Free energy along drug-protein binding pathways interactively sampled in virtual reality

Helen M Deeks, Kirils Zinovjev, Jonathan Barnoud, Adrian J Mulholland, Marc W Van der Kamp*, David R. Glowacki*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)

Abstract

We describe a two-step approach for combining interactive molecular dynamics in virtual reality (iMD-VR) with free energy (FE) calculation to explore the dynamics of biological processes at the molecular level. We refer to this combined approach as iMD-VR-FE. Stage one involves using a state-of-the-art ‘human-in-the-loop’ iMD-VR framework to generate a diverse range of protein–ligand unbinding pathways, benefitting from the sophistication of human spatial and chemical intuition. Stage two involves using the iMD-VR-sampled pathways as initial guesses for defining a path-based reaction coordinate from which we can obtain a corresponding free energy profile using FE methods. To investigate the performance of the method, we apply iMD-VR-FE to investigate the unbinding of a benzamidine ligand from a trypsin protein. The binding free energy calculated using iMD-VR-FE is similar for each pathway, indicating internal consistency. Moreover, the resulting free energy profiles can distinguish energetic differences between pathways corresponding to various protein–ligand conformations (e.g., helping to identify pathways that are more favourable) and enable identification of metastable states along the pathways. The two-step iMD-VR-FE approach offers an intuitive way for researchers to test hypotheses for candidate pathways in biomolecular systems, quickly obtaining both qualitative and quantitative insight.
Original languageEnglish
Article number16665 (2023)
Number of pages9
JournalScientific Reports
Volume13
Issue number1
DOIs
Publication statusPublished - 4 Oct 2023

Bibliographical note

Funding Information:
K.Z. and M.W.vdK. acknowledge support by the Biotechnology and Biological Sciences Research Council (BB/L018756/1 and BB/M026280/1), the Engineering and Physical Sciences Research Council (EP/V011421/1) and the UK Catalysis Hub (EPSRC grant EP/M013219/1). K.Z. also acknowledges the Maria Zambrano contract at the University of Valencia funded by Ministerio de Universidades (BOE-A-2021-6391). H.M.D. thanks the Engineering and Physical Sciences Research Council (EPSRC) for a PhD studentship. H.M.D. and A.J.M. acknowledge support by the Engineering and Physical Sciences Research Council and UK Catalysis Hub (EP/R026939/1, EP/R026815/1, EP/R026645/1, and EP/R027129/1). AJM acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (PREDACTED Advanced Grant, Grant Agreement No.: 101021207) and from EPSRC for CCP-BioSim (EP/M022609/1). J.B. acknowledges funding from the EPSRC (Programme Grant EP/P021123/1) and from the European Research Council under the European Union’s Horizon 2020 research and innovation programe through consolidator grant NANOVR 866559. DRG acknowledges support from the European Research Council under the European Union’s Horizon 2020 research and innovation programme through consolidator Grant NANOVR 866559, and also thanks the Axencia Galega de Innovación for funding as an Investigador Distinguido through the Oportunius Program. J.B. and DRG received support from the Xunta de Galicia (Centro de investigación de Galicia accreditation 2019–2022, ED431G-2019/04) and the European Union (European Regional Development Fund—ERDF). We thank the Advanced Computing Research Centre of the University of Bristol for computational facilities.

Publisher Copyright:
© 2023, Springer Nature Limited.

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