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The application of physics-based rescoring in drug design

The application of physics-based rescoring in drug design
The application of physics-based rescoring in drug design
Most drugs exert their action by binding to a macromolecule target, e.g. a protein. Hence estimating the binding affinity, using computer programs, is of utmost importance in the drug design process. Approximate free energy methods such as MM-PBSA/GBSA appear as attractive alternatives to rigorous free energy methodology. Recently, simplified versions of MM-PBSA/GBSA have been shown to give encouraging results for use as a fast rescoring tool of docked poses in the lead optimisation stage. The primary aim of this work is to ascertain the capability of the method in this context. First the reproducibility of a published all-in-one (docking and MM-GBSA rescoring) protocol was assessed. Following this the same five protein-ligand systems as in the published work, were subjected to multiple tests involving a range of docking tools and rescoring protocols with varying solvent models. Robust comparisons using statistical analysis, revealed the method performed overall poorly and inconsistently, showing, amongst other things, a strong sensitivity to starting system configuration. The effect of additional sampling on the performance of the method was investigated by generating Molecular Dynamics (MD) trajectories using both implicit and explicit solvent models. The first was shown to introduce considerable noise in the calculations, meanwhile, for the latter, the computational overhead was not justified. Overall we found that MM-PBSA/GBSA does not appear as promising in for routine use in the lead optimisation context, however it may have potential applications in the lead identification stage of drug discovery.
Haldoupis, Ioannis
fbea49d1-939e-4eec-98e0-ec23a3ef9921
Haldoupis, Ioannis
fbea49d1-939e-4eec-98e0-ec23a3ef9921
Essex, Jonathan
1f409cfe-6ba4-42e2-a0ab-a931826314b5

(2015) The application of physics-based rescoring in drug design. University of Southampton, Chemistry, Doctoral Thesis, 394pp.

Record type: Thesis (Doctoral)

Abstract

Most drugs exert their action by binding to a macromolecule target, e.g. a protein. Hence estimating the binding affinity, using computer programs, is of utmost importance in the drug design process. Approximate free energy methods such as MM-PBSA/GBSA appear as attractive alternatives to rigorous free energy methodology. Recently, simplified versions of MM-PBSA/GBSA have been shown to give encouraging results for use as a fast rescoring tool of docked poses in the lead optimisation stage. The primary aim of this work is to ascertain the capability of the method in this context. First the reproducibility of a published all-in-one (docking and MM-GBSA rescoring) protocol was assessed. Following this the same five protein-ligand systems as in the published work, were subjected to multiple tests involving a range of docking tools and rescoring protocols with varying solvent models. Robust comparisons using statistical analysis, revealed the method performed overall poorly and inconsistently, showing, amongst other things, a strong sensitivity to starting system configuration. The effect of additional sampling on the performance of the method was investigated by generating Molecular Dynamics (MD) trajectories using both implicit and explicit solvent models. The first was shown to introduce considerable noise in the calculations, meanwhile, for the latter, the computational overhead was not justified. Overall we found that MM-PBSA/GBSA does not appear as promising in for routine use in the lead optimisation context, however it may have potential applications in the lead identification stage of drug discovery.

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Published date: 25 June 2015
Organisations: University of Southampton, Chemistry

Identifiers

Local EPrints ID: 380891
URI: http://eprints.soton.ac.uk/id/eprint/380891
PURE UUID: 864d9003-af06-4f6a-a3db-76a08b02816c
ORCID for Jonathan Essex: ORCID iD orcid.org/0000-0003-2639-2746

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Date deposited: 22 Sep 2015 12:36
Last modified: 25 Jun 2018 04:01

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