Structure‐ and Data‐Driven Protein Engineering of Transaminases for Improving Activity and Stereoselectivity

Abstract: 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.

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch

Erschienen in
Structure‐ and Data‐Driven Protein Engineering of Transaminases for Improving Activity and Stereoselectivity ; day:03 ; month:05 ; year:2023 ; extent:10
Angewandte Chemie / International edition. International edition ; (03.05.2023) (gesamt 10)

Urheber
Ao, Yu‐Fei
Pei, Shuxin
Xiang, Chao
Menke, Marian J.
Shen, Lin
Sun, Chenghai
Dörr, Mark
Born, Stefan
Höhne, Matthias
Bornscheuer, Uwe Theo

DOI
10.1002/anie.202301660
URN
urn:nbn:de:101:1-2023050415032568928845
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
14.08.2025, 10:59 MESZ

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