Rugged Metropolis sampling with simultaneous updating of two dynamical variables

The rugged Metropolis (RM) algorithm is a biased updating scheme which aims at directly hitting the most likely configurations in a rugged free-energy landscape. Details of the one-variable (RM1) implementation of this algorithm are presented. This is followed by an extension to simultaneous updating of two dynamical variables (RM2). In a test with the brain peptide Met-Enkephalin in vacuum RM2 improves conventional Metropolis simulations by a factor of about 4. Correlations between three or more dihedral angles appear to prevent larger improvements at low temperatures. We also investigate a multihit Metropolis scheme, which spends more CPU time on variables with large autocorrelation times.