Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.631188
Title: Predicting structural and energetic effects of mutations at protein-protein interfaces
Author: Bradshaw, Richard Thomas
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2013
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Abstract:
Understanding the structural, dynamical and energetic basis of protein-protein interactions (PPIs) is key for a number of research disciplines. Predicting which sites in PPIs show potential for modulation with binding free energy (ΔG) calculations allows experimental work to be targeted and inhibitors to be rationally designed. However, PPIs remain a challenging target for computational free energy calculations due to their large and complex interfaces. A number of different methods for predicting ΔG from molecular dynamics simulations have been developed, yet each suffers from unique problems in its potential for widespread implementation across PPIs. This thesis initially evaluates the efficacy of the existing MM-PB/GBSA free energy calculation techniques and notes a niche for the improvement of the methods’ predictive power. This is followed by the development of a new computational method for predicting the effects of any PPI interface mutation, which we term Mutational Locally Enhanced Sampling (MULES). MULES generates atomistic molecular dynamics trajectories of native and mutant protein complexes simultaneously. These trajectories are then used to calculate relative binding free energies (ΔΔG) between the two complexes, investigating both structural and energetic effects of individual amino acids at an interface. In principle MULES allows the effect of any mutation to be calculated. Initially tested against a prototypical set of mutations with experimentally measured ΔΔG, MULES showed significantly improved accuracy in ΔΔG prediction and high precision and speed compared to existing methods. The approach was further validated on a large and diverse dataset of approximately 60 individual mutations, comparing results to experimental data and other computational predictions. Validation provided additional evidence for the improved accuracy, precision, speed and particularly versatility of the technique, but also identified areas for improvement. The successes and limitations of MULES discovered here will be of interest to the protein design, drug discovery and computational chemical biology communities.
Supervisor: Leatherbarrow, Robin ; Gould, Ian ; Tate, Ed Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.631188  DOI: Not available
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