Large scale quantum mechanical enzymology
type: CreativeWork
description: There exists a concerted and continual effort to simulate systems of genuine biological interest to greater accuracy with methods of increasing transferability. More accurate descriptions of these systems at a truly atomistic and electronic level are irrevocably changing our understanding of biochemical processes. Broadly, classical techniques do not employ enough rigour, while conventional quantum mechanical approaches are too computationally expensive for systems of the requisite size. Linear-scaling density-functional theory (DFT) is an accurate method that can apply the predictive power of quantum mechanics to the system sizes required to study problems in enzymology. This dissertation presents methodological developments and protocols, including best practice, for accurate preparation and optimisation, combined with proof-of-principle calculations demonstrating reliable results for a range of small molecule and large biomolecular systems. Previous authors have shown that DFT calculations yield an unphysical, negligible energy gap between the highest occupied and lowest unoccupied molecular orbitals for proteins and large water clusters, a characteristic reproduced in this dissertation. However, whilst others use this phenomenon to question the applicability of Kohn-Sham DFT to large systems, it is shown within this dissertation that the vanishing gap is, in fact, an electrostatic artefact of the method used to prepare the system. Furthermore, practical solutions are demonstrated for ensuring a physical gap is maintained upon increasing system size. Harnessing these advances, the rst application using linear-scaling DFT to optimise stationary points in the reaction pathway for the Bacillus subtilis chorismate mutase (CM) enzyme is made. Averaged energies of activation and reaction are presented for the rearrangement of chorismate to prephenate in CM and in water, for system sizes comprising up to 2000 atoms. Compared to the uncatalysed reaction, the calculated a…
abstract: There exists a concerted and continual effort to simulate systems of genuine biological interest to greater accuracy with methods of increasing transferability. More accurate descriptions of these systems at a truly atomistic and electronic level are irrevocably changing our understanding of biochemical processes. Broadly, classical techniques do not employ enough rigour, while conventional quantum mechanical approaches are too computationally expensive for systems of the requisite size. Linear-scaling density-functional theory (DFT) is an accurate method that can apply the predictive power of quantum mechanics to the system sizes required to study problems in enzymology. This dissertation presents methodological developments and protocols, including best practice, for accurate preparation and optimisation, combined with proof-of-principle calculations demonstrating reliable results for a range of small molecule and large biomolecular systems. Previous authors have shown that DFT calculations yield an unphysical, negligible energy gap between the highest occupied and lowest unoccupied molecular orbitals for proteins and large water clusters, a characteristic reproduced in this dissertation. However, whilst others use this phenomenon to question the applicability of Kohn-Sham DFT to large systems, it is shown within this dissertation that the vanishing gap is, in fact, an electrostatic artefact of the method used to prepare the system. Furthermore, practical solutions are demonstrated for ensuring a physical gap is maintained upon increasing system size. Harnessing these advances, the rst application using linear-scaling DFT to optimise stationary points in the reaction pathway for the Bacillus subtilis chorismate mutase (CM) enzyme is made. Averaged energies of activation and reaction are presented for the rearrangement of chorismate to prephenate in CM and in water, for system sizes comprising up to 2000 atoms. Compared to the uncatalysed reaction, the calculated a…
author: Lever, Greg
language: en
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/d92ca369-db1e-4ec9-a71e-96c25e8e4609/download
date published: 2014-10-07
date modified: 2026-07-22T22:24:24Z
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.16613