A Sinking Approach to Explore Arbitrary Areas in Free Energy Landscapes

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.