Accelerate Sampling in Atomistic Energy Landscapes using Topology-Based Coarse-Grained Models

Abstract

Successful simulations of biomolecules require sufficient sampling of the relevant conformational space. This has remained a major challenge due to large conformational space and significant energy barriers, especially with atomistic force fields. Temperature replica exchange (REX) has emerged as a powerful and popular technique for enhanced sampling. Yet, efficiency of temperature REX can be severely limited by the presence of sharp cooperative conformational transitions as well as due to large entropic barriers frequently associated with folding transitions. A coarse-grained representation is often necessary to significantly reduce the conformational space and allows faster sampling of reversible conformational transitions, albeit at the expense of reduced detail and accuracy. Here, we describe a multi-scale enhanced sampling (MSES) method that directly couples topology-based coarse-grained protein models with atomistic ones to accelerate the sampling of complex and rough atomistic energy landscapes. The bias from the coupling potential is completely removed by performing Hamiltonian/temperature REX, allowing one to benefit simultaneously from faster transitions of the coarse-grained model and the accuracy of the atomistic force field. The method has been applied to implicit solvent simulations of several peptides and small proteins including protein GB1, protein A and villin headpiece. The results demonstrate that MSES dramatically increases the number of folding/unfolding transitions sampled and improve the convergence of various thermodynamic properties of interest in all cases. Importantly, this method is simple and fully scalable to larger and more complex systems.