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Exploration of high dimensional free energy landscapes by a combination of temperature‐accelerated sliced sampling and parallel biasing

Temperature‐accelerated sliced sampling (TASS) is an enhanced sampling method for achieving accelerated and controlled exploration of high‐dimensional free energy landscapes in molecular dynamics simulations. With the aid of umbrella bias potentials, the TASS method realizes a controlled exploration and divide‐and‐conquer strategy for computing high‐dimensional free energy surfaces. In TASS, diffusion of the system in the collective variable (CV) space is enhanced with the help of metadynamics bias and elevated‐temperature of the auxiliary degrees of freedom (DOF) that are coupled to the CVs. Usually, a low‐dimensional metadynamics bias is applied in TASS. In order to further improve the performance of TASS, we propose here to use a highdimensional metadynamics bias, in the same form as in a parallel bias metadynamics scheme. Here, a modified reweighting scheme, in combination with artificial neural network is used for computing unbiased probability distribution of CVs and projections of high‐dimensional free energy surfaces. We first validate the accuracy and efficiency of our method in computing the four‐dimensional free energy landscape for alanine tripeptide in vacuo. Subsequently, we employ the approach to calculate the eight‐dimensional free energy landscape of alanine pentapeptide in vacuo. Finally, the method is applied to a more realistic problem wherein we compute the broad four‐dimensional free energy surface corresponding to the deacylation of a drug molecule which is covalently complexed with a β‐lactamase enzyme. We demonstrate that using parallel bias in TASS improves the efficiency of exploration of high‐dimensional free energy landscapes.