Multisecond ligand dissociation dynamics from atomistic simulations

Abstract

Coarse-graining of fully atomistic molecular dynamics simulations is a long-standing goal in order to allow the description of processes occurring on biologically relevant timescales. For example, the prediction of pathways, rates and rate-limiting steps in protein-ligand unbinding is crucial for modern drug discovery. To achieve the enhanced sampling, we first perform dissipation-corrected targeted molecular dynamics simulations, which yield free energy and friction profiles of the molecular process under consideration. In a second step, we use these fields to perform temperature-boosted Langevin simulations which account for the desired molecular kinetics occurring on multisecond timescales and beyond. Adopting the dissociation of solvated sodium chloride as well as trypsin-benzamidine and Hsp90-inhibitor protein-ligand complexes as test problems, we are able to reproduce rates from molecular dynamics simulation and experiments within a factor of 2-20, and dissociation constants within a factor of 1-4. Analysis of the friction profiles reveals that binding and unbinding dynamics are mediated by changes of the surrounding hydration shells in all investigated systems.

arXiv commentThis unedited earlier version of the manuscript may be downloaded for personal use only. The final manuscript was published in Nature Communications 11, 2918 (2020) as open access publication and is available at https://www.nature.com/articles/s41467-020-16655-1

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• Mechanical feedback controls the emergence of dynamical memory in growing tissue monolayers
• Predicting ion channel conductance via dissipation-corrected targeted molecular dynamics and Langevin equation simulations
• 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
• Learning protein-ligand unbinding pathways via single-parameter community detection
• Data-driven Langevin modeling of nonequilibrium processes
• Molecular origin of driving-dependent friction in fluids
• Nonequilibrium friction and free energy estimates for kinetic coarse-graining -- Driven particles in responsive media
• More sophisticated is not always better: comparison of similarity measures for unsupervised learning of pathways in biomolecular simulations