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Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization?

Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization?
Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization?
Continuum electrostatics is combined with rigorous free-energy calculations in an effort to deliver a reliable and efficient method for in silico lead optimization. The methodology is tested by calculation of the relative binding free energies of a set of inhibitors of neuraminidase, cyclooxygenase2, and cyclin-dependent kinase 2. The calculated free energies are compared to the results obtained with explicit solvent simulations and empirical scoring functions. For cyclooxygenase2, deficiencies in the continuum electrostatics theory are identified and corrected with a modified simulation protocol. For neuraminidase, it is shown that a continuum representation of the solvent leads to markedly different protein-ligand interactions compared to the explicit solvent simulations, and a reconciliation of the two protocols is problematic. Cyclin-dependent kinase 2 proves more challenging, and none of the methods employed in this study yield high quality predictions. Despite the differences observed, for these systems, the use of an implicit solvent framework to predict the ranking of congeneric inhibitors to a protein is shown to be faster, as accurate or more accurate than the explicit solvent protocol, and superior to empirical scoring schemes.
free-energy calculations, monte-carlo simulations, generalized born models, empirical scoring functions, influenza-virus sialidases, p38 map kinase, molecular-dynamics, drug design, thermodynamic integration, perturbation calculations
0022-2623
7427-7439
Michel, Julien
3dfda20a-a6fa-4214-8c7d-578f550b9ad7
Verdonk, Marcel L.
85965663-3c55-4c53-9f98-ff9d707a8056
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5
Michel, Julien
3dfda20a-a6fa-4214-8c7d-578f550b9ad7
Verdonk, Marcel L.
85965663-3c55-4c53-9f98-ff9d707a8056
Essex, Jonathan W.
1f409cfe-6ba4-42e2-a0ab-a931826314b5

Michel, Julien, Verdonk, Marcel L. and Essex, Jonathan W. (2006) Protein-ligand binding affinity predictions by implicit solvent simulations: a tool for lead optimization? Journal of Medicinal Chemistry, 49 (25), 7427-7439. (doi:10.1021/jm061021s).

Record type: Article

Abstract

Continuum electrostatics is combined with rigorous free-energy calculations in an effort to deliver a reliable and efficient method for in silico lead optimization. The methodology is tested by calculation of the relative binding free energies of a set of inhibitors of neuraminidase, cyclooxygenase2, and cyclin-dependent kinase 2. The calculated free energies are compared to the results obtained with explicit solvent simulations and empirical scoring functions. For cyclooxygenase2, deficiencies in the continuum electrostatics theory are identified and corrected with a modified simulation protocol. For neuraminidase, it is shown that a continuum representation of the solvent leads to markedly different protein-ligand interactions compared to the explicit solvent simulations, and a reconciliation of the two protocols is problematic. Cyclin-dependent kinase 2 proves more challenging, and none of the methods employed in this study yield high quality predictions. Despite the differences observed, for these systems, the use of an implicit solvent framework to predict the ranking of congeneric inhibitors to a protein is shown to be faster, as accurate or more accurate than the explicit solvent protocol, and superior to empirical scoring schemes.

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More information

Published date: 2006
Keywords: free-energy calculations, monte-carlo simulations, generalized born models, empirical scoring functions, influenza-virus sialidases, p38 map kinase, molecular-dynamics, drug design, thermodynamic integration, perturbation calculations

Identifiers

Local EPrints ID: 44500
URI: http://eprints.soton.ac.uk/id/eprint/44500
ISSN: 0022-2623
PURE UUID: 71c07868-615d-4bb9-99af-22b5118af22e
ORCID for Jonathan W. Essex: ORCID iD orcid.org/0000-0003-2639-2746

Catalogue record

Date deposited: 06 Mar 2007
Last modified: 05 Nov 2019 02:04

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