AsiteDesign: a Semirational Algorithm for an Automated Enzyme Design

Understanding the stability of a plastic-degrading Rieske iron oxidoreductase system

Rieske oxygenases (ROs) are a diverse metalloenzyme class with growing potential in bioconversion and synthetic applications. We postulated that ROs are nonetheless underutilized because they are unstable. Terephthalate dioxygenase (TPADO PDB ID 7Q05) is a structurally characterized heterohexameric α3β3 RO that, with its cognate reductase (TPARED), catalyzes the first intracellular step of bacterial polyethylene terephthalate plastic bioconversion. Here, we showed that the heterologously expressed TPADO/TPARED system exhibits only ~300 total turnovers at its optimal pH and temperature. We investigated the thermal stability of the system and the unfolding pathway of TPADO through a combination of biochemical and biophysical approaches. The system's activity is thermally limited by a melting temperature (Tm) of 39.9°C for the monomeric TPARED, while the independent Tm of TPADO is 50.8°C. Differential scanning calorimetry revealed a two-step thermal decomposition pathway for TPADO with Tm values of 47.6 and 58.0°C (ΔH = 210 and 509 kcal mol-1, respectively) for each step. Temperature-dependent small-angle x-ray scattering and dynamic light scattering both detected heat-induced dissociation of TPADO subunits at 53.8°C, followed by higher-temperature loss of tertiary structure that coincided with protein aggregation. The computed enthalpies of dissociation for the monomer interfaces were most congruent with a decomposition pathway initiated by β-β interface dissociation, a pattern predicted to be widespread in ROs. As a strategy for enhancing TPADO stability, we propose prioritizing the re-engineering of the β subunit interfaces, with subsequent targeted improvements of the subunits.

Selective microbial production of lacto-N-fucopentaose I in Escherichia coli using engineered α-1,2-fucosyltransferases

Lacto-N-fucopentaose I (LNFP I) is the second most abundant fucosylated human milk oligosaccharide (HMO) in breast milk after 2'-fucosyllactose (2'-FL). Studies have reported that LNFP I exhibits antimicrobial activity against group B Streptococcus and antiviral effects against Enterovirus and Norovirus. Microbial production of HMOs by engineered Escherichia coli is an attractive, low-cost process, but few studies have investigated production of long-chain HMOs, including the pentasaccharide LNFP I. LNFP I is synthesized by α1,2-fucosyltransfer reaction to the N-acetylglucosamine moiety of the lacto-N-tetraose skeleton, which is catalyzed by α1,2-fucosyltransferase (α1,2-FucT). However, α1,2-FucTs competitively transfer fucose to lactose, resulting in formation of the byproduct 2'-FL. In this study, we constructed LNFP I-producing strains of E. coli with various α1,2-fucTs, and observed undesired 2'-FL accumulation during fed-batch fermentation, although, in test tube assays, some strains produced LNFP I without 2'-FL. We hypothesized that promiscuous substrate selectivity of α1,2-FucT was responsible for 2'-FL production. Therefore, to decrease the formation of byproduct 2'-FL, we designed 15 variants of FsFucT from Francisella sp. FSC1006 by rational and semi-rational design approaches. Five of these variants of FsFucT surpassed a twofold reduction in 2'-FL production compared with wild-type FsFucT while maintaining comparable levels of LNFP I production. These designs encompassed substitutions in either a loop region of the enzyme (residues 154-171), or in specific residues (Q7, H162, and L164) that influence substrate binding either directly or indirectly. In particular, the E. coli strain that expressed FsFucT_S3 variants, with a substituted loop region (residues 154-171) forming an α-helix structure, achieved an accumulation of 19.6 g/L of LNFP I and 0.04 g/L of 2'-FL, while the E. coli strain expressing the wild-type FsFucT accumulated 12.2 g/L of LNFP I and 5.85 g/L of 2'-FL during Fed-bach fermentation. Therefore, we have successfully demonstrated the selective and efficient production of the pentasaccharide LNFP I without the byproduct 2'-FL by combining protein engineering of α1,2-FucT designed through in silico structural modeling of an α1,2-FucT and docking simulation with various ligands, with metabolic engineering of the host cell.

EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution

Hydrolase-catalyzed kinetic resolution is a well-established biocatalytic process. However, the computational tools that predict favorable enzyme scaffolds for separating a racemic substrate mixture are underdeveloped. To address this challenge, we trained a deep learning framework, EnzyKR, to automate the selection of hydrolases for stereoselective biocatalysis. EnzyKR adopts a classifier-regressor architecture that first identifies the reactive binding conformer of a substrate-hydrolase complex, and then predicts its activation free energy. A structure-based encoding strategy was used to depict the chiral interactions between hydrolases and enantiomers. Different from existing models trained on protein sequences and substrate SMILES strings, EnzyKR was trained using 204 substrate-hydrolase complexes, which were constructed by docking. EnzyKR was tested using a held-out dataset of 20 complexes on the task of predicting activation free energy. EnzyKR achieved a Pearson correlation coefficient (R) of 0.72, a Spearman rank correlation coefficient (Spearman R) of 0.72, and a mean absolute error (MAE) of 1.54 kcal mol-1 in this task. Furthermore, EnzyKR was tested on the task of predicting enantiomeric excess ratios for 28 hydrolytic kinetic resolution reactions catalyzed by fluoroacetate dehalogenase RPA1163, halohydrin HheC, A. mediolanus epoxide hydrolase, and P. fluorescens esterase. The performance of EnzyKR was compared against that of a recently developed kinetic predictor, DLKcat. EnzyKR correctly predicts the favored enantiomer and outperforms DLKcat in 18 out of 28 reactions, occupying 64% of the test cases. These results demonstrate EnzyKR to be a new approach for prediction of enantiomeric outcomes in hydrolase-catalyzed kinetic resolution reactions.