Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.
Zijuan LaiHiroshi TsugawaGert WohlgemuthSajjan Singh MehtaMatthew MuellerYuxuan ZhengAtsushi OgiwaraJohn MeissenMegan ShowalterKohei TakeuchiTobias KindPeter BealMasanori AritaOliver FiehnPublished in: Nature methods (2017)
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.
Keyphrases
- mass spectrometry
- liquid chromatography
- gas chromatography mass spectrometry
- gas chromatography
- high resolution
- solid phase extraction
- tandem mass spectrometry
- high resolution mass spectrometry
- simultaneous determination
- ms ms
- high performance liquid chromatography
- capillary electrophoresis
- big data
- multiple sclerosis
- electronic health record
- machine learning
- data analysis
- structure activity relationship
- artificial intelligence
- deep learning
- adverse drug