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Structure- and Data-Driven Protein Engineering of Transaminases for Improving Activity and Stereoselectivity
- Amine transaminases (ATAs) are powerful biocatalysts for the stereoselective synthesis of chiral amines. Machine learning provides a promising approach for protein engineering, but activity prediction models for ATAs remain elusive due to the difficulty of obtaining high-quality training data. Thus, we first created variants of the ATA from Ruegeria sp. (3FCR) with improved catalytic activity (up to 2000-fold) as well as reversed stereoselectivity by a structure-dependent rational design and collected a high-quality dataset in this process. Subsequently, we designed a modified one-hot code to describe steric and electronic effects of substrates and residues within ATAs. Finally, we built a gradient boosting regression tree predictor for catalytic activity and stereoselectivity, and applied this for the data-driven design of optimized variants which then showed improved activity (up to 3-fold compared to the best variants previously identified). We also demonstrated that the model can predict the catalytic activity for ATA variants of another origin by retraining with a small set of additional data.
Author: | Yu-Fei Ao, Shuxin Pei, Chao Xiang, Marian J. Menke, Lin Shen, Chenghai Sun, Mark Dörr, Stefan Born, Matthias Höhne, Uwe T. BornscheuerORCiD |
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URN: | urn:nbn:de:gbv:9-opus-109087 |
DOI: | https://doi.org/10.1002/anie.202301660 |
ISSN: | 1521-3773 |
Parent Title (English): | Angewandte Chemie International Edition |
Publisher: | Wiley |
Place of publication: | Hoboken, NJ |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2023/04/06 |
Date of first Publication: | 2023/06/05 |
Release Date: | 2024/03/25 |
Tag: | Biocatalysis; Catalytic Activity; Machine Learning; Stereoselectivity; Transaminases |
Volume: | 62 |
Issue: | 23 |
Article Number: | e202301660 |
Page Number: | 9 |
Faculties: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie |
Collections: | weitere DFG-förderfähige Artikel |
Licence (German): | Creative Commons - Namensnennung-Nicht kommerziell-Keine Bearbeitung 4.0 International |