title: Dependence on the dielectric model and pH in a synthetic helical peptide studied by Monte Carlo simulated annealing.
abstract: Monte Carlo simulated annealing is applied to the tertiary structure prediction of a 17-residue synthetic peptide, which is known by experiment to exhibit high helical content at low pH. Two dielectric models are considered: sigmoidal distance-dependent dielectric function and a constant dielectric function (epsilon = 2). Starting from completely random initial conformations, our simulations for both dielectric models at low pH gave many helical conformations. The obtained low-energy conformations are compared with the nuclear Overhauser effect spectroscopy cross-peak data for both main chain and side chains, and it is shown that the results for the sigmoidal dielectric function are in remarkable agreement with the experimental data. The results predict the existence of two disjoint helices around residues 5-9 and 11-16, while nmr experiments imply significant alpha-helix content between residues 5 and 14. Simulations with high pH, on the other hand, hardly gave a helical conformation, which is also in accord with the experiment. These findings indicate that when side chains are charged, electrostatic interactions due to these changes play a major role in the helix stability. Our results are compared with the previous 500 ps molecular dynamics simulations of the same peptide. It is argued that simulated annealing is superior to molecular dynamics in two respects: (1) direct folding of alpha-helix from completely random initial conformations is possible for the former, whereas only unfolding of an alpha-helix can be studied by the latter; (2) while both methods predict high helix content for low pH, the results for high pH agree with experiment (low helix content) only for the former method.
authors.affiliation: Stanford Linear Accelerator Center, Stanford University, California 94309.
authors.forename: Y
authors.initials: Y
authors.lastname: Okamoto
mesh_terms: D000595:Amino Acid Sequence; D001704:Biopolymers / Q000737:chemistry; D004563:Electrochemistry; D006863:Hydrogen-Ion Concentration; D008956:Models, Chemical; D008969:Molecular Sequence Data; D009010:Monte Carlo Method; D010455:Peptides / Q000138:chemical synthesis / Q000737:chemistry*; D017434:Protein Structure, Tertiary
chemical_list: D001704:Biopolymers; D010455:Peptides