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Combining solid acid catalysts with enzyme reactions in aqueous environments is challenging because either very acidic conditions inactivate the enzymes, or the solid acid catalyst is neutralized. In this study, Amberlyst-15 encapsulated in polydimethylsiloxane (Amb-15@PDMS) is used to deprotect the lignin depolymerization product G−C2 dioxolane phenol in a buffered system at pH 6.0. This reaction is directly coupled with the biocatalytic reduction of the released homovanillin to homovanillyl alcohol by recombinant horse liver alcohol dehydrogenase, which is subsequently acylated by the promiscuous acyltransferase/hydrolase PestE_I208A_L209F_N288A in a one-pot system. The deprotection catalyzed with Amb-15@PDMS attains up to 97 % conversion. Overall, this cascade enables conversions of up to 57 %.
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.
Baeyer-Villiger monooxygenases (BVMOs) are important flavin-dependent enzymes which perform oxygen insertion reactions leading to valuable products. As reported in many studies, BVMOs are usually unstable during application, preventing a wider usage in biocatalysis. Here, we discovered a novel NADPH-dependent BVMO which originates from Halopolyspora algeriensis using sequence similarity networks (SSNs). The enzyme is stable at temperatures between 10 °C to 30 °C up to five days after the purification, and yields the normal ester product. In this study, the substrate scope was investigated for a broad range of aliphatic ketones and the enzyme was biochemically characterized to identify optimum reaction conditions. The best substrate (86 % conversion) was 2-dodecanone using purified enzyme. This novel BVMO could potentially be applied as part of an enzymatic cascade or in bioprocesses which utilize aliphatic alkanes as feedstock.
Marine Bacteroidetes that degrade polysaccharides contribute to carbon cycling in the ocean. Organic matter, including glycans from terrestrial plants, might enter the oceans through rivers. Whether marine bacteria degrade structurally related glycans from diverse sources including terrestrial plants and marine algae was previously unknown. We show that the marine bacterium Flavimarina sp. Hel_I_48 encodes two polysaccharide utilization loci (PULs) which degrade xylans from terrestrial plants and marine algae. Biochemical experiments revealed activity and specificity of the encoded xylanases and associated enzymes of these PULs. Proteomics indicated that these genomic regions respond to glucuronoxylans and arabinoxylans. Substrate specificities of key enzymes suggest dedicated metabolic pathways for xylan utilization. Some of the xylanases were active on different xylans with the conserved β-1,4-linked xylose main chain. Enzyme activity was consistent with growth curves showing Flavimarina sp. Hel_I_48 uses structurally different xylans. The observed abundance of related xylan-degrading enzyme repertoires in genomes of other marine Bacteroidetes indicates similar activities are common in the ocean. The here presented data show that certain marine bacteria are genetically and biochemically variable enough to access parts of structurally diverse xylans from terrestrial plants as well as from marine algal sources.
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.
Poly(vinyl alcohol) (PVA) is a water‐soluble synthetic vinyl polymer with remarkable physical properties including thermostability and viscosity. Its biodegradability, however, is low even though a large amount of PVA is released into the environment. Established physical‐chemical degradation methods for PVA have several disadvantages such as high price, low efficiency, and secondary pollution. Biodegradation of PVA by microorganisms is slow and frequently involves pyrroloquinoline quinone (PQQ)‐dependent enzymes, making it expensive due to the costly cofactor and hence unattractive for industrial applications. In this study, we present a modified PVA film with improved properties as well as a PQQ‐independent novel enzymatic cascade for the degradation of modified and unmodified PVA. The cascade consists of four steps catalyzed by three enzymes with in situ cofactor recycling technology making this cascade suitable for industrial applications.
Marine algae produce complex polysaccharides, which can be degraded by marine heterotrophic bacteria utilizing carbohydrate-active enzymes. The red algal polysaccharide porphyran contains the methoxy sugar 6-O-methyl-D-galactose (G6Me). In the degradation of porphyran, oxidative demethylation of this monosaccharide towards D-galactose and formaldehyde occurs, which is catalyzed by a cytochrome P450 monooxygenase and its redox partners. In direct proximity to the genes encoding for the key enzymes of this oxidative demethylation, genes encoding for zinc-dependent alcohol dehydrogenases (ADHs) were identified, which seem to be conserved in porphyran utilizing marine Flavobacteriia. Considering the fact that dehydrogenases could play an auxiliary role in carbohydrate degradation, we aimed to elucidate the physiological role of these marine ADHs. Although our results reveal that the ADHs are not involved in formaldehyde detoxification, a knockout of the ADH gene causes a dramatic growth defect of Zobellia galactanivorans with G6Me as a substrate. This indicates that the ADH is required for G6Me utilization. Complete biochemical characterizations of the ADHs from Formosa agariphila KMM 3901T (FoADH) and Z. galactanivorans DsijT (ZoADH) were performed, and the substrate screening revealed that these enzymes preferentially convert aromatic aldehydes. Additionally, we elucidated the crystal structures of FoADH and ZoADH in complex with NAD+ and showed that the strict substrate specificity of these new auxiliary enzymes is based on a narrow active site.
Enzymatic degradation and recycling can reduce the environmental impact of plastics. Despite decades of research, no enzymes for the efficient hydrolysis of polyurethanes have been reported. Whereas the hydrolysis of the ester bonds in polyester‐polyurethanes by cutinases is known, the urethane bonds in polyether‐polyurethanes have remained inaccessible to biocatalytic hydrolysis. Here we report the discovery of urethanases from a metagenome library constructed from soil that had been exposed to polyurethane waste for many years. We then demonstrate the use of a urethanase in a chemoenzymatic process for polyurethane foam recycling. The urethanase hydrolyses low molecular weight dicarbamates resulting from chemical glycolysis of polyether‐polyurethane foam, making this strategy broadly applicable to diverse polyether‐polyurethane wastes.