Monte Carlo backbone sampling for polypeptides with variable bond angles and dihedral angles using concerted rotations and a Gaussian bias

An efficient concerted rotation algorithm for use in Monte Carlo statistical mechanics simulations of polypeptides is reported that includes flexible bond and dihedral angles. A Gaussian bias is applied with driver bond and dihedral angles to optimize the sampling efficiency. Jacobian weighting is required in the Metropolis test to correct for imbalances in resultant transition probabilities. Testing of the methodology includes Monte Carlo simulations for polyalanines with 8–14 residues and a 36-residue protein as well as a search to find the lowest-energy conformer of the pentapeptide Met-enkephalin. The results demonstrate the formal correctness and efficiency of the method.

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