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Amine transaminases (ATAs) are powerful biocatalysts for the stereoselective synthesis of chiral amines. However, wild-type ATAs usually show pH optima at slightly alkaline values and exhibit low catalytic activity under physiological conditions. For efficient asymmetric synthesis ATAs are commonly used in combination with lactate dehydrogenase (LDH, optimal pH: 7.5) and glucose dehydrogenase (GDH, optimal pH: 7.75) to shift the equilibrium towards the synthesis of the target chiral amine and hence their pH optima should fit to each other. Based on a protein structure alignment, variants of (R)-selective transaminases were rationally designed, produced in E. coli, purified and subjected to biochemical characterization. This resulted in the discovery of the variant E49Q of the ATA from Aspergillus fumigatus, for which the pH optimum was successfully shifted from pH 8.5 to 7.5 and this variant furthermore had a two times higher specific activity than the wild-type protein at pH 7.5. A possible mechanism for this shift of the optimal pH is proposed. Asymmetric synthesis of (R)-1-phenylethylamine from acetophenone in combination with LDH and GDH confirmed that the variant E49Q shows superior performance at pH 7.5 compared to the wild-type enzyme.
Abstract
Methylation of free hydroxyl groups is an important modification for flavonoids. It not only greatly increases absorption and oral bioavailability of flavonoids, but also brings new biological activities. Flavonoid methylation is usually achieved by a specific group of plant O‐methyltransferases (OMTs) which typically exhibit high substrate specificity. Here we investigated the effect of several residues in the binding pocket of the Clarkia breweri isoeugenol OMT on the substrate scope and regioselectivity against flavonoids. The mutation T133M, identified as reported in our previous publication, increased the activity of the enzyme against several flavonoids, namely eriodictyol, naringenin, luteolin, quercetin and even the isoflavonoid genistein, while a reduced set of amino acids at positions 322 and 326 affected both, the activity and the regioselectivity of the methyltranferase. On the basis of this work, methylated flavonoids that are rare in nature were produced in high purity.
Abstract
The aldehyde tag is appropriate to selectively label proteins, prepare antibody‐drug conjugates or to immobilize enzymes or antibodies for biotechnological and medical applications. The cysteine within the consensus sequence CxPxR of the aldehyde tag is specifically oxidized by the formylglycine‐generating enzyme (FGE) to the non‐canonical and electrophilic amino acid Cα‐formylglycine (FGly). Subsequent reductive amination is a common method for site‐directed immobilization, which usually results in poor immobilization efficiency due to the reaction conditions. Here, we introduce a new solid support like agarose modified with an aryl substituted pyrazolone (Knoevenagel reagent) that was obtained in a facile and efficient 2‐step synthesis. The modified agarose allowed the site‐selective and efficient immobilization of aldehyde‐containing small molecules, peptides and proteins – in particular enzymes – at physiological pH (6.2–8.2) without any additive or catalyst needed. In comparison to reductive amination, higher loadings and activities were achieved in various buffers at different concentrations and temperatures.
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.