Abstract
Hybrid simulation procedures which combine molecular dynamics with Monte Carlo are attracting increasing attention as tools for improving the sampling efficiency in molecular simulations. In particular, encouraging results have been reported for nonequilibrium candidate protocols, in which a Monte Carlo move is applied gradually, and interleaved with a process that equilibrates the remaining degrees of freedom. Although initial studies have uncovered a substantial potential of the method, its practical applicability for sampling structural transitions in macromolecules remains incompletely understood. Here, we address this issue by systematically investigating the efficiency of the nonequilibrium candidate Monte Carlo on the sampling of rotameric distributions of two peptide systems at atomistic resolution both in vacuum and explicit solvent. The studied systems allow us to directly probe the efficiency with which a single or a few slow degrees of freedom can be driven between well-separated free-energy minima and to explore the sensitivity of the method toward the involved free parameters. In line with results on other systems, our study suggests that order-of-magnitude gains can be obtained in certain scenarios but also identifies challenges that arise when applying the procedure in explicit solvent.
Highlights
The Markov chain Monte Carlo (MC) and molecular dynamics (MD) approaches to molecular simulation are thermodynamically equivalent but use fundamentally different sampling strategies
To understand how efficiently an nonequilibrium candidate Monte Carlo (NCMC) move can relax the internal DoFs of peptides, we initially conducted simulations in vacuum, eliminating the complications associated with onpathway water molecules
Two opposite trends are observed from the acceptance curve: the leftmost part shows a negative correlation with increasing NCMC steps, whereas the rightmost part displays a positive correlation
Summary
The Markov chain Monte Carlo (MC) and molecular dynamics (MD) approaches to molecular simulation are thermodynamically equivalent but use fundamentally different sampling strategies. MC can make dramatic changes to the molecule in each iteration, allowing for faster convergence of thermodynamic quantities but provides no (or little) information about dynamics. This would suggest that MC be the preferred tool for calculating thermodynamic averages, this is not generally true. The problem with MC is that it has the potential to improve performance by making large structural updates, it is not trivial to construct such moves in practice This is especially true for explicit solvent simulations, where any large modification in the solute structure is almost certain to fail due to overlap with solvent molecules. Even in an implicit solvent setting, the design of efficient moves requires proper handling of correlations between relevant degrees of freedom (DoFs) while maintaining detailed balance, which can be challenging[1,2] and is not necessarily transferable between systems
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