Literature DB >> 34986335

Metabolite discovery: Biochemistry's scientific driver.

Martin Giera1, Oscar Yanes2, Gary Siuzdak3.   

Abstract

Metabolite identification represents a major challenge, and opportunity, for biochemistry. The collective characterization and quantification of metabolites in living organisms, with its many successes, represents a major biochemical knowledgebase and the foundation of metabolism's rebirth in the 21st century; yet, characterizing newly observed metabolites has been an enduring obstacle. Crystallography and NMR spectroscopy have been of extraordinary importance, although their applicability in resolving metabolism's fine structure has been restricted by their intrinsic requirement of sufficient and sufficiently pure materials. Mass spectrometry has been a key technology, especially when coupled with high-performance separation technologies and emerging informatic and database solutions. Even more so, the collective of artificial intelligence technologies are rapidly evolving to help solve the metabolite characterization conundrum. This perspective describes this challenge, how it was historically addressed, and how metabolomics is evolving to address it today and in the future.
Copyright © 2021. Published by Elsevier Inc.

Entities:  

Keywords:  artificial intelligence; biochemistry; mass spectrometry; metabolites; nuclear magnetic resonance; structure; unknowns

Mesh:

Year:  2022        PMID: 34986335     DOI: 10.1016/j.cmet.2021.11.005

Source DB:  PubMed          Journal:  Cell Metab        ISSN: 1550-4131            Impact factor:   27.287


  2 in total

Review 1.  A new dawn beyond lysine ubiquitination.

Authors:  Daniel R Squair; Satpal Virdee
Journal:  Nat Chem Biol       Date:  2022-07-27       Impact factor: 16.174

Review 2.  Activity-based annotation: the emergence of systems biochemistry.

Authors:  Kyu Y Rhee; Robert S Jansen; Christoph Grundner
Journal:  Trends Biochem Sci       Date:  2022-04-13       Impact factor: 14.264

  2 in total

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