Author, as appears in the article.: Giera, Martin; Yanes, Oscar; Siuzdak, Gary
Department: Enginyeria Electrònica, Elèctrica i Automàtica
URV's Author/s: Yanes Torrado, Óscar
Keywords: Unknowns Structure Nuclear magnetic resonance Metabolites Mass-spectrometry data Mass spectrometry Biochemistry Artificial intelligence platform nmr-spectroscopy metabolomics induced dissociation identification deconvolution computational tool chromatography acids
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.
Thematic Areas: Physiology Odontología Molecular biology Medicina ii Medicina i Interdisciplinar General medicine Endocrinology & metabolism Educação física Ciências biológicas ii Ciências biológicas i Cell biology
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: oscar.yanes@urv.cat
Author identifier: 0000-0003-3695-7157
Record's date: 2024-10-12
Papper version: info:eu-repo/semantics/submittedVersion
Link to the original source: https://www.sciencedirect.com/science/article/abs/pii/S1550413121005337
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Cell Metabolism. 34 (1): 21-34
APA: Giera, Martin; Yanes, Oscar; Siuzdak, Gary (2022). Metabolite discovery: Biochemistry's scientific driver. Cell Metabolism, 34(1), 21-34. DOI: 10.1016/j.cmet.2021.11.005
Article's DOI: 10.1016/j.cmet.2021.11.005
Entity: Universitat Rovira i Virgili
Journal publication year: 2022
Publication Type: Journal Publications
Repositori URV