Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning.
Journal
Journal of chemical information and modeling
ISSN: 1549-960X
Titre abrégé: J Chem Inf Model
Pays: United States
ID NLM: 101230060
Informations de publication
Date de publication:
14 08 2023
14 08 2023
Historique:
pmc-release:
21
07
2024
medline:
15
8
2023
pubmed:
22
7
2023
entrez:
21
7
2023
Statut:
ppublish
Résumé
The prediction of enzyme activity is one of the main challenges in catalysis. With computer-aided methods, it is possible to simulate the reaction mechanism at the atomic level. However, these methods are usually expensive if they are to be used on a large scale, as they are needed for protein engineering campaigns. To alleviate this situation, machine learning methods can help in the generation of predictive-decision models. Herein, we test different regression algorithms for the prediction of the reaction energy barrier of the rate-limiting step of the hydrolysis of mono-(2-hydroxyethyl)terephthalic acid by the MHETase of
Identifiants
pubmed: 37479222
doi: 10.1021/acs.jcim.3c00772
pmc: PMC10430765
doi:
Substances chimiques
Hydrolases
EC 3.-
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4623-4632Références
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