Catalyst development for biochemical cascade reactions often follows a “whole‐cell‐approach” in which a single microbial cell is made to express all required enzyme activities. Although attractive in principle, the approach can encounter limitations when efficient overall flux necessitates precise balancing between activities. This study shows an effective integration of major design strategies from synthetic biology to a coherent development of plasmid vectors, enabling tunable two‐enzyme co‐expression in E. coli, for whole‐cell‐production of cellobiose. An efficient transformation of sucrose and glucose into cellobiose by a parallel (countercurrent) cascade of disaccharide phosphorylases requires the enzyme co‐expression to cope with large differences in specific activity of cellobiose phosphorylase (14 U mg−1) and sucrose phosphorylase (122 U mg−1). Mono‐ and bicistronic co‐expression strategies controlling transcription, transcription‐translation coupling or plasmid replication are analyzed for effect on activity and stable producibility of the whole‐cell‐catalyst. A key role of bom (basis of mobility) for plasmid stability dependent on the ori is reported and the importance of RBS (ribosome binding site) strength is demonstrated. Whole cell catalysts show high specific rates (460 µmol cellobiose min−1 g−1 dry cells) and performance metrics (30 g L−1; ∼82% yield; 3.8 g L−1 h−1 overall productivity) promising for cellobiose production.

Background Soluble cello-oligosaccharides (COS, β‐1,4‐D‐gluco‐oligosaccharides with degree of polymerization DP 2–6) have been receiving increased attention in different industrial sectors, from food and feed to cosmetics. Development of large-scale COS applications requires cost-effective technologies for their production. Cascade biocatalysis by the three enzymes sucrose-, cellobiose- and cellodextrin phosphorylase is promising because it enables bottom-up synthesis of COS from expedient substrates such as sucrose and glucose. A whole-cell-derived catalyst that incorporates the required enzyme activities from suitable co-expression would represent an important step towards making the cascade reaction fit for production. Multi-enzyme co-expression to reach distinct activity ratios is challenging in general, but it requires special emphasis for the synthesis of COS. Only a finely tuned balance between formation and elongation of the oligosaccharide precursor cellobiose results in the desired COS. Results Here, we show the integration of cellodextrin phosphorylase into a cellobiose-producing whole-cell catalyst. We arranged the co-expression cassettes such that their expression levels were upregulated. The most effective strategy involved a custom vector design that placed the coding sequences for cellobiose phosphorylase (CbP), cellodextrin phosphorylase (CdP) and sucrose phosphorylase (ScP) in a tricistron in the given order. The expression of the tricistron was controlled by the strong T7lacO promoter and strong ribosome binding sites (RBS) for each open reading frame. The resulting whole-cell catalyst achieved a recombinant protein yield of 46% of total intracellular protein in an optimal ScP:CbP:CdP activity ratio of 10:2.9:0.6, yielding an overall activity of 315 U/g dry cell mass. We demonstrated that bioconversion catalyzed by a semi-permeabilized whole-cell catalyst achieved an industrial relevant COS product titer of 125 g/L and a space–time yield of 20 g/L/h. With CbP as the cellobiose providing enzyme, flux into higher oligosaccharides (DP ≥ 6) was prevented and no insoluble products were formed after 6 h of conversion. Conclusions A whole-cell catalyst for COS biosynthesis was developed. The coordinated co-expression of the three biosynthesis enzymes balanced the activities of the individual enzymes such that COS production was maximized. With the flux control set to minimize the share of insolubles in the product, the whole-cell synthesis shows a performance with respect to yield, productivity, product concentration and quality that is promising for industrial production.

The metabolic engineering of pathways has been used extensively to produce molecules of interest on an industrial scale. Methods like gene regulation or substrate channeling helped to improve the desired product yield. Cell-free systems are used to overcome the weaknesses of engineered strains. One of the challenges in a cell-free system is selecting the optimized enzyme concentration for optimal yield. Here, a machine learning approach is used to select the enzyme concentration for the upper part of glycolysis. The artificial neural network approach (ANN) is known to be inefficient in extrapolating predictions outside the box: high predicted values will bump into a sort of "glass ceiling". In order to explore this "glass ceiling" space, we developed a new methodology named glass ceiling ANN (GC-ANN). Principal component analysis (PCA) and data classification methods are used to derive a rule for a high flux, and ANN to predict the flux through the pathway using the input data of 121 balances of four enzymes in the upper part of glycolysis. The outcomes of this study are i. in silico selection of optimum enzyme concentrations for a maximum flux through the pathway and ii. experimental in vitro validation of the "out-of-the-box" fluxes predicted using this new approach. Surprisingly, flux improvements of up to 63% were obtained. Gratifyingly, these improvements are coupled with a cost decrease of up to 25% for the assay.