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Probability Grid Monte Carlo
PGMC
.
Abstract:
We have devised a Monte-Carlo method that employs importance
sampling of dihedral angles to model peptide and
protein conformations. This new method, which we call Probability
Grid Monte Carlo (PGMC), modifies amino acid residue backbone and/or
sidechain dihedrals according to probability grids derived from the
Brookhaven Protein Database. We have used this method to study
peptide conformations and have successfully adapted it to a number
of important problems in protein modeling, including the prediction of all-atom
protein conformations from C
coordinates alone, and the prediction
of the conformations of protein loops. Here, the method is
applied to a study of the low energy conformations of the peptide
Met-enkephalin.