Imine Reductase Based All-Enzyme Hydrogel with Intrinsic Cofactor Regeneration for Flow Biocatalysis

All-enzyme hydrogels are biocatalytic materials, with which various enzymes can be immobilized in microreactors in a simple, mild, and efficient manner to be used for continuous flow processes. Here we present the construction and application of a cofactor regenerating hydrogel based on the imine reductase GF3546 from Streptomyces sp. combined with the cofactor regenerating glucose-1-dehydrogenase from Bacillus subtilis. The resulting hydrogel materials were characterized in terms of binding kinetics and viscoelastic properties. The materials were formed by rapid covalent crosslinking in less than 5 min, and they showed a typical mesh size of 67 ± 2 nm. The gels were applied for continuous flow biocatalysis. In a microfluidic reactor setup, the hydrogels showed excellent conversions of imines to amines for up to 40 h in continuous flow mode. Variation of flow rates led to a process where the gels showed a maximum space-time-yield of 150 g·(L·day)−1 at 100 μL/mi

Intracellular Assembly of Interacting Enzymes Yields Highly‐Active Nanoparticles for Flow Biocatalysis

All-enzyme hydrogel (AEH) particles with a hydrodynamic diameter of up to 120 nm were produced intracellularly with an Escherichia coli-based in vivo system. The inCell-AEH nanoparticles were generated from polycistronic vectors enabling simultaneous expression of two interacting enzymes, the Lactobacillus brevis alcohol dehydrogenase (ADH) and the Bacillus subtilis glucose-1-dehydrogenase (GDH), fused with a SpyCatcher or SpyTag, respectively. Formation of inCell-AEH was analyzed by dynamic light scattering and atomic force microscopy. Using the stereoselective two-step reduction of a prochiral diketone substrate, we show that the inCell-AEH approach can be advantageously used in whole-cell flow biocatalysis, by which flow reactors could be operated for >4 days under constant substrate perfusion. More importantly, the inCell-AEH concept enables the recovery of efficient catalyst materials for stable flow bioreactors in a simple and economical one-step procedure from crude bacterial lysates. We believe that our method will contribute to further optimization of sustainable biocatalytic processes

Valency engineering of monomeric enzymes for self-assembling biocatalytic hydrogels

All-enzyme hydrogels are efficient reagents for continuous flow biocatalysis. These materials can be obtained by self-assembly of two oligomeric enzymes, modified with the complementary SpyTag and SpyCatcher units. To facilitate access to the large proportion of biocatalytically relevant monomeric enzymes, we demonstrate that the tagging valency of the monomeric (S)-stereoselective ketoreductase Gre2p from Saccharomyces cerevisiae can be designed to assemble stable, active hydrogels with the cofactor-regenerating glucose 1-dehydrogenase GDH from Bacillus subtilis. Mounted in microfluidic reactors, these gels revealed high conversion rates and stereoselectivity in the reduction of prochiral methylketones under continuous flow for more than 8 days. The sequential use as well as parallelization by ‘numbering up’ of the flow reactor modules demonstrate that this approach is suitable for syntheses on the semipreparative scale

Self-immobilizing Biocatalysts for fluidic Reaction Cascades

The industrial implementation of whole-cells and enzymes in flow biocatalysis microreactors is essential for the emergence of a biobased circular economy. Major challenges concern the efficient immobilization of delicate enzymes inside miniaturized reactors without compromising their catalytic activity. We describe the design and application of four different immobilization techniques including self-immobilizing whole-cells and purified enzymes on magnetic microbeads, as well as reactor modules manufactured by 3D printing of bioinks containing thermostable enzymes. To increase the volumetric activity of our microreactors we furthermore developed and applied self-assembling all-enzyme hydrogels with cofactor-regenerating capabilities. The resulting reactor formats have excellent operational stability times of > 14 days and maximum space-time yields of > 450 g product/L-1day-1 paving the way for mild and effective immobilization techniques of biocatalysts in microfluidic systems

Self-Immobilizing Biocatalysts Maximize Space–Time Yields in Flow Reactors

Maximizing space–time yields (STY) of biocatalytic flow processes is essential for the establishment of a circular biobased economy. We present a comparative study in which different biocatalytic flow reactor concepts were tested with the same enzyme, the (R)-selective alcohol dehydrogenase from Lactobacillus brevis (LbADH), that was used for stereoselective reduction of 5-nitrononane-2,8-dione. The LbADH contained a genetically encoded streptavidin (STV)-binding peptide to enable self-immobilization on STV-coated surfaces. The purified enzyme was immobilized by physisorption or chemisorption as monolayers on the flow channel walls, on magnetic microbeads in a packed-bed format, or as self-assembled all-enzyme hydrogels. Moreover, a multilayer biofilm with cytosolic-expressed LbADH served as a whole-cell biocatalyst. To enable cross-platform comparison, STY values were determined for the various reactor modules. While mono- and multilayer coatings of the reactor surface led to STY 450). The latter modules could be operated for prolonged times (>6 days). Given that our approach should be transferable to other enzymes, we anticipate that compartmentalized microfluidic reaction modules equipped with self-immobilizing biocatalysts would be of great utility for numerous biocatalytic and even chemo-enzymatic cascade reactions under continuous flow conditions

A Phenolic Acid Decarboxylase-Based All-Enzyme Hydrogel for Flow Reactor Technology

Carrier-free enzyme immobilization techniques are an important development in the field of efficient and streamlined continuous synthetic processes using microreactors. Here, the use of monolithic, self-assembling all-enzyme hydrogels is expanded to phenolic acid decarboxylases. This provides access to the continuous flow production of p-hydroxystyrene from p-coumaric acid for more than 10 h with conversions ≥98% and space time yields of 57.7 g·(d·L)−1. Furthermore, modulation of the degree of crosslinking in the hydrogels resulted in a defined variation of the rheological behavior in terms of elasticity and mesh size of the corresponding materials. This work is addressing the demand of sustainable strategies for defunctionalization of renewable feedstocks

Imine Reductase Based All-Enzyme Hydrogel with Intrinsic Cofactor Regeneration for Flow Biocatalysis

All-enzyme hydrogels are biocatalytic materials, with which various enzymes can be immobilized in microreactors in a simple, mild, and efficient manner to be used for continuous flow processes. Here we present the construction and application of a cofactor regenerating hydrogel based on the imine reductase GF3546 from Streptomyces sp. combined with the cofactor regenerating glucose-1-dehydrogenase from Bacillus subtilis. The resulting hydrogel materials were characterized in terms of binding kinetics and viscoelastic properties. The materials were formed by rapid covalent crosslinking in less than 5 min, and they showed a typical mesh size of 67 ± 2 nm. The gels were applied for continuous flow biocatalysis. In a microfluidic reactor setup, the hydrogels showed excellent conversions of imines to amines for up to 40 h in continuous flow mode. Variation of flow rates led to a process where the gels showed a maximum space-time-yield of 150 g·(L·day)−1 at 100 μL/min

Neural Network Heuristic Functions: Taking Confidence into Account

Neural networks (NN) are increasingly investigated in AI Planning, and are used successfully to learn heuristic functions. NNs commonly not only predict a value, but also output a confidence in this prediction. From the perspective of heuristic search with NN heuristics, it is a natural idea to take this into account, e.g. falling back to a standard heuristic where confidence is low. We contribute an empirical study of this idea. We design search methods which prune nodes, or switch between search queues, based on the confidence of NNs. We furthermore explore the possibility of out-of-distribution (OOD) training, which tries to reduce the overconfidence of NNs on inputs different to the training distribution. In experiments on IPC benchmarks, we find that our search methods improve coverage over standard methods, and that OOD training has the desired effect in terms of prediction accuracy and confidence, though its impact on search seems marginal