Solid State Nuclear Magnetic Resonance (ssNMR) spectroscopy can be a powerful tool for investigating the atomic-level structures and dynamics of biological macromolecules, including proteins. In this dissertation, I present an ssNMR study of three diverse proteins, revealing insights into their respective secondary structures, conformational variations, and intermolecular interactions. Additionally, I introduce novel computational methods to facilitate the assignment of chemical shifts of ssNMR spectra. The first of the proteins is the capsid protein of the Rous Sarcoma Virus. In previous research, the structure of the hexameric lattice of the in-vitro tubular assembly of the capsid protein was determined. In this study, chemical shift assignments were completed and the structure of the T=1 capsid assembly (comprising entirely of a pentameric lattice) of the I190V mutant variant of the capsid was determined, providing the missing component of the in-vivo capsid structure. The second protein studied was amyloid-beta 42, a particularly cytotoxic variant of the main component of amyloid plaques in the brains of Alzheimer's disease patients. Chemical shift assignments were made on ssNMR data from samples aggregated in cholesterol-containing phosphatidylcholine (POPC) lipid vesicles, and secondary structure and molecular distance information was obtained. Lastly, preliminary chemical shift assignments, statistics, and structural analysis was done on the polypeptide Ref-2Cx4, derived from the conserved domain of the Hawaiian bobtail squid reflectin protein. The reflectin protein, used in the squid's camouflage mechanism, possesses optically reflective and proton-conductive properties. The final part of the dissertation addresses a major bottleneck in ssNMR studies—the assignment of chemical shifts. I introduce Visual Assist, a suite of computational tools designed to streamline and expedite the assignment process. The developed computational methods are validated on the diverse set of proteins above, demonstrating their general applicability and efficiency.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2023-1119 |
| Date | 01 January 2023 |
| Creators | Thames, Tyrone |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Thesis and Dissertation 2023-2024 |
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