Structure elucidation of unknown metabolites in metabolomics by combined NMR and MS/MS prediction

Rene M. Boiteau, David W. Hoyt, Carrie D. Nicora, Hannah A. Kinmonth-Schultz, Joy K. Ward, Kerem Bingol

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

Abstract

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS2), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS2 approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.

Original languageEnglish (US)
Article number8
JournalMetabolites
Volume8
Issue number1
DOIs
StatePublished - Mar 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Arabidopsis thaliana metabolome
  • Chemical shift prediction
  • Hybrid MS/NMR method
  • In silico fragmentation
  • Metabolite identification
  • Metabolomics

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