Effect of Binding on Enantioselectivity of Epoxide Hydrolase

Enantioconvergent hydrolysis of racemic 1,2-epoxypentane and 1,2-epoxyhexane by an engineered Escherichia coli strain overexpressing a novel Streptomyces fradiae epoxide hydrolase

In order to excavate microbial epoxide hydrolases (EHs) with desired catalytic properties, a novel EH, SfEH1, was identified based on the genome annotation of Streptomyces fradiae and sequence alignment analysis with local protein library. The SfEH1-encoding gene, sfeh1, was then cloned and over-expressed in soluble form in Escherichia coli/BL21(DE3). The optimal temperature and pH of recombinant SfEH1 (reSfEH1) and reSfEH1-expressing E. coli (E. coli/sfeh1) were both determined as 30 ℃ and 7.0, also indicating that the influences of temperature and pH on reSfEH1's activities were more obvious than those of E. coli/sfeh1 whole cells. Subsequently, using E. coli/sfeh1 as catalyst, its catalytic properties towards thirteen common mono-substituted epoxides were tested, in which E. coli/sfeh1 had the highest activity of 28.5 U/g dry cells for rac-1,2-epoxyoctane (rac-6a), and (R)-1,2-pentanediol ((R)-3b) (or (R)-1,2-hexanediol ((R)-4b)) with up to 92.5% (or 94.1%) ee_p was obtained at almost 100% conversion ratio. Regioselectivity coefficients (α_S and β_R) displayed in the enantioconvergent hydrolysis of rac-3a (or rac-4a) were calculated to be 98.7% and 93.8% (or 95.2% and 98.9%). Finally, the reason of the high and complementary regioselectivity was confirmed by both kinetic parameter analysis and molecular docking simulations.

Computer-aided understanding and engineering of enzymatic selectivity

Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.

Computational Design of Enantiocomplementary Epoxide Hydrolases for Asymmetric Synthesis of Aliphatic and Aromatic Diols

The use of enzymes in preparative biocatalysis often requires tailoring enzyme selectivity by protein engineering. Herein we explore the use of computational library design and molecular dynamics simulations to create variants of limonene epoxide hydrolase that produce enantiomeric diols from meso‐epoxides. Three substrates of different sizes were targeted: cis‐2,3‐butene oxide, cyclopentene oxide, and cis‐stilbene oxide. Most of the 28 designs tested were active and showed the predicted enantioselectivity. Excellent enantioselectivities were obtained for the bulky substrate cis‐stilbene oxide, and enantiocomplementary mutants produced (S,S)‐ and (R,R)‐stilbene diol with >97 % enantiomeric excess. An (R,R)‐selective mutant was used to prepare (R,R)‐stilbene diol with high enantiopurity (98 % conversion into diol, >99 % ee). Some variants displayed higher catalytic rates (k_cat) than the original enzyme, but in most cases K_M values increased as well. The results demonstrate the feasibility of computational design and screening to engineer enantioselective epoxide hydrolase variants with very limited laboratory screening.

Machine learning to predict the specific optical rotations of chiral fluorinated molecules

A chemoinformatics method was applied to the assignment of absolute configurations and to the quantitative prediction of specific optical rotations using a data set of 88 chiral fluorinated molecules (44 pairs of enantiomers). Counterpropagation neural networks were explored for the classification of enantiomers as dextrorotatory or levorotatory. Regression models were trained using multilayer perceptrons (MLP), random forests (RF) or multilinear regressions (MLR), on the basis of physicochemical atomic stereo (PAS) descriptors. New descriptors were also derived considering the common structural features of the data set (cPAS descriptors), which enabled RF models to predict the whole data set with R = 0.964, mean absolute error (MAE) of 9.8° and root mean square error (RMSE) of 12.5° in leave-one-pair-out cross-validation experiments. The predictions for the 30 compounds measured in chloroform were obtained with R = 0.971, MAE = 9.1° and RMSE = 12.5°, which compares favorably with quantum chemistry calculations reported in the literature.