grand: a Python module for grand canonical water sampling in OpenMM
grand: a Python module for grand canonical water sampling in OpenMM
Networks of water molecules can play a critical role at the protein–ligand interface and can directly influence drug–target interactions. Grand canonical methods aid in the sampling of these water molecules, where conventional molecular dynamics equilibration times are often long, by allowing waters to be inserted and deleted from the system, according to the chemical potential. Here, we present our open source Python module, grand (https://github.com/essex-lab/grand), which allows molecular dynamics simulations to be performed in conjunction with grand canonical Monte Carlo sampling, using the OpenMM simulation engine. We demonstrate the accuracy of this module by reproducing the density of bulk water observed from constant pressure simulations. Application of this code to the bovine pancreatic trypsin inhibitor protein reproduces three buried crystallographic water sites that are poorly sampled using conventional molecular dynamics.
Monte Carlo simulations, computer simulation, molecules, thermodynamic properties, water
4436-4441
Samways, Marley Luke
75cda5aa-31ef-4f62-9ea3-8655ea55d3fb
Bruce Macdonald, Hannah
8e3f96bf-6806-4dc9-bd25-5b7a5325c7a7
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
26 October 2020
Samways, Marley Luke
75cda5aa-31ef-4f62-9ea3-8655ea55d3fb
Bruce Macdonald, Hannah
8e3f96bf-6806-4dc9-bd25-5b7a5325c7a7
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Samways, Marley Luke, Bruce Macdonald, Hannah and Essex, Jonathan W.
(2020)
grand: a Python module for grand canonical water sampling in OpenMM.
Journal of Chemical Information and Modeling, 60 (10), .
(doi:10.1021/acs.jcim.0c00648).
Abstract
Networks of water molecules can play a critical role at the protein–ligand interface and can directly influence drug–target interactions. Grand canonical methods aid in the sampling of these water molecules, where conventional molecular dynamics equilibration times are often long, by allowing waters to be inserted and deleted from the system, according to the chemical potential. Here, we present our open source Python module, grand (https://github.com/essex-lab/grand), which allows molecular dynamics simulations to be performed in conjunction with grand canonical Monte Carlo sampling, using the OpenMM simulation engine. We demonstrate the accuracy of this module by reproducing the density of bulk water observed from constant pressure simulations. Application of this code to the bovine pancreatic trypsin inhibitor protein reproduces three buried crystallographic water sites that are poorly sampled using conventional molecular dynamics.
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e-pub ahead of print date: 24 August 2020
Published date: 26 October 2020
Additional Information:
Funding Information:
The authors thank the EPSRC for funding. M.L.S. is supported by the EPSRC-funded CDT in Next Generation Computational Modelling, under Grant EP/L015382/1. The authors acknowledge the use of the IRIDIS High Performance Computing Facility and associated support services at the University of Southampton in the completion of this work.
Publisher Copyright:
© 2020 American Chemical Society.
Keywords:
Monte Carlo simulations, computer simulation, molecules, thermodynamic properties, water
Identifiers
Local EPrints ID: 444466
URI: http://eprints.soton.ac.uk/id/eprint/444466
ISSN: 1549-9596
PURE UUID: 8396218b-f6af-43f9-b24e-b401298b61ef
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Date deposited: 20 Oct 2020 16:32
Last modified: 17 Mar 2024 05:58
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Contributors
Author:
Marley Luke Samways
Author:
Hannah Bruce Macdonald
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