Computational simulations of enzyme dynamics and the modelling of their reaction mechanisms

Ainsley, Jon (2017) Computational simulations of enzyme dynamics and the modelling of their reaction mechanisms. Doctoral thesis, Northumbria University.

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Proteins and enzymes are large and complex biological molecules, characterized by unique three-dimensional structure are highly flexible and dynamic nature. Thorough understanding of protein and enzyme function requires studying of their conformational flexibility, because important physiological processes, such as ligand binding and catalysis rely on an enzyme’s dynamic nature and their ability to adopt a variety of conformational states. Computational methods are widely applied in studying enzymes and proteins structure and function providing a detailed atomistic-level of resolution data about the dynamics and catalytic processes, mechanisms in biomolecules, therefore even more nowadays a term ‘computational enzymology’ has emerged. Experimental methods often have difficulty in predicting dynamic motions of proteins. Computational simulations techniques, such as Molecular Dynamics simulations, have proven successful in simulating the conformational flexibility of proteins in studying structure-function relationships.

Additionally, the binding events between two molecules, e.g. an enzyme and its substrate, can be computationally predicted with molecular docking methods. Enzymes are proteins that catalyse almost all biochemical reactions and metabolic processes in all organisms. In order to study the conformational flexibility of proteins we apply molecular dynamics simulations, and in order to simulate their reaction mechanisms we apply quantum mechanical simulations. Quantum mechanical simulations can also be used to predict the electronic structure of organic compounds, by calculating their electronic structures we perform orbital analyses and predict their optical properties. The results gained from our computational simulations can give new insights into explanation of experimental findings and data and can inspire and guide further experiments.

Item Type: Thesis (Doctoral)
Subjects: C700 Molecular Biology, Biophysics and Biochemistry
Department: Faculties > Health and Life Sciences > Applied Sciences
University Services > Graduate School > Doctor of Philosophy
Depositing User: Becky Skoyles
Date Deposited: 11 Oct 2018 15:27
Last Modified: 31 Jul 2021 22:46

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