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Title: Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

Abstract

Here, we introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS2), and NMR in 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 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 for 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.

Authors:
 [1];  [1];  [1];  [2];  [2]; ORCiD logo [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Univ. of Kansas, Lawrence, KS (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Environmental Molecular Sciences Lab. (EMSL)
Sponsoring Org.:
USDOE
OSTI Identifier:
1421319
Report Number(s):
PNNL-SA-129343
Journal ID: ISSN 2218-1989; METALU; 49679
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Metabolites
Additional Journal Information:
Journal Volume: 8; Journal Issue: 1; Journal ID: ISSN 2218-1989
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Environmental Molecular Sciences Laboratory; metabolomics; metabolite identification; hybrid MS/NMR method; in silico fragmentation; chemical shift prediction; Arabidopsis thaliana metabolome

Citation Formats

Boiteau, Rene M., Hoyt, David W., Nicora, Carrie D., Kinmonth-Schultz, Hannah A., Ward, Joy K., and Bingol, Kerem. Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction. United States: N. p., 2018. Web. https://doi.org/10.3390/metabo8010008.
Boiteau, Rene M., Hoyt, David W., Nicora, Carrie D., Kinmonth-Schultz, Hannah A., Ward, Joy K., & Bingol, Kerem. Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction. United States. https://doi.org/10.3390/metabo8010008
Boiteau, Rene M., Hoyt, David W., Nicora, Carrie D., Kinmonth-Schultz, Hannah A., Ward, Joy K., and Bingol, Kerem. Wed . "Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction". United States. https://doi.org/10.3390/metabo8010008. https://www.osti.gov/servlets/purl/1421319.
@article{osti_1421319,
title = {Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction},
author = {Boiteau, Rene M. and Hoyt, David W. and Nicora, Carrie D. and Kinmonth-Schultz, Hannah A. and Ward, Joy K. and Bingol, Kerem},
abstractNote = {Here, we introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS2), and NMR in 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 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 for 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.},
doi = {10.3390/metabo8010008},
journal = {Metabolites},
number = 1,
volume = 8,
place = {United States},
year = {2018},
month = {1}
}

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Cited by: 18 works
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