Automated protein NMR data analysis and its application to a-synuclein fibrils

Courtney, Joseph M

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https://hdl.handle.net/2142/97595 Copy

Description

Title

Automated protein NMR data analysis and its application to a-synuclein fibrils

Author(s)

Courtney, Joseph M

Issue Date

2017-04-19

Director of Research (if dissertation) or Advisor (if thesis)

Rienstra, Chad M.

Doctoral Committee Chair(s)

Rienstra, Chad M.

Committee Member(s)

• Gruebele, Martin
• Hammes-Schiffer, Sharon
• Oldfield, Eric

Department of Study

Chemistry

Discipline

Chemistry

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Solid-state nuclear magnetic resonance (SSNMR)
• Alpha-synuclein
• Magic-angle spinning (MAS)

Abstract

In principle, nuclear magnetic resonance (NMR) spectroscopy provides structural and conformational information with sub-Angstrom precision and the ability to measure dynamics with timescales ranging from femtoseconds to years, all with atomic specificity. However, due to the relatively low sensitivity of NMR, fundamental limits on spectral resolution, and the complexity of the quantum mechanical phenomena NMR exploits, that wealth of information often remains out of reach. The highly varied presentation of molecular information in NMR spectra and the difficulty of numerical simulation of non-trivial systems has lead the majority of data analysis to be performed by trained experts, and because of its time-intensive nature, that analysis is rarely replicated by a third party or validated in an objective manner. In this dissertation I report my efforts to automate NMR data analysis in an objective and replicable manner and to provide tools for validation of resulting three-dimensional structures by direct comparison to raw spectral data. The first method, COMPASS, attempts to extract as much information as possible from a single 13C-13C two-dimensional spectrum for the determination of protein structure and successfully identified the true structure of 15 test proteins. The second method, GPS, predicts features of data that would be expected given a set of chemical shift assignments and possibly a three-dimensional structure and uses the presence or absence of those features in experimental spectra to refine or validate a given structure. I then report my application of these computational methods to the problems of refining an a-synuclein fibril structure with proton-detected NMR data, the analysis and characterization of a pair of interrelated a-synuclein fibril strains with distinct pathological properties, and to the general question of fibril polymorphism, a phenomenon that presents a substantial challenge to forming consistent conclusions about fibril properties and interactions across samples and research groups.

Graduation Semester

2017-05

Type of Resource

text

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Copyright and License Information

Copyright 2017 Joseph Courtney

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