Abstract
Coarse grained simulation methods allow the simulation of large biomolecular systems with less computational expense than the corresponding all-atom simulations. The savings come from two sources: faster computation due to a reduced number of particles, and improved sampling due to a smoother free energy surface. Many commonly used coarse-grained models suffer from serious limitations, such as being unable to properly model protein secondary structure without the addition of unphysical restraints. We have constructed a novel coarse-grained Monte Carlo method based on dividing proteins into nearly rigid fragments, constructing distance and orientation-dependent tables of the interaction energies between those fragments, and applying potential energy smoothing techniques to those tables. Preliminary results on peptides indicate that the new method is able to preserve α-helices without additional restraints. In addition, when sufficient smoothing is applied, the new method also shows an improvement in sampling per unit computation time compared to Monte Carlo simulation without tabulation or atomistic molecular dynamics.
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