Protein engineering of botulinum toxins with enhanced ganglioside binding capacity A biophysical and computational approach
type: CreativeWork
description: 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…
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…
author: Copoiu, Liviu
language: eng
keywords: protein engineering, machine learning, deep learning, botulinum toxin, biophysical
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2021-12-31
date modified: 2026-07-22T22:24:20Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.89635