Deep learning study of tyrosine reveals that roaming can lead to photodamage.
Journal
Nature chemistry
ISSN: 1755-4349
Titre abrégé: Nat Chem
Pays: England
ID NLM: 101499734
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
received:
13
09
2021
accepted:
13
04
2022
pubmed:
3
6
2022
medline:
11
8
2022
entrez:
2
6
2022
Statut:
ppublish
Résumé
Amino acids are among the building blocks of life, forming peptides and proteins, and have been carefully 'selected' to prevent harmful reactions caused by light. To prevent photodamage, molecules relax from electronic excited states to the ground state faster than the harmful reactions can occur; however, such photochemistry is not fully understood, in part because theoretical simulations of such systems are extremely expensive-with only smaller chromophores accessible. Here, we study the excited-state dynamics of tyrosine using a method based on deep neural networks that leverages the physics underlying quantum chemical data and combines different levels of theory. We reveal unconventional and dynamically controlled 'roaming' dynamics in excited tyrosine that are beyond chemical intuition and compete with other ultrafast deactivation mechanisms. Our findings suggest that the roaming atoms are radicals that can lead to photodamage, offering a new perspective on the photostability and photodamage of biological systems.
Identifiants
pubmed: 35655007
doi: 10.1038/s41557-022-00950-z
pii: 10.1038/s41557-022-00950-z
doi:
Substances chimiques
Amino Acids
0
Tyrosine
42HK56048U
Banques de données
figshare
['10.6084/m9.figshare.15132081']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
914-919Subventions
Organisme : Austrian Science Fund FWF
ID : J 4522
Pays : Austria
Organisme : Austrian Science Fund FWF
ID : W 1232
Pays : Austria
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.
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