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Identification of Unknown Metabolomics Mixture Compounds by Combining NMR, MS, and Cheminformatics.

Authors
  • Leggett, Abigail1
  • Wang, Cheng2
  • Li, Da-Wei3
  • Somogyi, Arpad3
  • Bruschweiler-Li, Lei3
  • Brüschweiler, Rafael4
  • 1 Ohio State Biochemistry Program, The Ohio State University, Columbus, OH, United States. , (United States)
  • 2 Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, United States. , (United States)
  • 3 Campus Chemical Instrument Center (CCIC), The Ohio State University, Columbus, OH, United States. , (United States)
  • 4 Ohio State Biochemistry Program, The Ohio State University, Columbus, OH, United States; Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, United States; Campus Chemical Instrument Center (CCIC), The Ohio State University, Columbus, OH, United States; Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, OH, United States. Electronic address: [email protected] , (United States)
Type
Published Article
Journal
Methods in enzymology
Publication Date
Jan 01, 2019
Volume
615
Pages
407–422
Identifiers
DOI: 10.1016/bs.mie.2018.09.003
PMID: 30638535
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Metabolomics aims at the comprehensive identification of metabolites in complex mixtures to characterize the state of a biological system and elucidate their roles in biochemical pathways. For many biological samples, a large number of spectral features observed by NMR spectroscopy and mass spectrometry (MS) belong to unknowns, i.e., these features do not belong to metabolites that have been previously identified, and their spectral information is not available in databases. By combining NMR, MS, and combinatorial cheminformatics, the analysis of unknowns can be pursued in complex mixtures requiring minimal purification. This chapter describes the SUMMIT MS/NMR approach covering sample preparation, NMR and MS data collection and processing, and the identification of likely unknowns with the use of cheminformatics tools and the prediction of NMR spectral properties. © 2019 Elsevier Inc. All rights reserved.

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