4.7 Article

Rare Event Kinetics from Adaptive Bias Enhanced Sampling

Journal

JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Volume 18, Issue 11, Pages 6500-6509

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.2c00806

Keywords

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Funding

  1. Swiss National Science Foundation through an Early Postdoc.Mobility fellowship

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We introduce a novel enhanced sampling approach called on-the-fly probability enhanced sampling (OPES) flooding, which is used to calculate the kinetics of rare events. The theoretical details of the OPES flooding technique are described, and its application on three systems of increasing complexity is demonstrated. The choice of optimum parameter combinations for obtaining accurate results from limited computational effort is also discussed.
We introduce a novel enhanced sampling approach named on-the-fly probability enhanced sampling (OPES) flooding for calculating the kinetics of rare events from atomistic molecular dynamics simulation. This method is derived from the OPES approach [Invernizzi and Parrinello, J. Phys. Chem. Lett. 2020, 11, 7, 2731-2736], which has been recently developed for calculating converged free energy surfaces for complex systems. In this paper, we describe the theoretical details of the OPES flooding technique and demonstrate the application on three systems of increasing complexity: barrier crossing in a two-dimensional double-well potential, conformational transition in the alanine dipeptide in the gas phase, and the folding and unfolding of the chignolin polypeptide in an aqueous environment. From extensive tests, we show that the calculation of accurate kinetics not only requires the transition state to be bias-free, but the amount of bias deposited should also not exceed the effective barrier height measured along the chosen collective variables. In this vein, the possibility of computing rates from biasing suboptimal order parameters has also been explored. Furthermore, we describe the choice of optimum parameter combinations for obtaining accurate results from limited computational effort.

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