Understanding ligand dissociation mechanisms at an atomic resolution is a highly desired but difficult to achieve objective in experiments as well as in computer simulations. Structural details of the dissociation process are in general hard to capture in experiments, while the relevant time scales are far beyond molecular dynamics (MD) simulations even with the most powerful supercomputers. An even bigger challenge is predicting and explaining why specific mutations often far from the binding site lead to tremendous variations in the binding affinity/kinetics. As such, many different specialized enhanced sampling methods have been proposed that make it possible to efficiently calculate the dissociation mechanisms in biomolecular systems. One such method is infrequent metadynamics. Here we demonstrate how this method when coupled with recent methods for on-the-fly reaction coordinate discovery SGOOP and RAVE can lead to accurate estimates of ligand residence times and associated dissociation pathways in a fairly automated manner suited for high throughput applications. The use of the automated reaction coordinate discovery methods reduce drastically the need for prior human bias in the biased metadynamics simulations, and should be applicable to more generic biased simulations as well. We benchmark our results against long unbiased MD simulations for a small FKBP protein and against <i>in vitro</i> experiments in protein kinases and PreQ<sub>1 </sub>riboswitches. We also demonstrate how this method can be used to predict critical resistance mutations in the host.

Drug dissociation pathways, kinetics and resistance mutations from bias-free biased simulations

ACS Spring Meeting 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.