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

• Published in: Journal of computational chemistry 2022-06, Vol.43 (17), p.1186-1200
• Main Authors: Gupta, Abhinav, Verma, Shivani, Javed, Ramsha, Sudhakar, Suraj, Srivastava, Saurabh, Nair, Nisanth N.
• Format: Article
• Language: English
• Subjects: Alanine; alanine pentapeptide; alanine tripeptide; artificial neural network; Artificial neural networks; Bias; Computation; deacylation; Degrees of freedom; Energy; Entropy; Exploration; Free energy; free energy calculations; Mathematical analysis; Molecular dynamics; Molecular Dynamics Simulation; parallel bias Metadynamics; Sampling methods; Temperature; temperature accelerate sliced sampling; Thermodynamics; umbrella sampling; weighted histogram analysis; β‐lactamase
• ISSN: 0192-8651; 1096-987X
• DOI: 10.1002/jcc.26882

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. A powerful enhanced sampling technique to efficiently explore high‐dimensional free energy landscapes is proposed here. By a combination of parallel‐biasing, temperature acceleration, and restrained sampling of collective variables, the method can explore high‐dimensional surfaces in an extremely efficient manner. The method can sample more than 50% of the eight‐dimensional free energy landscape of alanine‐pentapeptide if all the 33 umbrella windows run for just 2 ns, and nearly the whole surface in just 20 ns.