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Prediction of the binding free energies of inhibitors of epidermal growth factor receptor kinase and the identification of the dynamics thereof

Prediction of the binding free energies of inhibitors of epidermal growth factor receptor kinase and the identification of the dynamics thereof
Prediction of the binding free energies of inhibitors of epidermal growth factor receptor kinase and the identification of the dynamics thereof
Epidermal Growth Factor Receptor (EGFR) kinase is a signalling protein implicated in a number of cancers, including non-small cell lung cancer (NSCLC). As well as activating mutations of EGFR kinase being oncogenic, the prognosis of NSCLC correlates with the impact of EGFR mutations on inhibitor binding affinities. However, treatment with tyrosine kinase inhibitors is particularly vulnerable to resistance mutations. The exact mechanisms by which EGFR kinase mutations impart activation or resistance has not been clearly defined at an atomistic level, and attempts to elucidate these mechanisms in silico are hindered by the long time scales over which the conformational dynamics of EGFR kinase occur. In this thesis rigorous free energy calculations are employed to investigate the relative binding free energy of inhibitors of EGFR kinase, and elucidate the hydration of the binding pocket. Additionally, various enhanced molecular dynamics (MD) sampling methods are utilised alongside conventional MD to investigate their ability to overcome the challenge of the long time scales of conformational change in EGFR kinase. The complementary use of dimensionality reduction techniques such as principal components analysis and locally scaled diffusion map analysis is shown to be useful in characterising long time scale dynamics, as well as in validating the sampling of enhanced MD methods. Using these techniques alongside traditional analyses, new insight into the role of three activating mutations was gained; however, the results suggest that accessible simulation times are still too short, implying a continuing role for enhanced MD methods in the future.
Bull, Christopher
03155e2d-484e-48b3-8aff-769d67e561e2
Bull, Christopher
03155e2d-484e-48b3-8aff-769d67e561e2
Essex, Jonathan
1f409cfe-6ba4-42e2-a0ab-a931826314b5

(2013) Prediction of the binding free energies of inhibitors of epidermal growth factor receptor kinase and the identification of the dynamics thereof. University of Southampton, Chemistry, Doctoral Thesis, 313pp.

Record type: Thesis (Doctoral)

Abstract

Epidermal Growth Factor Receptor (EGFR) kinase is a signalling protein implicated in a number of cancers, including non-small cell lung cancer (NSCLC). As well as activating mutations of EGFR kinase being oncogenic, the prognosis of NSCLC correlates with the impact of EGFR mutations on inhibitor binding affinities. However, treatment with tyrosine kinase inhibitors is particularly vulnerable to resistance mutations. The exact mechanisms by which EGFR kinase mutations impart activation or resistance has not been clearly defined at an atomistic level, and attempts to elucidate these mechanisms in silico are hindered by the long time scales over which the conformational dynamics of EGFR kinase occur. In this thesis rigorous free energy calculations are employed to investigate the relative binding free energy of inhibitors of EGFR kinase, and elucidate the hydration of the binding pocket. Additionally, various enhanced molecular dynamics (MD) sampling methods are utilised alongside conventional MD to investigate their ability to overcome the challenge of the long time scales of conformational change in EGFR kinase. The complementary use of dimensionality reduction techniques such as principal components analysis and locally scaled diffusion map analysis is shown to be useful in characterising long time scale dynamics, as well as in validating the sampling of enhanced MD methods. Using these techniques alongside traditional analyses, new insight into the role of three activating mutations was gained; however, the results suggest that accessible simulation times are still too short, implying a continuing role for enhanced MD methods in the future.

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Published date: 30 September 2013
Organisations: University of Southampton, Chemistry

Identifiers

Local EPrints ID: 367099
URI: http://eprints.soton.ac.uk/id/eprint/367099
PURE UUID: 2d3f4185-aaa6-4010-b98d-9503461efbfc
ORCID for Jonathan Essex: ORCID iD orcid.org/0000-0003-2639-2746

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Date deposited: 22 Oct 2014 15:43
Last modified: 06 Jun 2018 13:07

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