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Computational mass spectrometry for metabolomics: Identification of metabolites and small molecules
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  • Published: 09 October 2010

Computational mass spectrometry for metabolomics: Identification of metabolites and small molecules

  • Steffen Neumann1 &
  • Sebastian Böcker2 

Analytical and Bioanalytical Chemistry volume 398, pages 2779–2788 (2010)Cite this article

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Abstract

The identification of compounds from mass spectrometry (MS) data is still seen as a major bottleneck in the interpretation of MS data. This is particularly the case for the identification of small compounds such as metabolites, where until recently little progress has been made. Here we review the available approaches to annotation and identification of chemical compounds based on electrospray ionization (ESI-MS) data. The methods are not limited to metabolomics applications, but are applicable to any small compounds amenable to MS analysis. Starting with the definition of identification, we focus on the analysis of tandem mass and MSn spectra, which can provide a wealth of structural information. Searching in libraries of reference spectra provides the most reliable source of identification, especially if measured on comparable instruments. We review several choices for the distance functions. The identification without reference spectra is even more challenging, because it requires approaches to interpret tandem mass spectra with regard to the molecular structure. Both commercial and free tools are capable of mining general-purpose compound libraries, and identifying candidate compounds. The holy grail of computational mass spectrometry is the de novo deduction of structure hypotheses for compounds, where method development has only started thus far. In a case study, we apply several of the available methods to the three compounds, kaempferol, reserpine, and verapamil, and investigate whether this results in reliable identifications.

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Author information

Authors and Affiliations

  1. Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, 06120, Halle, Germany

    Steffen Neumann

  2. Department of Mathematics and Computer Science, Friedrich-Schiller-University, Jena, 07743, Jena, Germany

    Sebastian Böcker

Authors
  1. Steffen Neumann
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  2. Sebastian Böcker
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Corresponding author

Correspondence to Steffen Neumann.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Online Resource 1

Molecular structures and details used for the identification case studies (PDF 890 kb)

Online Resource 2

Computational mass spectrometry for metabolomics: focus on the identification of metabolites and small molecules (TXT 11.2 kb)

Online Resource 3

Results of the FiD software (PDF 482 kb)

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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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Cite this article

Neumann, S., Böcker, S. Computational mass spectrometry for metabolomics: Identification of metabolites and small molecules. Anal Bioanal Chem 398, 2779–2788 (2010). https://doi.org/10.1007/s00216-010-4142-5

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  • Received: 14 June 2010

  • Revised: 16 August 2010

  • Accepted: 18 August 2010

  • Published: 09 October 2010

  • Issue Date: December 2010

  • DOI: https://doi.org/10.1007/s00216-010-4142-5

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Keywords

  • Mass spectrometry
  • Metabolomics
  • Compound identification
  • Spectral library
  • Structure elucidation
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