A HIERARCHICAL MULTISCALE APPROACH TO PROTEIN STRUCTURE PREDICTION: PRODUCTION OF LOW-RESOLUTION PACKING ARRANGEMENTS OF HELICES AND REFINEMENT OF THE BEST MODELS WITH A UNITED-RESIDUE FORCE FIELD.

A hierarchical, two-stage approach to ab initio protein structure prediction is presented and applied to four α-helical proteins. In the first stage, a bank of low-resolution models is generated using a highly simplified protein representation and energy function, coupled with a Conformation-Family Monte Carlo (CFMC) search for the energy minimum. For helical proteins, this procedure (referred to as REPACK) produces a set of plausible packed arrangements of the helices, given their positions in the amino acid sequence. Secondary structure prediction methods such as JPRED can be used to provide the secondary structure assignment. In the second stage, these packing arrangements are used as starting points for a new search method (Local Search), based on the Monte Carlo-with-Minimization (MCM) algorithm and a united-residue (UNRES) energy function. The focus of the Local Search is mainly on improving loop conformations and side-chain positions, with minor modifications to the overall packing of the helices. By reducing the size of the conformational space that must be sampled with the UNRES energy function, which is much more expensive to compute than the REPACK energy function, this prediction scheme can be applied to much larger proteins than were tractable in the past with other UNRES-based search methods. It was applied successfully to a 224-residue protein (target T0198, PDB code 1SUM) in the sixth community-wide blind-prediction experiment on the Critical Assessment of techniques for protein Structure Prediction (CASP6). [ABSTRACT FROM AUTHOR]