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Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo

Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo
Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo

Water plays an important role in mediating protein-ligand interactions. Water rearrangement upon a ligand binding or modification can be very slow and beyond typical timescales used in molecular dynamics (MD) simulations. Thus, inadequate sampling of slow water motions in MD simulations often impairs the accuracy of the accuracy of ligand binding free energy calculations. Previous studies suggest grand canonical Monte Carlo (GCMC) outperforms normal MD simulations for water sampling, thus GCMC has been applied to help improve the accuracy of ligand binding free energy calculations. However, in prior work we observed protein and/or ligand motions impaired how well GCMC performs at water rehydration, suggesting more work is needed to improve this method to handle water sampling. In this work, we applied GCMC in 21 protein-ligand systems to assess the performance of GCMC for rehydrating buried water sites. While our results show that GCMC can rapidly rehydrate all selected water sites for most systems, it fails in five systems. In most failed systems, we observe protein/ligand motions, which occur in the absence of water, combine to close water sites and block instantaneous GCMC water insertion moves. For these five failed systems, we both extended our GCMC simulations and tested a new technique named grand canonical nonequilibrium candidate Monte Carlo (GCNCMC). GCNCMC combines GCMC with the nonequilibrium candidate Monte Carlo (NCMC) sampling technique to improve the probability of a successful water insertion/deletion. Our results show that GCNCMC and extended GCMC can rehydrate all target water sites for three of the five problematic systems and GCNCMC is more efficient than GCMC in two out of the three systems. In one system, only GCNCMC can rehydrate all target water sites, while GCMC fails. Both GCNCMC and GCMC fail in one system. This work suggests this new GCNCMC method is promising for water rehydration especially when protein/ligand motions may block water insertion/removal.

Electron density map, Enhanced water sampling, Grand canonical Monte Carlo, Nonequilibrium candidate Monte Carlo
0920-654X
767-779
Ge, Yunhui
d764d1fe-12bb-47e0-a705-5094b6654733
Melling, Oliver J.
ba6757bd-a045-4a1d-b554-0388d188d069
Dong, Weiming
e80aadea-e173-4744-81fe-1ce1d301510c
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Mobley, David L.
bcfb19ab-8b8f-47a4-8e4c-c353dba8b653
Ge, Yunhui
d764d1fe-12bb-47e0-a705-5094b6654733
Melling, Oliver J.
ba6757bd-a045-4a1d-b554-0388d188d069
Dong, Weiming
e80aadea-e173-4744-81fe-1ce1d301510c
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Mobley, David L.
bcfb19ab-8b8f-47a4-8e4c-c353dba8b653

Ge, Yunhui, Melling, Oliver J., Dong, Weiming, Essex, Jonathan W. and Mobley, David L. (2022) Enhancing sampling of water rehydration upon ligand binding using variants of grand canonical Monte Carlo. Journal of Computer-Aided Molecular Design, 36 (10), 767-779. (doi:10.1007/s10822-022-00479-w).

Record type: Article

Abstract

Water plays an important role in mediating protein-ligand interactions. Water rearrangement upon a ligand binding or modification can be very slow and beyond typical timescales used in molecular dynamics (MD) simulations. Thus, inadequate sampling of slow water motions in MD simulations often impairs the accuracy of the accuracy of ligand binding free energy calculations. Previous studies suggest grand canonical Monte Carlo (GCMC) outperforms normal MD simulations for water sampling, thus GCMC has been applied to help improve the accuracy of ligand binding free energy calculations. However, in prior work we observed protein and/or ligand motions impaired how well GCMC performs at water rehydration, suggesting more work is needed to improve this method to handle water sampling. In this work, we applied GCMC in 21 protein-ligand systems to assess the performance of GCMC for rehydrating buried water sites. While our results show that GCMC can rapidly rehydrate all selected water sites for most systems, it fails in five systems. In most failed systems, we observe protein/ligand motions, which occur in the absence of water, combine to close water sites and block instantaneous GCMC water insertion moves. For these five failed systems, we both extended our GCMC simulations and tested a new technique named grand canonical nonequilibrium candidate Monte Carlo (GCNCMC). GCNCMC combines GCMC with the nonequilibrium candidate Monte Carlo (NCMC) sampling technique to improve the probability of a successful water insertion/deletion. Our results show that GCNCMC and extended GCMC can rehydrate all target water sites for three of the five problematic systems and GCNCMC is more efficient than GCMC in two out of the three systems. In one system, only GCNCMC can rehydrate all target water sites, while GCMC fails. Both GCNCMC and GCMC fail in one system. This work suggests this new GCNCMC method is promising for water rehydration especially when protein/ligand motions may block water insertion/removal.

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GCMC_GCNCMC - Accepted Manuscript
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Accepted/In Press date: 15 September 2022
Published date: 6 October 2022
Additional Information: Funding Information: D.L.M. appreciates financial support from the National Institutes of Health (R01GM108889 and R01GM132386). DLM and YG also appreciate financial support from XtalPi. We appreciate the Open Force Field Consortium for its support of the Open Force Field Initiative, which provided software infrastructure used in this work. Funding Information: D.L.M. appreciates financial support from the National Institutes of Health (R01GM108889 and R01GM132386). DLM and YG also appreciate financial support from XtalPi. We appreciate the Open Force Field Consortium for its support of the Open Force Field Initiative, which provided software infrastructure used in this work. Publisher Copyright: © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Keywords: Electron density map, Enhanced water sampling, Grand canonical Monte Carlo, Nonequilibrium candidate Monte Carlo

Identifiers

Local EPrints ID: 472602
URI: http://eprints.soton.ac.uk/id/eprint/472602
ISSN: 0920-654X
PURE UUID: 0bf2aecd-87ae-44f7-a116-18e70e45b1e8
ORCID for Jonathan W. Essex: ORCID iD orcid.org/0000-0003-2639-2746

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Date deposited: 09 Dec 2022 17:42
Last modified: 18 Mar 2024 05:30

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Contributors

Author: Yunhui Ge
Author: Oliver J. Melling
Author: Weiming Dong
Author: David L. Mobley

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