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

Autor: Ao YF; Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany.; Beijing National Laboratory for Molecular Sciences, CAS Key Laboratory of Molecular Recognition and Function, Institute of Chemistry, Chinese Academy of Sciences, Zhongguancun North First Street 2, Beijing, 100190, China.; University of Chinese Academy of Sciences, Yuquan Road 19(A), Beijing, 100049, China., Dörr M; Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany., Menke MJ; Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany., Born S; Technische Universität Berlin, Chair of Bioprocess Engineering, Ackerstraße 76, 13355, Berlin, Germany., Heuson E; Univ. Lille, CNRS, Centrale Lille, Univ. Artois, UMR 8181 UCCS, Unité de Catalyse et Chimie du Solide, 59000, Lille, France., Bornscheuer UT; Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany.
Jazyk: angličtina
Zdroj: Chembiochem : a European journal of chemical biology [Chembiochem] 2024 Feb 01; Vol. 25 (3), pp. e202300754. Date of Electronic Publication: 2023 Dec 11.
DOI: 10.1002/cbic.202300754
Abstrakt: 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.
(© 2023 The Authors. ChemBioChem published by Wiley-VCH GmbH.)
Databáze: MEDLINE
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