Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

Accurately predicting protein-ligand binding affinities and binding modes
is a major goal in computational chemistry, but even the prediction of
ligand binding modes in proteins poses major challenges. Here, we focus on
solving the binding mode prediction problem for rigid fragments. That is,
we focus on computing the dominant placement, conformation, and
orientations of a relatively rigid, fragment-like ligand in a receptor,
and the populations of the multiple binding modes which may be relevant.
This problem is important in its own right, but is even more timely given
the recent success of alchemical free energy calculations. Alchemical
calculations are increasingly used to predict binding free energies of
ligands to receptors. However, the accuracy of these calculations is
dependent on proper sam- pling of the relevant ligand binding modes.
Unfor- tunately, ligand binding modes may often be uncer- tain, hard to
predict, and/or slow to interconvert on simulation timescales, so proper
sampling with cur- rent techniques can require prohibitively long sim-
ulations. We need new methods which dramatically improve sampling of
ligand binding modes. Here, we develop and apply a nonequilibrium
candidate Monte Carlo (NCMC) method to improve sampling of ligand binding
modes. In this technique, the ligandis rotated and subsequently allowed to
relax in its new position through alchemical perturbation before accepting
or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo
move. When applied to a T4 lysozyme model binding system, this NCMC method
shows over two orders of magnitude improvement in binding mode sampling
efficiency compared to a brute force molecular dynamics sim- ulation. This
is a first step towards applying this methodology to
pharmaceutically-relevant binding of fragments and, eventually, drug-like
molecules. We are making this approach available via our new Binding Modes
of Ligands using Enhanced Sampling (BLUES) package which is freely
available on GitHub. This dataset acts as additional supporting info
for 10.1021/acs.jpcb.7b11820 and includes the full trajectories
as well as the assorted scripts used to produce the data found in the
paper.