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.
This work was supported by the US National Science Foundation (NSF)–Japan Science and Technology Agency (JST) Strategic International Collaborative Research Program (SICORP) for Japan–United States metabolomics. We appreciate funding from the US National Science Foundation (projects MCB 113944 and MCB 1611846 to O.F.), the US National Institutes of Health (U24 DK097154 to O.F.), and AMED–Core Research for Evolutionary Science and Technology (AMED-CREST) and JSPS KAKENHI (grants 15K01812, 15H05897, 15H05898, 17H03621 to M.A.).
Integrated supplementary information
Software comparison for MS-FINDER 2.0 versus CFM-ID, MetFrag, Molecular Structure Correlator (MSC), and MassFrontier
Software comparison for MS-DIAL 2.0 versus AMDIS, AnalyzerPro, and ChromaTOF
Summary of false discovery rate studies