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Protein engineering of botulinum toxins with enhanced ganglioside binding capacity A biophysical and computational approach


Type

Thesis

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Authors

Abstract

Botulinum toxins (BoNTs) comprise a family of extremely potent neurotoxins, which have been harnessed as muscle relaxants for the treatment of a wide variety of debilitating diseases, particularly for the treatment of movement disorders. BoNT entry into neurons leads to destructive cleavage of cellular proteins critical to vesicle fusion and neurotransmitter release at the neuromuscular junction. A dual receptor model has been proposed for BoNT binding to target neurons, comprising a low affinity ganglioside interaction followed by a higher affinity interaction with a protein receptor. A deeper understanding of the molecular nature of these interactions will facilitate the generation of modified BoNT proteins with novel characteristics and significant therapeutic potential. This project was aimed principally at molecular characterisation of the interaction of BoNT/A with gangliosides, using a combination of computational, biochemical, and biophysical methods. Specific achievements included:

  1. The development and optimisation of two biophysical protocols for measuring Botulinum neurotoxin binding to gangliosides.
  2. The preparation of a well curated carbohydrate database that contained all known structures of protein-carbohydrate.
  3. The generation of a comprehensive and meaningful benchmark for algorithms that are designed to predict affinity values for protein-small molecule interactions.
  4. The training of a state-of-the-art machine learning algorithm that can predict affinity values for protein-small molecule interactions.
  5. The use of computational and biophysical approaches to explore the specificity and selectivity of ganglioside binding pockets. The objective was to make contributions to the development of an engineered BoNT with novel binding properties and therapeutic potential. In addition, a set of novel tools and methodologies that can be applied across most types of structural data was developed. In addition to the main project this thesis describes a series of collaborative efforts, not directly related to BoNTs, that were undertaken during my PhD. This section focuses mainly on projects related to the COVID-19 pandemic, which heavily disrupted ordinary research work, as well as a parallel project modelling the proteome of Mycobacterium abscessus.

Description

Date

2021-12-31

Advisors

Blundell, Thomas Leon

Keywords

protein engineering, machine learning, deep learning, botulinum toxin, biophysical

Qualification

Doctor of Philosophy (PhD)

Awarding Institution

University of Cambridge
Sponsorship
Ipsen Bioinnovation via a CASE Studentship