Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity.

Ao, Y. F.; Dörr, M.; Menke, M. J.; Born, S.; Heuson, Egon; Bornscheuer, U.

Document type :

Article dans une revue scientifique: Article original

DOI :

10.1002/cbic.202300754

PMID :

38029350

Permalink :

http://hdl.handle.net/20.500.12210/103478

Title :

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity.

Author(s) :

Ao, Y. F. [Auteur]
Universität Greifswald - University of Greifswald
Dörr, M. [Auteur]
Universität Greifswald - University of Greifswald
Menke, M. J. [Auteur]
Universität Greifswald - University of Greifswald
Born, S. [Auteur]
Heuson, Egon [Auteur]
Unité de Catalyse et Chimie du Solide (UCCS) - UMR 8181
Bornscheuer, U. [Auteur]
Universität Greifswald - University of Greifswald

Journal title :

ChemBioChem

Abbreviated title :

Chembiochem

Volume number :

Pages :

e202300754

Publication date :

2023-12-02

ISSN :

1439-7633

English keyword(s) :

Biocatalysis
catalytic activity
machine learning
protein engineering
selectivity

HAL domain(s) :

Chimie/Catalyse

English abstract : [en]

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational ...
Show more >Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CNRS
Centrale Lille
ENSCL
Univ. Artois

Collections :

Unité de Catalyse et Chimie du Solide (UCCS) - UMR 8181

Research team(s) :

Valorisation des alcanes et de la biomasse (VAALBIO)

Submission date :

2024-01-20T00:23:50Z
2024-02-09T08:38:41Z
2024-02-09T09:37:16Z

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

Version	Link	Modification date
3	20.500.12210/103478*	2024-02-09T09:37:03Z
2	20.500.12210/103478.2	2024-02-09T08:34:08Z
1	20.500.12210/103478.1	2024-01-20T00:23:50Z

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