Computational methods for the study of immunoglobulin aggregation

Thesis

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.

Authors: Shorthouse, DR

• Publication date: 2015
• DOI: 10.5287/ora-gakzvwm94
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: Molecular Dynamics; Computational Biochemistry; Biophysics
• Subjects: Computational biochemistry; Biochemistry; Life Sciences