Predicting helical hairpins from sequences by Monte Carlo simulations

The key problem in polypeptide-structure prediction is with regard to thermodynamics. Two factors Limit prediction in nb initio computer simulations. First, the thermodynamically dominant conformations must be found from an extremely large number of possible conformations. Second, these low-energy forms must deviate little from the experimental structures. Here, we report on the application of the diffusion-controlled Monte Carlo approach to predict four a-helical hairpins with 34-38 residues by global optimization, using an energy optimized on other supersecondary structures. A total of seven simulations is carried out for each protein starting from fully extended conformations. Three proteins are correctly folded (within 3.0 Angstrom rms from the experimental structures), but the fourth protein cannot distinguish between several equienergetic conformations. Possible improvement of the energy model is suggested. (C) 2000 John Wiley & Sons, Inc.