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Meletiou, Athina (2018) Large-scale grid computing approaches towards mapping the conformational space of mycolic acids from Mycobacterium tuberculosis. PhD thesis, University of Nottingham.

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Abstract

Tuberculosis (TB) has plagued humanity for centuries, and, despite being largely curable, it still claims thousands of lives daily. The causative agent, Mycobacterium tuberculosis (M. tb), shows resilience by evading immunological and antitubercular challenges. This is mainly due to its lipid-rich cell wall, whose main components are mycolic acids (MAs). MAs are long fatty acids with functional groups of precise stereochemistry. The MA chemical structure and composition balance in the cell wall modulates antigenicity, cell wall permeability, and virulence. MA chemical structure also steers MA folding. However, MA structure-function relationships are still not fully understood.

In this work, exhaustive MA conformational determination studies were performed in order to generate systematic detail into the correlation of MA structure and conformations, which may in turn hold the key to understanding TB at the atomistic level. Atomistic molecular dynamics (MD) simulations, in different solvents and temperatures, were performed on 166 MAs covering all three M. tb MA classes, both the cis and trans stereochemistries of the proximal cyclopropyl group, and a wide range of chain lengths. Analysis focused on dihedral angle and principal component analysis (PCA) clustering, as well as intra-molecular distance matrix calculations. The results presented in this work confirmed the MA functional groups as folding points and the spontaneity of the MA folding. Three major conformations were identified, namely four-chain, knot, and five-chain folds, with the four-chain fold being the dominant conformation. The knot fold demonstrated definable subtypes, whose further characterisation may offer potential for refinement of current serodiagnostic methods, since MAs have been used as antigens in TB serodiagnosis.

Item Type:	Thesis (University of Nottingham only) (PhD)
Supervisors:	Croft, A.K. Jaeger, C.
Keywords:	Tuberculosis, Prevention; Computational grids (Computer systems); Mycobacterium tuberculosis
Subjects:	Q Science > QR Microbiology
Faculties/Schools:	UK Campuses > Faculty of Engineering
Item ID:	50561
Depositing User:	Meletiou, Athina
Date Deposited:	13 Jul 2018 04:41
Last Modified:	08 May 2020 08:16
URI:	https://eprints.nottingham.ac.uk/id/eprint/50561

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