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Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity
- 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.
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| Author: | Yu-Fei Ao, Mark Dörr, Marian J. Menke, Stefan Born, Egon Heuson, Uwe T. BornscheuerORCiD |
|---|---|
| URN: | urn:nbn:de:gbv:9-opus-109344 |
| DOI: | https://doi.org/10.1002/cbic.202300754 |
| ISSN: | 1439-7633 |
| Parent Title (English): | ChemBioChem |
| Publisher: | Wiley |
| Place of publication: | Hoboken, NJ |
| Document Type: | Article |
| Language: | English |
| Date of Publication (online): | 2023/11/29 |
| Date of first Publication: | 2024/02/01 |
| Release Date: | 2024/03/27 |
| Tag: | Biocatalysis; catalytic activity; machine learning; protein engineering; selectivity |
| Volume: | 25 |
| Issue: | 3 |
| Article Number: | e202300754 |
| Page Number: | 8 |
| Faculties: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie |
| Collections: | weitere DFG-förderfähige Artikel |
| Licence (German): |  Creative Commons - Namensnennung-Nicht kommerziell-Keine Bearbeitung 4.0 International |