Improving the Exploration of High Dimensional Free Energy Landscape by a Combination of Temperature Accelerated Sliced Sampling and Parallel Biasing

Abstract: Biased sampling methods such as the Temperature Accelerated Sliced Sampling (TASS), which can explore high dimensional collective variable (CV) space, is of great interest in free energy calculations. Such methods can efficiently sample configurational space even when a large number of CVs for biasing are used while many conventional methods are limited to two or three CVs. In this paper, we propose a modification to the TASS method, called Parallel Bias TASS or PBTASS, wherein a multidimensional parallel metadynamics bias is incorporated on a selected set of CVs. The corresponding time-dependent reweighting equations are derived, and the method is benchmarked. In particular, we compare the accuracy and efficiency of PBTASS with various methods viz. standard TASS, Temperature Accelerated Molecular Dynamics/driven-Adiabatic Free Energy Dynamics, and Parallel Bias Metadynamics. We demonstrate the capability of the PBTASS method by reconstructing the eight-dimensional free energy surface of alanine pentapeptide in vacuo from a 25 ns long trajectory. Free energy barriers and free energies of high energy saddle points on the high dimensional free energy landscape of this system are reported.