From polypeptide sequences to structures using Monte Carlo simulations and an optimized potential

摘要

The ab initio prediction of the structure of a polypeptide from its sequence necessarily requires the detection of the lowest energy forms which correspond to the native state of the polypeptide. A potential for modeling the energy hypersurface of polypeptides using a hybrid level of description is optimized for the structures of four training peptides which have been shown experimentally to adopt, #alpha#, #beta##beta#, #alpha##beta#, and #beta##beta##alpha# conformations is aqueous solution. This potential is then used in diffusion process-controlled Monte Carlo simulations to predict the native structures of this training set of peptides and a test set to 20 peptides which were not themselves used during the optimization of the potential. Starting from various fully extended conformations, all simulations lead to an ensemble of conformations compatible with experimental results. These conformations include simple motifs such as coil, #alpha# helix, #beta#-turn, #beta#-hairpin, #beta##alpha#, and coil-#alpha# conformations, but also more complex motifs such as turnlike, #beta##beta##alpha#, #beta##beta##beta#, and #alpha#-helical hairpin conformations.