Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.644718
Title: Computational methods for the study of immunoglobulin aggregation
Author: Shorthouse, David Robert
ISNI:       0000 0004 5357 4526
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2015
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Abstract:
Protein aggregation is a major challenge in the development of antibody-based therapeutics. Therapeutic antibodies are produced and stored in high concentrations and under fluctuating conditions unfavourable for their stability. Aggregation of these proteins in solution leads to serious consequences for patients, with the initiation of immune reactions, which have the potential to be fatal, and in the loss of clinical potency. The types of aggregates formed by antibodies, and the processes that lead to their propagation are poorly understood. By studying these molecules via computational approaches, we are able to simulate and probe their tendency to aggregate on experimentally comparable timescales. By performing small numbers of coarse grained simulations of immunoglobulin frag- ments it is shown that specific regions of proteins are involved in self-self interactions, and these regions are targets for reducing the self-association of experimental molecules. Techniques developed here are integrated within a high throughput approach that is able to generate information on aggregation for a large number of candidate antibody structures. The methodology was refined via development of a novel technique for coarse grained simulations of oligosaccharides. This method was initially tested on glycolipids, and then extended to glycoproteins. The primary outcome is a coarse grained model for a glyco- sylated antibody Fc fragment. The glycosylated Fc was then simulated, and compared to experimental data. Coarse grained simulations support the hypothesis that the protein be- comes more flexible in the absence of glycosylation.
Supervisor: Sansom, Mark S. P.; Gallagher, Thomas Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.644718  DOI: Not available
Keywords: Life Sciences ; Biochemistry ; Computational biochemistry ; Biophysics ; Molecular Dynamics
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