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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Quantum Chemistry Calculations for Metabolomics.

Ricardo M Borges1, Sean M Colby2, Susanta Das3

  • 1Walter Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, Brazil.

Chemical Reviews
|May 12, 2021
PubMed
Summary

Computational quantum chemistry enables standards-free small-molecule identification in metabolomics by generating in silico reference libraries. This approach overcomes limitations of traditional methods, enhancing characterization of complex biological samples.

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Area of Science:

  • Metabolomics
  • Computational Chemistry
  • Analytical Chemistry

Background:

  • Metabolomics aims to identify small molecules in complex samples, but most remain unidentified due to vast chemical diversity.
  • Current identification relies on authentic chemical standards, which are unavailable for the majority of metabolites.
  • Analytical technologies have advanced, yet the "metabolome coverage" remains limited.

Purpose of the Study:

  • To review the challenges in small-molecule identification within metabolomics.
  • To highlight the application of computational quantum chemistry as a solution for generating "in silico" reference libraries.
  • To discuss the potential of quantum chemistry for "standards-free" metabolomics identification.

Main Methods:

  • Review of existing literature on computational quantum chemistry applications in metabolomics.
  • Analysis of successful examples across various analytical techniques including NMR, ion mobility, IR, and mass spectrometry.
  • Discussion of best practices, error sources, and future outlook for quantum chemistry in metabolomics.

Main Results:

  • Quantum chemistry calculations provide an alternative to physical standards for building reference libraries.
  • Successful applications demonstrated for NMR, ion mobility, IR, and mass spectrometry, enabling "standards-free" identification.
  • Quantum chemistry offers a viable path to increase the number of identifiable small molecules in complex samples.

Conclusions:

  • Computational quantum chemistry is a powerful tool to overcome limitations in current metabolomics identification.
  • Adoption of "in silico" library generation using quantum chemistry can significantly enhance metabolome characterization.
  • Researchers are encouraged to integrate quantum chemistry calculations into their workflows for small-molecule identification.