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.

Public Health Relevance

Advanced covariance NMR methods enable the efficient assignment ofresonances and the extraction of better distance information for the accuratedetermination of protein structures. These are a prerequisite for understandingprotein function and protein-ligand interactions for the development of new andbetter drugs. New NMR-based metabolomics tools provide comprehensiveinformation about the chemistry of cells; tissues; and biofluids. They will providea new perspective on disease and treatment and will lead the way topersonalized medicine.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
7R01GM066041-11
Application #
8786684
Study Section
Macromolecular Structure and Function B Study Section (MSFB)
Program Officer
Wehrle, Janna P
Project Start
2002-09-01
Project End
2014-04-30
Budget Start
2013-09-16
Budget End
2014-04-30
Support Year
11
Fiscal Year
2012
Total Cost
$151,890
Indirect Cost
$52,939
Name
Ohio State University
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
832127323
City
Columbus
State
OH
Country
United States
Zip Code
43210
Bingol, Kerem; Brüschweiler, Rafael (2016) Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods. Curr Opin Biotechnol 43:17-24
Markley, John L; Brüschweiler, Rafael; Edison, Arthur S et al. (2016) The future of NMR-based metabolomics. Curr Opin Biotechnol 43:34-40
Bingol, Kerem; Bruschweiler-Li, Lei; Li, Dawei et al. (2016) Emerging new strategies for successful metabolite identification in metabolomics. Bioanalysis 8:557-73
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
Bingol, Kerem; Brüschweiler, Rafael (2015) Two elephants in the room: new hybrid nuclear magnetic resonance and mass spectrometry approaches for metabolomics. Curr Opin Clin Nutr Metab Care 18:471-7
Bingol, Kerem; Brüschweiler, Rafael (2015) NMR/MS Translator for the Enhanced Simultaneous Analysis of Metabolomics Mixtures by NMR Spectroscopy and Mass Spectrometry: Application to Human Urine. J Proteome Res 14:2642-8
Bingol, Kerem; Bruschweiler-Li, Lei; Yu, Cao et al. (2015) Metabolomics beyond spectroscopic databases: a combined MS/NMR strategy for the rapid identification of new metabolites in complex mixtures. Anal Chem 87:3864-70
Zhang, Bo; Xie, Mouzhe; Bruschweiler-Li, Lei et al. (2015) Use of Charged Nanoparticles in NMR-Based Metabolomics for Spectral Simplification and Improved Metabolite Identification. Anal Chem 87:7211-7
Bingol, Kerem; Li, Da-Wei; Bruschweiler-Li, Lei et al. (2015) Unified and isomer-specific NMR metabolomics database for the accurate analysis of (13)C-(1)H HSQC spectra. ACS Chem Biol 10:452-9
Li, Da-Wei; Meng, Dan; Brüschweiler, Rafael (2015) Reliable resonance assignments of selected residues of proteins with known structure based on empirical NMR chemical shift prediction. J Magn Reson 254:93-7

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