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Investigating the role of electrostatic interactions in modulating protein function

Fowler, Nicholas

[Thesis]. Manchester, UK: The University of Manchester; 2018.

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Abstract

The work in this thesis centres on understanding how electrostatic interactions modulate the function of proteins. A computational method is presented to predict the reduction potential shift of copper protein mutants. Two test mutants have been synthesised and their experimentally determined reduction potentials are compared to predictions. The focus of the thesis then shifts from considering how electrostatic interactions modulate the function of a single protein, to that of entire proteomes. Electrostatic-based methods for pKa and reactivity prediction are applied to the high-throughput proteomics data that are accruing for cysteine and lysine modifications. Conclusions are made on the biophysical factors which contribute to modification mechanisms, and cysteine/lysine susceptibility to specific modifications.

Bibliographic metadata

Type of resource:
Content type:
Form of thesis:
Type of submission:
Degree type:
Doctor of Philosophy
Degree programme:
BBSRC Doctoral Training Programme Chemistry
Publication date:
Location:
Manchester, UK
Total pages:
213
Abstract:
The work in this thesis centres on understanding how electrostatic interactions modulate the function of proteins. A computational method is presented to predict the reduction potential shift of copper protein mutants. Two test mutants have been synthesised and their experimentally determined reduction potentials are compared to predictions. The focus of the thesis then shifts from considering how electrostatic interactions modulate the function of a single protein, to that of entire proteomes. Electrostatic-based methods for pKa and reactivity prediction are applied to the high-throughput proteomics data that are accruing for cysteine and lysine modifications. Conclusions are made on the biophysical factors which contribute to modification mechanisms, and cysteine/lysine susceptibility to specific modifications.
Thesis main supervisor(s):
Funder(s):
Language:
en

Institutional metadata

University researcher(s):

Record metadata

Manchester eScholar ID:
uk-ac-man-scw:316046
Created by:
Fowler, Nicholas
Created:
18th September, 2018, 16:54:42
Last modified by:
Fowler, Nicholas
Last modified:
14th October, 2019, 12:25:16

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