Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.628669
Title: Prediction of the binding free energies of inhibitors of epidermal growth factor receptor kinase and the identification of the dynamics thereof
Author: Bull, Christopher
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2013
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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.
Supervisor: Essex, Jonathan Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.628669  DOI: Not available
Keywords: QD Chemistry
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