New Approaches to NMR-Based Metabolite Identification

Abstract

Metabolite identification is a bottleneck for metabonomics studies using either MS- or NMR-based detection technologies. In contrast to the 4 nucleotides in genomics and the 20 amino acids in proteomics studies, the chemical space of small molecule metabolites is huge and confident identification of these metabolites is challenging. Instructive guides for the use of both MS [1] and NMR-based [2] detection technologies for metabolite identification have appeared recently. However, a key issue is the confidence of metabolite identification. Metabolite identification is of two types: 1) the structure elucidation of novel metabolites, that require isolation or synthesis for rigorous identification and 2) the structure confirmation of known metabolites. The Metabolomics Standards Initiative recognizes 4 levels of metabolite identification [3] and proposed that a known metabolite cannot be classed as Identified unless compared with data from a reference standard of that metabolite in the laboratory. New proposals, including a method called metabolite identification carbon efficiency (MICE) [4] for NMR-based metabolite identification, have proposed that comparison with database or literature data is sufficient. We now describe the development of an improved MICE method incorporating metabolite topological analysis. 1. Watson DG. A rough guide to metabolite identification using high resolution liquid chromatography mass spectrometry in metabolomic profiling in metazoans. Computational and Structural Biotechnology Journal, 4(5), 1-10 (2013). 2. Dona AC et al. A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Computational and Structural Biotechnology Journal, accepted doi:10.1016/j.csbj.2016.02.005 (2016). 3. Sumner LW et al. Proposed minimum reporting standards for chemical analysis. Metabolomics, 3(3), 211-221 (2007). 4. Everett JR. A new paradigm for known metabolite identification in metabonomics/metabolomics: metabolite identification efficiency. Computational and structural biotechnology journal, 13, 131-144 (2015)