| Because of the rapid growth of interest in computational biomolecular simulations, there is a great need for methods that address the problems of conformational sampling, calculations of long-range forces, and multiple timescales. We propose a Monte Carlo sampling algorithm that is similar in spirit to multiple time-scale molecular dynamics, in which the short-range and long-range forces are updated on different time scales. The new scheme is called multiple Markovian time-step Monte Carlo (MTS-MC) and is based on the separation of the potential interactions into two additive parts. In addition, we present a combination of the particle-particle particle-mesh Ewald sum and multiple-time-step Langevin algorithms, based on an implementation by Zhou, Harder, Xu and Berne, which proves to be superior to all the other algorithms of the same nature. |
