Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.747260
Title: Understanding and predicting the dynamics, folding and binding of proteins
Author: Pfeiffenberger, E.
ISNI:       0000 0004 7229 380X
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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Abstract:
Proteins are involved in all processes of life and their shapes, interactions and functions are governed by physical forces. A model with atomic resolution is pivotal for the understanding of their mechanisms and how mutations perturb these. However, given the large variation of proteins and the limitations of experimental methods, in-silico approaches are the only viable solution. Presented here are a number of computational methods to predict their structure and binary interactions with atomic detail. Firstly, a machine-learning method was developed that models the recognition process of protein-protein binding to improve the identification of near-native binding sites. Secondly, a refinement method was developed to improve the structural accuracy of predicted monomers. An intra reside-residue contact map space was defined to perform more directed conformational exploration with metadynamics in order to find solutions that better resemble the native state. This method was extended to perform refinement of pre-docked heterodimers in order to predict the conformational transition from unbound to bound. Here, an inter residue-residue contact map space was defined between the interface of a receptor and a ligand. Following this extensive sampling of protein conformations by simulation, a recurrent neural network was defined and trained to predict the state changes during the sampling such that improved quality conformations can be identified. Finally, extensive in-silico biophysical experiments were performed to understand the mechanism of auto-phosphorylation for RET-kinase in wild-type and its deregulation by an oncogenic mutation.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.747260  DOI: Not available
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