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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.
Baeyer-Villiger monooxygenases (BVMOs) are important flavin-dependent enzymes which perform oxygen insertion reactions leading to valuable products. As reported in many studies, BVMOs are usually unstable during application, preventing a wider usage in biocatalysis. Here, we discovered a novel NADPH-dependent BVMO which originates from Halopolyspora algeriensis using sequence similarity networks (SSNs). The enzyme is stable at temperatures between 10 °C to 30 °C up to five days after the purification, and yields the normal ester product. In this study, the substrate scope was investigated for a broad range of aliphatic ketones and the enzyme was biochemically characterized to identify optimum reaction conditions. The best substrate (86 % conversion) was 2-dodecanone using purified enzyme. This novel BVMO could potentially be applied as part of an enzymatic cascade or in bioprocesses which utilize aliphatic alkanes as feedstock.
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.