Allostery and signalling pathways: methods and applications through graph theory
File(s)
Author(s)
Wu, Nan
Type
Thesis
Abstract
Allostery plays a crucial role in regulating protein activity via a perturbation at sites different from the orthosteric site. The conceptualisation of dynamic-based allostery suggests that the effects of allosteric ligand binding are transmitted as signals throughout the protein. However, elucidating such pathways requires extensive mutational studies. The direct observation of signal propagation is challenging owing to the inherent complexity of protein structures and varied timescales of allosteric effects. Hence, our understanding of intra-protein allosteric communication is limited with few insights to facilitate allosteric drug design.
Representing proteins as graphs provides a solution to capture the complex physico-chemical information in protein structures and study signal propagation across the graphs. In this thesis, we develop two distinct but interconnected novel graph-theoretic methods based on dynamic processes on graphs to explore allosteric signalling. Starting with constructing an energy-weighted atomistic protein graph, we apply diffusion-based approaches to the graph to detect bonds coupled strongly to allosteric perturbation. Paths of optimised propensity compute and quantify allosteric pathways as propensity optimised paths (POPs) using these bonds thereby determining critical signalling residues. Source-free propensities (SFprop) quantify the effect of perturbation of each residue on the entire protein to assign functional significance.
POPs analysis has been applied to multiple disease-related allosteric proteins. It uncovers experimentally validated allosteric signalling pathways in h-Ras, caspase-1, PDK1 and c-Abl. Quantifying these paths reveals critical signalling residues and their scores inform molecular design of the c-Abl allosteric activator. Upon evaluating the conservation scores of residues within POPs connecting the allosteric and orthosteric sites for IGPSs and 117 allosteric proteins, we confirm an over-presentation of conserved residues in allosteric signalling pathways. Allosteric communication is therefore evolutionarily conserved and encoded within protein structures. SFprop detects functionally significant sites and cysteines in EGFR tyrosine kinase, XPO1 and SARS-CoV-2 PLpro, providing insights into targeting allosteric cysteines.
Representing proteins as graphs provides a solution to capture the complex physico-chemical information in protein structures and study signal propagation across the graphs. In this thesis, we develop two distinct but interconnected novel graph-theoretic methods based on dynamic processes on graphs to explore allosteric signalling. Starting with constructing an energy-weighted atomistic protein graph, we apply diffusion-based approaches to the graph to detect bonds coupled strongly to allosteric perturbation. Paths of optimised propensity compute and quantify allosteric pathways as propensity optimised paths (POPs) using these bonds thereby determining critical signalling residues. Source-free propensities (SFprop) quantify the effect of perturbation of each residue on the entire protein to assign functional significance.
POPs analysis has been applied to multiple disease-related allosteric proteins. It uncovers experimentally validated allosteric signalling pathways in h-Ras, caspase-1, PDK1 and c-Abl. Quantifying these paths reveals critical signalling residues and their scores inform molecular design of the c-Abl allosteric activator. Upon evaluating the conservation scores of residues within POPs connecting the allosteric and orthosteric sites for IGPSs and 117 allosteric proteins, we confirm an over-presentation of conserved residues in allosteric signalling pathways. Allosteric communication is therefore evolutionarily conserved and encoded within protein structures. SFprop detects functionally significant sites and cysteines in EGFR tyrosine kinase, XPO1 and SARS-CoV-2 PLpro, providing insights into targeting allosteric cysteines.
Version
Open Access
Date Issued
2024-07-19
Date Awarded
01/09/2024
License URL
Advisor
Yaliraki, Sophia
Sponsor
Imperial College London
Publisher Department
Chemistry
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)