A Sinking Approach to Explore Arbitrary Areas in Free Energy Landscapes.

Abstract

To address the time-scale limitations in molecular dynamics (MD) simulations, numerous enhanced sampling methods have been developed to expedite the exploration of complex free energy landscapes. A commonly employed approach accelerates the sampling of degrees of freedom associated with predefined collective variables (CVs), which typically tend to traverse the entire CV range. However, in many scenarios, the focus of interest is on specific regions within the CV space. In this paper, we introduce a novel "sinking" approach that enables enhanced sampling of arbitrary areas within the CV space. This method, referred to as SinkMeta, "sinks" the interior bias potential to create a restraining potential "cliff" at the grid edges, thus confining the exploration of CVs in MD simulations to a predefined area. SinkMeta requires minimal sampling steps to estimate the free energy landscape for CV subspaces of various shapes and dimensions, offering an efficient and flexible solution for sampling minimum free energy paths in high-dimensional spaces. We believe that SinkMeta will pioneer a new paradigm for sampling partial phase spaces and provide an efficient and straightforward way to study the interaction of drugs with biomolecules such as proteins and DNA in MD simulations.