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Title: Diverse approaches to modelling conformations of mycolic acids from Myobactrium tuberculosis
Author: Groenewald, Wilma
ISNI:       0000 0004 5363 8606
Awarding Body: Bangor University
Current Institution: Bangor University
Date of Award: 2014
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Tuberculosis (TB) has plagued the human race for centuries. Despite being largely a curable disease, over 3500 people die from the disease each day. Mycobaeterium . tuberculosis, that causes TB, is particularly resilient. This is due, in part, to its lipid-rich cell wall, containing mycolic acids (MAs) as a major component. MAs are long fatty acids with functional groups of precise stereochemistry. The MA composition in the cell wall has been shown to affect properties such as cell wall permeability and virulence. The functional group stereochemistry is key in the interaction with different components of the host immune system. It has been shown that MA structure determines its conformational preference. In this work, a selection of diverse computational methods is employed to study different aspects of MA conformation. The precise stereochemical effects of the cyclopropane functional groups in model compounds with short alkyl chains were investigated, through quantum chemistry. Whole MAs were simulated using molecular dynamics in the gas phase and in polar and nonpolar solvents, in order to investigate folding patterns of MAs as a function of their structure together with the external environment. Lastly, a coarse grained (CG) model of an Alpha-MA was produced and used to simulate a MA monolayer. The monolayer is a good representation of the cell wall packing of MAs and could be correlated with existing monolayer experimental data. Together, the results presented here confirm the functional groups as folding points in MAs. Larger systems that are closer to the natural MA environment are essential as they can be compared with experimental data and will be useful as a tool in combating TB. However, the integrity of the larger CG system relies on the flow of information from the smaller, more accurate computational methods through to the CG model, and hence these approaches should be continued in parallel in future work.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available