Nuclear magnetic resonance(NMR) spectroscopy is a very popular platform for studying metabolites in biofluids. It is a rapid, non-destructive technique that allows one to identify and/or quantify a wide range of compounds without the need for prior compound separation or derivatization. Currently, NMR-based metabolomics studies focus mainly on 1D proton NMR experiments. A manual peak-fitting process is usually required in order to identify compounds from a 1D spectrum. This process becomes increasing difficult for more complex biofluids (e.g. urine or tissue extracts) because of severe peak overlaps.
In contrast to 1D NMR, 2D NMR offers a robust approach to resolving seriously overlapped spectra. Indeed, 2D (and 3D) NMR has long been used to resolve and identify individual resonances from large macromolecules such as DNA, RNA and proteins. 2D NMR is also increasingly being used in metabolomics studies in order to resolve spectral ambiguities for the identification of specific compounds in complex biofluid mixtures. However, there are currently very few dedicated tools available for analyzing 2D spectra of complex mixture, which significantly limits the broader applications of 2D NMR methods in metabolomics studies.
With the availability of a number of small-molecule NMR databases such as the Human Metabolome Database, the Biological Magnetic Resonance Bank, and the Madison Metabolomics Consortium Database (MMCD), it is now possible to create a 2D spectral reference library that covers essentially all common metabolites for all common biofluids. We propose that by using a comprehensive 2D spectral reference library along with a detailed knowledge of metabolite compositions of different biofluids, compound identification from 2D NMR spectra can be automated to a great extent. The objective of this project is to develop an easy-to-use software tool, called MetaboMiner, to aid in rapid and efficient metabolite identification from complex mixtures using 2D NMR spectroscopy .
|Last updated: May 25, 2008||Contact: Jianguo Xia email@example.com 780-4925786|