Thermodynamically Optimized Machine-learned Reaction Coordinates for Hydrophobic Ligand Dissociation

TL;DR

This paper introduces a deep learning-based method to identify thermodynamically optimized reaction coordinates for hydrophobic ligand unbinding, revealing the roles of energy and entropy in the process.

Contribution

It develops a modified deep learning framework to learn reaction coordinates that incorporate thermodynamic considerations for hydrophobic ligand dissociation.

Findings

Transition driven mainly by solvation effects

Reaction coordinates reveal entropic and enthalpic contributions

Framework applicable to different ligand sizes

Abstract

Ligand unbinding is mediated by the free energy change, which has intertwined contributions from both energy and entropy. It is important but not easy to quantify their individual contributions. We model hydrophobic ligand unbinding for two systems, a methane particle and a C60 fullerene, both unbinding from hydrophobic pockets in all-atom water. By using a modified deep learning framework, we learn a thermodynamically optimized reaction coordinate to describe hydrophobic ligand dissociation for both systems. Interpretation of these reaction coordinates reveals the roles of entropic and enthalpic forces as ligand and pocket sizes change. Irrespective of the contrasting roles of energy and entropy, we also find that for both the systems the transition from the bound to unbound states is driven primarily by solvation of the pocket and ligand, independent of ligand size. Our framework thus gives useful thermodynamic insight into hydrophobic ligand dissociation problems that are otherwise difficult to glean.