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p-Coumaric acid (p-CA) is a key precursor for the biosynthesis of flavonoids. Tyrosine ammonia lyases (TALs) specifically catalyze the synthesis of p-CA from l-tyrosine, which is a convenient enzymatic pathway. To explore novel and highly active TALs, a phylogenetic tree-building approach was conducted including 875 putative TALs and 46 putative phenylalanine/tyrosine ammonia lyases (PTALs). Among them, 5 TALs and 3 PTALs were successfully characterized and found to exhibit the proposed enzymatic activity. The TAL from Chryseobacterium luteum sp. nov (TALclu) has the highest affinity (Km=0.019 mm) and conversion efficiency (kcat/Km=1631 s−1 ⋅ mm−1) towards l-tyrosine. The reaction conditions for two purified enzymes and their E. coli recombinant cells were optimized and p-CA yields of 2.03 g/L after 8 hours by TALclu and 2.35 g/L after 24 h by TAL from Rivularia sp. PCC 7116 (TALrpc) in whole cells were achieved. These TALs are thus candidates for the construction of whole-cell systems to produce the flavonoid precursor p-CA.
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.