Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences.
enzyme catalysis
enzyme design
generative model
mutation effects
natural evolution
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
28 Nov 2023
28 Nov 2023
Historique:
pmc-release:
20
05
2024
medline:
22
11
2023
pubmed:
20
11
2023
entrez:
20
11
2023
Statut:
ppublish
Résumé
The availability of natural protein sequences synergized with generative AI provides new paradigms to engineer enzymes. Although active enzyme variants with numerous mutations have been designed using generative models, their performance often falls short of their wild type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze
Identifiants
pubmed: 37983512
doi: 10.1073/pnas.2312848120
pmc: PMC10691223
doi:
Substances chimiques
Luciferases, Renilla
EC 1.13.12.5
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2312848120Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM122472
Pays : United States
Commentaires et corrections
Type : UpdateOf
Déclaration de conflit d'intérêts
Competing interests statement:The authors declare no competing interest.
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