of ''long-range'' repulsive terms, penalizi ng chain self-intersections, We treat this function as a chain molecul e ''energy'' and minimize it using self-consistent field (SCF) theory to represent the pairwise Link repulsions as 3D fields acting on the l inks, The statistical mechanics of chain molecules enables computation of the chain distribution in this field on the lattice, The field is refined by iteration to become self-consistent with the chain distribu tion, then dynamic programming is used to find the optimal lattice mod el as the ''lowest-energy'' chain pathway in this SCF, We have tested the method on one of the coarsest (and most difficult) lattices used f or model building on proteins of all structural types and show that th e method is adequate for building self-avoiding models of proteins wit h low RMS deviations from the actual structures. (C) 1996 Wiley-Liss, Inc.