UCSF

A multiscale, modular approach to protein sampling with novel Monte Carlo algorithms is is presented. The systems studied use an all atom forcefield with a Generalized Born implicit solvation model. The multiscale approach addresses 3 degrees of freedom: 1) the solvation terms, 2) the sidechain degrees of freedom, and 3) the backbone degrees of freedom. The goal of the work is to identify the special design issues surrounding these degrees of freedom, and create an overall sampling approach that optimizes all of these, while generating coherent trajectories that obey detailed balance. This design is expected to sample challenging, highly constrained systems that may be exceedingly difficult using standard molecular dynamics methods. The work presents present developments with regard to algorithmic approaches, design features, and applications to protein systems. Future directions in these areas are also discussed.