Because of a bottleneck in metabolite identification, the large number of metabolites in body fluid still remains unknown, the development of innovative identification methods is mandatory. This hampers biomarker research for the innovative food and pharmaceutical industries. Metabolite identification projects have been designed to improve the current state of metabolite identification strategies.
In view of a major bottleneck in metabolite identification, the large number of metabolites in body fluid still remaining unknown, the development of innovative identification methods is mandatory. This bottleneck hampers biomarker research for the innovative food and pharmaceutical industries Such innovations are also desirable because the number of scientists with sufficient identification skills is steadily diminishing. In order to cope with these phenomena, metabolite identification projects have been designed aimed at improving the current state of metabolite identification strategies. Main priorities of these research projects were the construction of MSn and NMR databases of reference metabolites, the development of new methods (improved MSn acquisition, LC-MSn acquisition, automatic fractionation and enrichment of compounds present at low levels to obtain sufficient quantities for MSn and NMR measurements) and the development of innovative tools (MSn preprocessing and processing as well as NMR spectrum prediction, spectrum retrieval, visualization and NMR-based compound quantification tools).
Important results have been attained with respect to databases: an advanced NMR/MS database has been constructed containing circa 6000 polyphenol/flavonoid compounds. In collaboration with the Humane Metabolome Database (David Wishart) a MSn spectral data base has been constructed consisting of circa 430 reference compounds. In addition, essential tools for MSn data preprocessing and processing have been created. Tool development has also been carried out for spectrum visualization of reference spectra present in the MSn and NMR databases as well as for structure annotation to unknown (potential) metabolites. One of the strategic projects in metabolite identification is computer- assisted identification by integration of a wide range of spectroscopic, physicochemical and biochemical data. Spectroscopic data are used for comprehensive structure generation followed by fast candidate rejection, aimed at leaving only a few candidate metabolites.
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