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Extracting Ramachandran torsional angle distributions from 2D NMR data using Tikhonov regularization /

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dc.contributor.author Keyvanloo Shahrestanaky, Amirmohamad. en_US
dc.date.accessioned 2009-06-29T14:36:51Z
dc.date.available 2009-06-29T14:36:51Z
dc.date.issued 2004-06-29T14:36:51Z
dc.identifier.uri http://hdl.handle.net/10464/1643
dc.description.abstract Solid state nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for studying structural and dynamical properties of disordered and partially ordered materials, such as glasses, polymers, liquid crystals, and biological materials. In particular, twodimensional( 2D) NMR methods such as ^^C-^^C correlation spectroscopy under the magicangle- spinning (MAS) conditions have been used to measure structural constraints on the secondary structure of proteins and polypeptides. Amyloid fibrils implicated in a broad class of diseases such as Alzheimer's are known to contain a particular repeating structural motif, called a /5-sheet. However, the details of such structures are poorly understood, primarily because the structural constraints extracted from the 2D NMR data in the form of the so-called Ramachandran (backbone torsion) angle distributions, g{^,'4)), are strongly model-dependent. Inverse theory methods are used to extract Ramachandran angle distributions from a set of 2D MAS and constant-time double-quantum-filtered dipolar recoupling (CTDQFD) data. This is a vastly underdetermined problem, and the stability of the inverse mapping is problematic. Tikhonov regularization is a well-known method of improving the stability of the inverse; in this work it is extended to use a new regularization functional based on the Laplacian rather than on the norm of the function itself. In this way, one makes use of the inherently two-dimensional nature of the underlying Ramachandran maps. In addition, a modification of the existing numerical procedure is performed, as appropriate for an underdetermined inverse problem. Stability of the algorithm with respect to the signal-to-noise (S/N) ratio is examined using a simulated data set. The results show excellent convergence to the true angle distribution function g{(j),ii) for the S/N ratio above 100. en_US
dc.language.iso eng en_US
dc.publisher Brock University en_US
dc.subject Nuclear magnetic resonance spectroscopy. en_US
dc.subject Inversion (Geophysics) en_US
dc.subject Amyloid beta-protein en_US
dc.title Extracting Ramachandran torsional angle distributions from 2D NMR data using Tikhonov regularization / en_US
dc.type Electronic Thesis or Dissertation en_US
dc.degree.name M.Sc. Physics en_US
dc.degree.level Masters en_US
dc.contributor.department Department of Physics en_US
dc.degree.discipline Faculty of Mathematics and Science en_US


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