The University of Southampton
University of Southampton Institutional Repository

J-coupling predictor function building and large–scale NMR simulations of proteins and nucleic acids

J-coupling predictor function building and large–scale NMR simulations of proteins and nucleic acids
J-coupling predictor function building and large–scale NMR simulations of proteins and nucleic acids
This thesis discusses large scale quantum mechanical NMR simulations and fitting works, and deals with biomolecular spin systems.
Chapter one introduces the theoretical background that underpins the simulation work. It describes spin and it properties, the interactions of spin with the magnetic field, the environment and it self and the Hamiltonians that describe those interactions. It also covers the Bloch equations that is normally used to predict the longitudinal and transverse relaxations. It further briefly describes the common relaxation mechanisms that are available for a spin system. It also included NMR experiments that are often used in the study of protein and nucleic acid systems.
Chapter 2 summarizes the building of J-coupling predictor function. It provides the detail on how the estimator function was built. The function is required for NMR simulations and incorporates the functionality of J-coupling estimation, which provides J-coupling values for proteins and nucleic acids. After building the estimator function NOESY spectra simulation of ubiquitin and RNA stem loops spin systems was performed. Chapter 3 then discusses our simulation method, treating the system quantum mechanically along with an advanced and detailed description of NOE using Redfield relaxation theory.
Chapter 4 reports on large–scale simulations and fitting of an exchanging 1H-15N HSQC spectra of calmodulin (PDB: 1CLL) upon stepwise addition of a ligand, that was analysed using a iii formalism that operates in the direct product of spin state space and chemical state space. It also presents the least squares technique for the fitting procedure and the Nelder-Mead simplex minimization technique used in the process. The chapter also highlights the data collated from the fitting work.
Finally, chapter 5 concludes the thesis with the simulation of NOESY stem loops RNA spin system and fitting with respect to correlation time and frequency offset. It also explains how we fitted the data from theory to those from the experiment and why the diagonal peaks were eliminated from the fitting
.
University of Southampton
Welderufael, Zenawi, Teklay
3a3b458e-9830-4bed-a27b-bc5792613265
Welderufael, Zenawi, Teklay
3a3b458e-9830-4bed-a27b-bc5792613265
Kuprov, Ilya
bb07f28a-5038-4524-8146-e3fc8344c065

Welderufael, Zenawi, Teklay (2016) J-coupling predictor function building and large–scale NMR simulations of proteins and nucleic acids. University of Southampton, Doctoral Thesis, 224pp.

Record type: Thesis (Doctoral)

Abstract

This thesis discusses large scale quantum mechanical NMR simulations and fitting works, and deals with biomolecular spin systems.
Chapter one introduces the theoretical background that underpins the simulation work. It describes spin and it properties, the interactions of spin with the magnetic field, the environment and it self and the Hamiltonians that describe those interactions. It also covers the Bloch equations that is normally used to predict the longitudinal and transverse relaxations. It further briefly describes the common relaxation mechanisms that are available for a spin system. It also included NMR experiments that are often used in the study of protein and nucleic acid systems.
Chapter 2 summarizes the building of J-coupling predictor function. It provides the detail on how the estimator function was built. The function is required for NMR simulations and incorporates the functionality of J-coupling estimation, which provides J-coupling values for proteins and nucleic acids. After building the estimator function NOESY spectra simulation of ubiquitin and RNA stem loops spin systems was performed. Chapter 3 then discusses our simulation method, treating the system quantum mechanically along with an advanced and detailed description of NOE using Redfield relaxation theory.
Chapter 4 reports on large–scale simulations and fitting of an exchanging 1H-15N HSQC spectra of calmodulin (PDB: 1CLL) upon stepwise addition of a ligand, that was analysed using a iii formalism that operates in the direct product of spin state space and chemical state space. It also presents the least squares technique for the fitting procedure and the Nelder-Mead simplex minimization technique used in the process. The chapter also highlights the data collated from the fitting work.
Finally, chapter 5 concludes the thesis with the simulation of NOESY stem loops RNA spin system and fitting with respect to correlation time and frequency offset. It also explains how we fitted the data from theory to those from the experiment and why the diagonal peaks were eliminated from the fitting
.

Text
complete_thesis (003) - Version of Record
Available under License University of Southampton Thesis Licence.
Download (11MB)

More information

Published date: September 2016
Organisations: University of Southampton, Chemistry

Identifiers

Local EPrints ID: 409733
URI: http://eprints.soton.ac.uk/id/eprint/409733
PURE UUID: 9daefaad-d2f9-4bb5-8bee-c96d055ddf2e
ORCID for Ilya Kuprov: ORCID iD orcid.org/0000-0003-0430-2682

Catalogue record

Date deposited: 01 Jun 2017 04:07
Last modified: 14 Mar 2019 01:36

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×