Identifying epimetabolites by integrating metabolome databases with mass spectrometry cheminformatics

By Zijuan Lai, H. Tsugawa, Gert Wohlgemuth, S. Mehta, M.-C. Mueller, Yuxuan Zheng, A. Ogiwara, John K. Meissen, Megan Showalter, K. Takeuchi and 4 more. Nature Methods, 2017

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Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography–mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography–mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/0c4d54d529441cda78cdaf30eaa861f0c6bff6cf

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Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics (2018, background): “Another approach showed the combined use of high-resolution MS/MS data and use of the metabolic in-silico network expansion database (MINE) for the discovery of novel methylated epi-metabolites including N-methyl-UMP [45].”