Water networks in complexes between proteins and FDA-approved drugs
Water networks in complexes between proteins and FDA-approved drugs
Water molecules at protein-ligand interfaces are often of significant pharmaceutical interest, owing in part to the entropy which can be released upon the displacement of an ordered water by a therapeutic compound. Protein structures may not, however, completely resolve all critical bound water molecules, or there may be no experimental data available. As such, predicting the location of water molecules in the absence of a crystal structure is important in the context of rational drug design. Grand canonical Monte Carlo (GCMC) is a computational technique that is gaining popularity for the simulation of buried water sites. In this work, we assess the ability of GCMC to accurately predict water binding locations, using a dataset that we have curated, containing 108 unique structures of complexes between proteins and Food and Drug Administration (FDA)-approved small-molecule drugs. We show that GCMC correctly predicts 81.4% of nonbulk crystallographic water sites to within 1.4 Å. However, our analysis demonstrates that the reported performance of water prediction methods is highly sensitive to the way in which the performance is measured. We also find that crystallographic water sites with more protein/ligand hydrogen bonds and stronger electron density are more reliably predicted by GCMC. An analysis of water networks revealed that more than half of the structures contain at least one ligand-contacting water network. In these cases, displacement of a water site by a ligand modification might yield unexpected results if the larger network is destabilized. Cooperative effects between waters should therefore be explicitly considered in structure-based drug design.
Water/chemistry, Ligands, Proteins/chemistry, Computer Simulation, Pharmaceutical Preparations, Binding Sites, Protein Binding
387-396
Samways, Marley L.
75cda5aa-31ef-4f62-9ea3-8655ea55d3fb
Bruce Macdonald, Hannah E.
8e3f96bf-6806-4dc9-bd25-5b7a5325c7a7
Taylor, Richard D.
141004d4-95a6-44f1-93ce-ca36c1b34d61
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
9 January 2023
Samways, Marley L.
75cda5aa-31ef-4f62-9ea3-8655ea55d3fb
Bruce Macdonald, Hannah E.
8e3f96bf-6806-4dc9-bd25-5b7a5325c7a7
Taylor, Richard D.
141004d4-95a6-44f1-93ce-ca36c1b34d61
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Samways, Marley L., Bruce Macdonald, Hannah E., Taylor, Richard D. and Essex, Jonathan W.
(2023)
Water networks in complexes between proteins and FDA-approved drugs.
Journal of Chemical Information and Modeling, 63 (1), .
(doi:10.1021/acs.jcim.2c01225).
Abstract
Water molecules at protein-ligand interfaces are often of significant pharmaceutical interest, owing in part to the entropy which can be released upon the displacement of an ordered water by a therapeutic compound. Protein structures may not, however, completely resolve all critical bound water molecules, or there may be no experimental data available. As such, predicting the location of water molecules in the absence of a crystal structure is important in the context of rational drug design. Grand canonical Monte Carlo (GCMC) is a computational technique that is gaining popularity for the simulation of buried water sites. In this work, we assess the ability of GCMC to accurately predict water binding locations, using a dataset that we have curated, containing 108 unique structures of complexes between proteins and Food and Drug Administration (FDA)-approved small-molecule drugs. We show that GCMC correctly predicts 81.4% of nonbulk crystallographic water sites to within 1.4 Å. However, our analysis demonstrates that the reported performance of water prediction methods is highly sensitive to the way in which the performance is measured. We also find that crystallographic water sites with more protein/ligand hydrogen bonds and stronger electron density are more reliably predicted by GCMC. An analysis of water networks revealed that more than half of the structures contain at least one ligand-contacting water network. In these cases, displacement of a water site by a ligand modification might yield unexpected results if the larger network is destabilized. Cooperative effects between waters should therefore be explicitly considered in structure-based drug design.
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acs.jcim.2c01225
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More information
Accepted/In Press date: 5 December 2022
e-pub ahead of print date: 5 December 2022
Published date: 9 January 2023
Additional Information:
Funding Information:
The authors thank the EPSRC, CCP5, and UCB for funding. M.L.S. was supported by the EPSRC-funded CDT in Next Generation Computational Modelling during this work, under grant EP/L015382/1, and also received support from a CCP5 summer bursary. H.E.B.M. was supported by the EPSRC-funded CDT in Theory and Modelling in Chemical Sciences, under grant EP/L015722/1. The authors thank UCB for computational time and resources, and acknowledge the use of the IRIDIS High Performance Computing Facility and associated support services at the University of Southampton. The authors thank Marcel Verdonk for helpful discussions.
Publisher Copyright:
© 2022 The Authors. Published by American Chemical Society.
Keywords:
Water/chemistry, Ligands, Proteins/chemistry, Computer Simulation, Pharmaceutical Preparations, Binding Sites, Protein Binding
Identifiers
Local EPrints ID: 475722
URI: http://eprints.soton.ac.uk/id/eprint/475722
ISSN: 1549-9596
PURE UUID: fcaa3365-4c90-42b8-ad83-00f9bdab1788
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Date deposited: 27 Mar 2023 16:32
Last modified: 18 Mar 2024 02:39
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Author:
Marley L. Samways
Author:
Hannah E. Bruce Macdonald
Author:
Richard D. Taylor
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