Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules

TLDR

Molecular dynamics is limited to nanosecond timescales, leaving many biomolecular processes trapped in high‑energy minima that require rare events to transition between basins. The authors propose a robust bias potential that enables accelerated molecular dynamics to overcome high energy barriers without prior knowledge of wells or saddle points. The method adds a bias potential that mirrors the landscape shape, enhancing escape rates from wells and allowing proper sampling of minima while extending simulation timescales. The approach samples biomolecular conformational space more efficiently than conventional MD and converges to the correct canonical distribution.

Abstract

Many interesting dynamic properties of biological molecules cannot be simulated directly using molecular dynamics because of nanosecond time scale limitations. These systems are trapped in potential energy minima with high free energy barriers for large numbers of computational steps. The dynamic evolution of many molecular systems occurs through a series of rare events as the system moves from one potential energy basin to another. Therefore, we have proposed a robust bias potential function that can be used in an efficient accelerated molecular dynamics approach to simulate the transition of high energy barriers without any advance knowledge of the location of either the potential energy wells or saddle points. In this method, the potential energy landscape is altered by adding a bias potential to the true potential such that the escape rates from potential wells are enhanced, which accelerates and extends the time scale in molecular dynamics simulations. Our definition of the bias potential echoes the underlying shape of the potential energy landscape on the modified surface, thus allowing for the potential energy minima to be well defined, and hence properly sampled during the simulation. We have shown that our approach, which can be extended to biomolecules, samples the conformational space more efficiently than normal molecular dynamics simulations, and converges to the correct canonical distribution.