“A Study in Yellow”: Investigations in the Stereoselectivity of Ene-Reductases

Ene-reductases from the Old Yellow Enzyme (OYE) superfamily are a well-known and efficient biocatalytic alternative for the asymmetric reduction of C=C bonds. Considering the broad variety of substituents that can be tolerated, and the excellent stereoselectivities achieved, it is apparent why these enzymes are so appealing for preparative and industrial applications. Different classes of C=C bonds activated by at least one electron-withdrawing group have been shown to be accepted by these versatile biocatalysts in the last decades, affording a vast range of chiral intermediates employed in the synthesis of pharmaceuticals, agrochemicals, flavours, fragrances and fine chemicals. In order to access both enantiomers of reduced products, stereodivergent pairs of OYEs are desirable, but their natural occurrence is limited. The detailed knowledge of the stereochemical course of the reaction can uncover alternative strategies to orient the selectivity via mutagenesis, evolution, and substrate engineering. An overview of the ongoing studies on OYE-mediated bioreductions will be provided, with particular focus on stereochemical investigations by deuterium labelling.     

Flow regime identification in aerated stirred vessel using passive acoustic emission and machine learning

Smart at- or online process sensors, which employ machine learning (ML) to process data, have been the subject of extensive research in recent years, due to their potential for real-time process control. In this paper, a passive acoustic emission process sensor has been used to detect gas–liquid regimes within a stirred, aerated vessel using novel ML approaches. Pressure fluctuations (acoustic emissions) in an air-water system were recorded using a piezoelectric sensor installed on the external wall of three identical cylindrical tanks of diameter, T = 160 mm, filled to a volume of 5 L (height, H = 1.5 T). The tanks were made of either glass, steel, or aluminium, and each tank was equipped with a Rushton turbine of diameter, D = 0.35 T. The investigated flow regimes, flooding, loading, complete dispersion, and un-gassed, were obtained by changing the air feed flow rates and by varying the impeller speed. The acoustic spectra obtained were processed to select an optimal number of features characterizing each of the regimes, and these were used to train three different ML algorithms. The pre-processing includes a principal component analysis (PCA) step, which reduces the volume of data fed to the ML algorithms, saving computational time up to a factor of 5. The algorithms (decision tree, k-nearest neighbour, and support vector machines) were challenged to use these features to identify the correct flow regime. Accurate predictions of the three gas–liquid regimes of interest have been achieved. The accuracy of the prediction ranges from 90% to 99%, and this difference is related to the material used for the vessel.