Abstract

Simulating drug binding and unbinding is a challenge, as the rugged energy landscapes that separate bound and unbound states require extensive sampling that consumes significant computational resources. Here, we describe the use of interactive molecular dynamics in virtual reality (iMD-VR) as an accurate low-cost strategy for flexible protein-ligand docking. We outline an experimental protocol which enables expert iMD-VR users to guide ligands into and out of the binding pockets of trypsin, neuraminidase, and HIV-1 protease, and recreate their respective crystallographic protein-ligand binding poses within 5–10 minutes. Following a brief training phase, our studies shown that iMD-VR novices were able to generate unbinding and rebinding pathways on similar timescales as iMD-VR experts, with the majority able to recover binding poses within 2.15 Å RMSD of the crystallographic binding pose. These results indicate that iMD-VR affords sufficient control for users to carry out the detailed atomic manipulations required to dock flexible ligands into dynamic enzyme active sites and recover crystallographic poses, offering an interesting new approach for simulating drug docking and generating binding hypotheses.

Highlights

  • To the best of our knowledge, this study represents the first time that iMD-VR has been extended to studies of complex protein-ligand binding and unbinding dynamics

  • Our results show that expert iMD-VR users are able to manipulate protein-ligand systems to sample bound and unbound states

  • We assessed the extent to which novice iMD-VR uses were able to sample binding and unbinding pathways using a training phase followed by a testing phase

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Summary

Introduction

[1] Whilst the molecular dynamics (MD) approach to biomolecular simulation [2,3,4,5] and protein ligand-binding [6,7,8,9,10,11,12] has exploded in recent years, [13] the computational cost of MD-based approaches remains significant–a result of the fact that proteins are high-dimensional systems characterized by many local energy minima separated by a rugged landscape. Computational researchers across a wide range of fields are becoming increasingly aware of their responsibility to explore low-cost simulation methodologies whose energy and hardware demands are environmentally sustainable. [1] Whilst the molecular dynamics (MD) approach to biomolecular simulation [2,3,4,5] and protein ligand-binding [6,7,8,9,10,11,12] has exploded in recent years, [13] the computational cost of MD-based approaches remains significant–a result of the fact that proteins are high-dimensional systems characterized by many local energy minima separated by a rugged landscape.

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