  The dissociation of ligands from proteins and other biomacromolecules occurs
over a wide range of timescales. For most pharmaceutically relevant inhibitors,
these timescales are far beyond those that are accessible by conventional
molecular dynamics (MD) simulation. Consequently, to explore ligand egress
mechanisms and compute dissociation rates, it is necessary to enhance the
sampling of ligand unbinding. Random Acceleration MD (RAMD) is a simple method
to enhance ligand egress from a macromolecular binding site, which enables the
exploration of ligand egress routes without prior knowledge of the reaction
coordinates. Furthermore, the tauRAMD procedure can be used to compute the
relative residence times of ligands. When combined with a machine-learning
analysis of protein-ligand interaction fingerprints (IFPs), molecular features
that affect ligand unbinding kinetics can be identified. Here, we describe the
implementation of RAMD in GROMACS 2020, which provides significantly improved
computational performance, with scaling to large molecular systems. For the
automated analysis of RAMD results, we developed MD-IFP, a set of tools for the
generation of IFPs along unbinding trajectories and for their use in the
exploration of ligand dynamics. We demonstrate that the analysis of ligand
dissociation trajectories by mapping them onto the IFP space enables the
characterization of ligand dissociation routes and metastable states. The
combined implementation of RAMD and MD-IFP provides a computationally efficient
and freely available workflow that can be applied to hundreds of compounds in a
reasonable computational time and will facilitate the use of tauRAMD in drug
design.
