Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.777956
Title: Exploring protein dynamics using graph theory and single-molecule spectroscopy
Author: Peach, Robert
ISNI:       0000 0004 7963 7206
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Abstract:
Proteins are the workhorses of any living system. They are essential for day-to-day life and yet we still do not fully understand how they work. Proteins are intricate structures composed of thousands of atoms that interact and fluctuate over multiple temporal and spatial scales. These motions are encoded in the peptide sequence; fast and local vibrations develop collectively into the motion of secondary structures and catalytic domains. Despite the importance of dynamical fluctuations in mediating function and other biomolecular processes such as allostery, there is inadequate evidence supporting the various theories and calculational approaches which attempt to determine the relationships between structure, dynamics and function. Graph theory provides a way of capturing and representing the 3-dimensional atomistic physico- chemical details of a protein in a reduced form. In this thesis, we use Markov Stability, a novel graph-theoretic method for analysing the hierarchy of protein motions. We begin by producing an energy-weighted atomistic graph representation of a protein. We then use the transient behaviour of a random-walk to identify regions of atoms/residues that form communities. Using mutagenesis we are able to identify mutations that result in a large change to the community structure. We then use experimental methods (primarily single-molecule FRET) to explore the effect of these predicted mutations and validate the predictive qualities of Markov Stability. In this thesis, we demonstrate a strong correlation between theoretical and experimental mea- sures. We focus our study on Adenylate Kinase; a protein that balances molecular stability and fast large domain motions in the pursuit of cellular energy homeostasis. Given the structural data taken from the protein data bank we use the Markov stability graph theoretical approach to identify a number of biologically relevant community structures at different timescales. We are able to illustrate agreement between Markov Stability and the rate of motions of ADK subdomains. We then use the graph theoretical analysis to predict the effect of mutations to global dynamics and validate these predictions using single-molecule FRET; we show excellent agreement between the calculated properties of the graph theoretical analysis and the variation in FRET dynamics. Finally, we explore the relationship between the predictive score attributed to mutants by Markov Stability and the associated change in molecular stability.
Supervisor: Willison, Keith ; Klug, David ; Yaliraki, Sophia ; Barahona, Mauricio Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.777956  DOI:
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