Accelerating Rare Dissociative Processes in Biomolecules Using Selectively Scaled MD Simulations

Indrajit Deb, Aaron T. Frank (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

Molecular dynamics (MD) simulations can be a powerful tool for modeling complex dissociative processes such as ligand unbinding. However, many biologically relevant dissociative processes occur on timescales that far exceed the timescales of typical MD simulations. Here, we implement and apply an enhanced sampling method in which specific energy terms in the potential energy function are selectively "scaled" to accelerate dissociative events during simulations. Using ligand unbinding as an example of a complex dissociative process, we selectively scaled up ligand-water interactions in an attempt to increase the rate of ligand unbinding. After applying our selectively scaled MD (ssMD) approach to several cyclin-dependent kinase-inhibitor complexes, we discovered that we could accelerate ligand unbinding, thereby allowing, in some cases, unbinding events to occur within as little as 2 ns. Moreover, we found that we could make realistic estimates of the initial unbinding times (τunbindsim) as well as the accompanying change in free energy (ΔGsim) of the inhibitors from our ssMD simulation data. To accomplish this, we employed a previously described Kramers'-based rate extrapolation method and a newly described free energy extrapolation method. Because our ssMD approach is general, it should find utility as an easy-to-deploy, enhanced sampling method for modeling other dissociative processes.

Original languageEnglish
Pages (from-to)5817-5828
Number of pages12
JournalJournal of Chemical Theory and Computation
Volume15
Issue number11
Early online date11 Sept 2019
DOIs
Publication statusPublished - 12 Nov 2019

ASJC Scopus subject areas

  • Computer Science Applications
  • Physical and Theoretical Chemistry

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