We tested the dihedral probability grid Monte Carlo (DPG-MC) methodology to determine optimal conformations of polypeptides by applying it to predict the low energy ensemble for two peptides whose solution NMR structures are known: integrin receptor peptide (YGRGDSP, Type I1 P-turn) and S3 a-helical peptide (YMSEDEL KAAEAAFKRHGPT).DPG-MC involves importance sampling, local random stepping in the vicinity of a current local minima, and Metropolis sampling criteria for acceptance or rejection of new structures. Internal coordinate values are based on side-chain-specific dihedral angle probability distributions (from analysis of high-resolution protein crystal structures). Important features of DPG-MC are: (1) Each DPG-MC step selects the torsion angles (6, $, x) from a discrete grid that are then applied directly to the structure. The torsion angle increments can be taken as S = 60, 30, 15, 10, or 5", depending on the application. (2) DPG-MC utilizes a temperature-dependent probability function ( P ) in conjunction with Metropolis sampling to accept or reject new structures.For each peptide, we found close agreement with the known structure for the low energy conformational ensemble located with DPG-MC. This suggests that DPG-MC will be useful for predicting conformations of other polypeptides.Keywords: computational chemistry; importance sampling; Monte Carlo; peptide conformation; protein conformation; protein folding A full understanding of protein function requires knowledge of the three-dimensional structure. Unfortunately, experimentally determined structures for most proteins are unavailable. Consequently, it is essential to develop approaches for predicting secondary and tertiary structures of proteins.In the past decade, several approaches to protein structure prediction have evolved, including: (1) lattice search methods