The overall objectives of this research are to develop and apply new nuclear magnetic resonance (NMR) methods and web-based technology for the rapid and reliable collection, processing, and analysis of NMR spectra of large and small biomolecules. The principles of Covariance NMR Spectroscopy, developed in the previous funding period, will be extended to higher dimensional spectroscopy, including GFT and projection reconstruction NMR, in order to gain substantial increase in spectral resolution along indirect dimensions and to enable the automated assignment of backbone and side-chain resonances of proteins. Covariance-based resolution enhancement methods will be developed for new 4D NOESY-schemes to increase the number of short- and long-range NMR distance constraints, including stereo-specific assignments, necessary for the determination of high-quality protein structures. Covariance NMR and other matrix-based methods for the analysis of complex metabolite mixtures will be developed to obtain quantitative information on the mixture components, not available by other methods, with the goal toward automated high-throughput fingerprinting and profiling in the emerging field of metabolomics. The methods will be made available to a broad range of users through a suite of interconnected web servers.
Advanced covariance NMR methods enable the efficient assignment of resonances and the extraction of better distance information for the accurate determination of protein structures. These are a prerequisite for understanding protein function and protein-ligand interactions for the development of new and better drugs. New NMR-based metabolomics tools provide comprehensive information about the chemistry of cells, tissues, and biofluids. They will provide a new perspective on disease and treatment and will lead the way to personalized medicine.
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|Hansen, Alexandar L; Li, Dawei; Wang, Cheng et al. (2017) Absolute Minimal Sampling of Homonuclear 2D NMR TOCSY Spectra for High-Throughput Applications of Complex Mixtures. Angew Chem Int Ed Engl 56:8149-8152|
|Wang, Cheng; He, Lidong; Li, Da-Wei et al. (2017) Accurate Identification of Unknown and Known Metabolic Mixture Components by Combining 3D NMR with Fourier Transform Ion Cyclotron Resonance Tandem Mass Spectrometry. J Proteome Res 16:3774-3786|
|Hoffmann, Felix; Li, Da-Wei; Sebastiani, Daniel et al. (2017) Improved Quantum Chemical NMR Chemical Shift Prediction of Metabolites in Aqueous Solution toward the Validation of Unknowns. J Phys Chem A 121:3071-3078|
|Bingol, Kerem; Brüschweiler, Rafael (2017) Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods. Curr Opin Biotechnol 43:17-24|
|Bingol, Kerem; Li, Da-Wei; Zhang, Bo et al. (2016) Comprehensive Metabolite Identification Strategy Using Multiple Two-Dimensional NMR Spectra of a Complex Mixture Implemented in the COLMARm Web Server. Anal Chem 88:12411-12418|
|Bingol, Kerem; Bruschweiler-Li, Lei; Li, Dawei et al. (2016) Emerging new strategies for successful metabolite identification in metabolomics. Bioanalysis 8:557-73|
|Xie, Mouzhe; Hansen, Alexandar L; Yuan, Jiaqi et al. (2016) Residue-Specific Interactions of an Intrinsically Disordered Protein with Silica Nanoparticles and their Quantitative Prediction. J Phys Chem C Nanomater Interfaces 120:24463-24468|
|Zhang, Bo; Xie, Mouzhe; Bruschweiler-Li, Lei et al. (2016) Nanoparticle-Assisted Removal of Protein in Human Serum for Metabolomics Studies. Anal Chem 88:1003-7|
|Hansen, Alexandar L; Brüschweiler, Rafael (2016) Absolute Minimal Sampling in High-Dimensional NMR Spectroscopy. Angew Chem Int Ed Engl 55:14169-14172|
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