Open Binding Pose Metadynamics: An Effective Approach for the Ranking of Protein–Ligand Binding Poses

Predicting the correct
pose of a ligand binding to a protein and
its associated binding affinity is of great importance in computer-aided
drug discovery. A number of approaches have been developed to these
ends, ranging from the widely used fast molecular docking to the computationally
expensive enhanced sampling molecular simulations. In this context,
methods such as coarse-grained metadynamics and binding pose metadynamics
(BPMD) use simulations with metadynamics biasing to probe the binding
affinity without trying to fully converge the binding free energy
landscape in order to decrease the computational cost. In BPMD, the
metadynamics bias perturbs the ligand away from the initial pose.
The resistance of the ligand to this bias is used to calculate a stability
score. The method has been shown to be useful in reranking predicted
binding poses from docking. Here, we present OpenBPMD, an open-source
Python reimplementation and reinterpretation of BPMD. OpenBPMD is
powered by the OpenMM simulation engine and uses a revised scoring
function. The algorithm was validated by testing it on a wide range
of targets and showing that it matches or exceeds the performance
of the original BPMD. We also investigated the role of accurate water
positioning on the performance of the algorithm and showed how the
combination with a grand-canonical Monte Carlo algorithm improves
the accuracy of the predictions.