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Title: Development of NMR methods for the structural elucidation of large proteins
Authors: ZHENG YU
Keywords: NMR, large protein, novel strategy, STARS, NMRspy, XYZ4D,
Issue Date: 17-Feb-2010
Source: ZHENG YU (2010-02-17). Development of NMR methods for the structural elucidation of large proteins. ScholarBank@NUS Repository.
Abstract: Protein structures are an important source of information for understanding biological function at the molecular level and provide the basis for many studies in research areas such as structure-based drug design and homology modelling. Currently the two main techniques for determining the three-dimensional structures of biological macromolecules are X-ray diffraction and NMR spectroscopy. In cases where proteins cannot be crystallized, NMR is the best, perhaps the only, method available to characterize the structures. At present, ~15% of protein structures deposited in the protein data bank is determined by NMR, but only ~1% of the NMR structures are for proteins larger than 25 kDa. Additionally, most of the large proteins only have crude global folds based on backbone assignments and a few side chain assignments which are obtained using deuterated samples. Unfortuantely, the preparation of deuterated or/and specific isotopic labelled protein samples is often challenging and places a bottleneck on the NMR study of large proteins. In this thesis, I proposed several new NMR techniques and computational methods to obtain partial or complete sequence specific assignments and to further determine high-resolution structures of lager proteins, using both the simple and cheap non-deuterated protein samples. Firstly, a new 3D multiple-quantum MQ-(H)CCmHm-TOCSY experiment is presented in chapter 2 to assign methyl resonances in high-molecular weight proteins, on the basis of spectral patterns and prior backbone assignments. The favorable relaxation properties of the multiple-quantum coherences and the slow decays of in-phase methyl 13C magnetizations optimize performance of the proposed experiment for application to large proteins. In combination with the H(C)CmHm-TOCSY experiment, a strategy is presented in chapter 3 for assigning protons of methyl-containing residues of uniformly 13C-labeled large proteins. Secondary, I present a novel strategy in chapter 4to assign backbone and side chain resonances of large proteins without deuteration, with which one can obtain high resolution structures from 1H-1H distance restraints. The strategy uses information from through-bond correlation experiments to filter intra-residue and sequential correlations from through-space correlation experiments, and then matches the filtered correlations to obtain sequential assignment. The strategy extends the size limit for structure determination by NMR to 42 kDa for monomeric proteins and to 65 kDa for differentially labeled multimeric proteins without deuteration or selective labeling. To assist the development of the new strategy mentioned above, a graphics package STARS was developed for performing statistics on interatomic distances and torsion angles in protein secondary structures from a protein crystal structure database. This graphics package shown in chapter 5 is also capable of facilitating assignment of ambiguous NOESY peaks, NMR structure determination, structure validation and comparison of protein folds. In order to comply with the requirements of our new experiments and strategies, I present a new software package NMRspy in chapter 6 which can be used for NMR spectroscopy visualization, analysis and management. It provides a variety of function and analysis routines that facilitate the analysis of complex, crowded and folded high-dimensional spectra. On the basis of this software platform, in chapter 7 I present a software extension XYZ4D for semi-automatic and automatic analysis of NMR data using the novel strategy shown in chapter 4. This software extension corresponds to the manual assignment steps of the new strategy but release users from tedious and time-consuming routines.
Appears in Collections:Ph.D Theses (Open)

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