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Water network perturbation in ligand binding: Adenosine A2AAntagonists as a case study

Water network perturbation in ligand binding: Adenosine A2AAntagonists as a case study
Water network perturbation in ligand binding: Adenosine A2AAntagonists as a case study
Recent efforts in the computational evaluation of the thermodynamic properties of water molecules have resulted in the development of promising new in silico methods to evaluate the role of water in ligand binding. These methods include WaterMap, SZMAP, GRID/CRY probe, and Grand Canonical Monte Carlo simulations. They allow the prediction of the position and relative free energy of the water molecule in the protein active site and the analysis of the perturbation of an explicit water network (WNP) as a consequence of ligand binding. We have for the first time extended these approaches toward the prediction of kinetics for small molecules and of relative free energy of binding with a focus on the perturbation of the water network and application to large diverse data sets. Our results support a qualitative correlation between the residence time of 12 related triazine adenosine A2A receptor antagonists and the number and position of high energy trapped solvent molecules. From a quantitative viewpoint, we successfully applied these computational techniques as an implicit solvent alternative, in linear combination with a molecular mechanics force field, to predict the relative ligand free energy of binding (WNP-MMSA). The applicability of this linear method, based on the thermodynamics additivity principle, did not extend to 375 diverse A2A receptor antagonists. However, a fast but effective method could be enabled by replacing the linear approach with a machine learning technique using probabilistic classification trees, which classified the binding affinity correctly for 90% of the ligands in the training set and 67% in the test set.
1549-9596
1700-1713
Bortolato, Andrea
676811ba-aaa8-4f6e-a937-a790c09a3a7a
Tehan, Ben G.
f8bde1e6-d314-4db7-8611-4e8f745ef7c6
Bodnarchuk, Michael S.
cb7c3390-a1e3-4e13-916c-200706d11f34
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Mason, Jonathan S.
d2030439-828c-402b-9d6f-826303adfccb
Bortolato, Andrea
676811ba-aaa8-4f6e-a937-a790c09a3a7a
Tehan, Ben G.
f8bde1e6-d314-4db7-8611-4e8f745ef7c6
Bodnarchuk, Michael S.
cb7c3390-a1e3-4e13-916c-200706d11f34
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Mason, Jonathan S.
d2030439-828c-402b-9d6f-826303adfccb

Bortolato, Andrea, Tehan, Ben G., Bodnarchuk, Michael S., Essex, Jonathan W. and Mason, Jonathan S. (2013) Water network perturbation in ligand binding: Adenosine A2AAntagonists as a case study Journal of Chemical Information and Modeling, 53, (7), pp. 1700-1713.

Record type: Article

Abstract

Recent efforts in the computational evaluation of the thermodynamic properties of water molecules have resulted in the development of promising new in silico methods to evaluate the role of water in ligand binding. These methods include WaterMap, SZMAP, GRID/CRY probe, and Grand Canonical Monte Carlo simulations. They allow the prediction of the position and relative free energy of the water molecule in the protein active site and the analysis of the perturbation of an explicit water network (WNP) as a consequence of ligand binding. We have for the first time extended these approaches toward the prediction of kinetics for small molecules and of relative free energy of binding with a focus on the perturbation of the water network and application to large diverse data sets. Our results support a qualitative correlation between the residence time of 12 related triazine adenosine A2A receptor antagonists and the number and position of high energy trapped solvent molecules. From a quantitative viewpoint, we successfully applied these computational techniques as an implicit solvent alternative, in linear combination with a molecular mechanics force field, to predict the relative ligand free energy of binding (WNP-MMSA). The applicability of this linear method, based on the thermodynamics additivity principle, did not extend to 375 diverse A2A receptor antagonists. However, a fast but effective method could be enabled by replacing the linear approach with a machine learning technique using probabilistic classification trees, which classified the binding affinity correctly for 90% of the ligands in the training set and 67% in the test set.

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More information

Published date: 2013
Organisations: Chemistry, Faculty of Natural and Environmental Sciences, Computational Systems Chemistry

Identifiers

Local EPrints ID: 356466
URI: http://eprints.soton.ac.uk/id/eprint/356466
ISSN: 1549-9596
PURE UUID: ccb220b0-562a-4de5-8bc8-e77b7f966a8f
ORCID for Jonathan W. Essex: ORCID iD orcid.org/0000-0003-2639-2746

Catalogue record

Date deposited: 30 Sep 2013 15:29
Last modified: 08 Nov 2017 22:05

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Contributors

Author: Andrea Bortolato
Author: Ben G. Tehan
Author: Michael S. Bodnarchuk
Author: Jonathan S. Mason

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