Data Driven Classification of Ligand Unbinding Pathways

TL;DR

This paper introduces an automated, data-driven method using dynamic time-warping to classify ligand unbinding pathways from molecular dynamics simulations, enabling detailed kinetic analysis and outperforming manual methods.

Contribution

The authors develop a novel automated approach for analyzing ligand unbinding pathways, improving classification accuracy and enabling pathway-specific kinetic calculations.

Findings

Accurately classified unbinding pathways using a generic contact/distance descriptor set.

Outperformed manual classification in distinguishing parallel dissociation channels.

Computed ligand dissociation timescales consistent with experimental residence times.

Abstract

Studying the pathways of ligand-receptor binding is essential to understand the mechanism of target recognition by small molecules. The binding free energy and kinetics of protein-ligand complexes can be computed using molecular dynamics (MD) simulations, often in quantitative agreement with experiments. However, only a qualitative picture of the ligand binding/unbinding paths can be obtained through a conventional analysis of the MD trajectories. Besides, the higher degree of manual effort involved in analyzing pathways limits its applicability in large-scale drug discovery. Here we address this limitation by introducing an automated approach for analyzing molecular transition paths with a particular focus on protein-ligand dissociation. Our method is based on the dynamic time-warping (DTW) algorithm, originally designed for speech recognition. We accurately classified molecular trajectories using a very generic descriptor set of contacts or distances. Our approach outperforms manual classification by distinguishing between parallel dissociation channels, within the pathways identified by visual inspection. Most notably, we could compute exit-path-specific ligand-dissociation kinetics. The unbinding timescale along the fastest path agrees with the experimental residence time, providing a physical interpretation to our entirely data-driven protocol. In combination with appropriate enhanced sampling algorithms, this technique can be used for the initial exploration of ligand-dissociation pathways as well as for calculating path-specific thermodynamic and kinetic properties.