Autor segons l'article: Giera, Martin; Yanes, Oscar; Siuzdak, Gary
Departament: Enginyeria Electrònica, Elèctrica i Automàtica
Autor/s de la URV: Yanes Torrado, Óscar
Paraules clau: 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
Resum: 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.
Àrees temàtiques: Physiology Odontología Molecular biology Medicina ii Medicina i Interdisciplinar General medicine Endocrinology & metabolism Educação física Ciências biológicas ii Ciências biológicas i Cell biology
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: oscar.yanes@urv.cat
Identificador de l'autor: 0000-0003-3695-7157
Data d'alta del registre: 2024-10-12
Versió de l'article dipositat: info:eu-repo/semantics/submittedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/abs/pii/S1550413121005337
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Cell Metabolism. 34 (1): 21-34
Referència de l'ítem segons les normes 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
DOI de l'article: 10.1016/j.cmet.2021.11.005
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2022
Tipus de publicació: Journal Publications
Repositori URV