Finding Multiple Reaction Pathways of Ligand Unbinding

Abstract

Searching for reaction pathways describing rare events in large systems presents a long-standing challenge in chemistry and physics. Incorrectly computed reaction pathways result in the degeneracy of microscopic configurations and inability to sample hidden energy barriers. To this aim, we present a general enhanced sampling method to find multiple diverse reaction pathways of ligand unbinding through non-convex optimization of a loss function describing ligand-protein interactions. The method successfully overcomes large energy barriers using an adaptive bias potential, and constructs possible reaction pathways along transient tunnels without the initial guesses of intermediate or final states, requiring crystallographic information only. We examine the method on the T4 lysozyme L99A mutant which is often used as a model system to study ligand binding to proteins, provide a previously unknown reaction pathway, and show that using the bias potential and the tunnel widths it is possible to capture heterogeneity of the unbinding mechanisms between the found transient protein tunnels.

References in corpus (5)

• Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
• A Variational Approach to Enhanced Sampling and Free Energy Calculations
• Frequency adaptive metadynamics for the calculation of rare-event kinetics
• Unfolding Hidden Barriers by Active Enhanced Sampling
• Finding Multiple Reaction Pathways of Ligand Unbinding

Cited by in corpus (10)

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• Ligand unbinding mechanisms and kinetics for T4 lysozyme mutants from tauRAMD simulations
• Finding Multiple Reaction Pathways of Ligand Unbinding
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• Ligand unbinding pathway and mechanism analysis assisted by machine learning and graph methods
• Path separation of dissipation-corrected targeted molecular dynamics simulations of protein-ligand unbinding
• Free energy along drug-protein binding pathways interactively sampled in virtual reality
• maze: Heterogeneous Ligand Unbinding along Transient Protein Tunnels
• Learning protein-ligand unbinding pathways via single-parameter community detection
• More sophisticated is not always better: comparison of similarity measures for unsupervised learning of pathways in biomolecular simulations