Kinetics of Barrier Crossing Events from Temperature Accelerated Sliced Sampling Simulations

Abstract

Temperature-accelerated sliced sampling (TASS) is a well-established enhanced sampling method that facilitates exhaustive exploration of high-dimensional collective variable (CV) space through directed sampling employing a combination of umbrella restraining biases, metadynamics biases, and temperature acceleration of CVs. In this work, we broaden the applicability of TASS by introducing a protocol for computing rate constants of barrier crossing events. The challenge addressed here is to recover kinetics from free energy data computed from different slices of the TASS simulation. The proposed protocol utilizes artificial neural networks based representation of high-dimensional free energy landscapes, and Infrequent Metadynamics. We demonstrate the accuracy of the approach by obtaining rate constants for the conformational change of alanine dipeptide in vacuo, the unbinding of benzamidine from trypsin, and the unbinding of aspirin from $\beta$-cyclodextrin.