An assay procedure for determining the rate of an enzyme reaction lacking an optical signal: validity of coupled enzyme assays

Enabling technology and core theory of synthetic biology

Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering

Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conversion and biopharma. Typical enhancements sought in enzyme engineering and in vitro evolution campaigns include improved folding stability, catalytic activity, and/or substrate specificity. Despite significant progress in recent years in the areas of high-throughput screening and DNA sequencing, our ability to explore the vast space of functional enzyme sequences remains severely limited. Here, we review the currently available suite of modern methods for enzyme engineering, with a focus on novel readout systems based on enzyme cascades, and new approaches to reaction compartmentalization including single-cell hydrogel encapsulation techniques to achieve a genotype-phenotype link. We further summarize systematic scanning mutagenesis approaches and their merger with deep mutational scanning and massively parallel next-generation DNA sequencing technologies to generate mutability landscapes. Finally, we discuss the implementation of machine learning models for computational prediction of enzyme phenotypic fitness from sequence. This broad overview of current state-of-the-art approaches for enzyme engineering and evolution will aid newcomers and experienced researchers alike in identifying the important challenges that should be addressed to move the field forward.

Bienzymatic Sequential Reaction on Microgel Particles and Their Cofactor Dependent Applications

We report, the preparation and characterization of bioconjugates, wherein enzymes pyruvate kinase (Pk) and l-lactic dehydrogenase (Ldh) were covalently bound to poly(N-isopropylacrylamide)-poly(ethylenimine) (PNIPAm-PEI) microgel support using glutaraldehyde (GA) as the cross-linker. The effects of different arrangements of enzymes on the microgels were investigated for the enzymatic behavior and to obtain maximum Pk-Ldh sequential reaction. The dual enzyme bioconjugates prepared by simultaneous addition of both the enzymes immobilized on the same microgel particles (PL), and PiLi, that is, dual enzyme bioconjugate obtained by combining single-enzyme bioconjugates (immobilized pyruvate kinase (Pi) and immobilized lactate dehydrogenase (Li)), were used to study the effect of the assembly of dual enzymes systems on the microgels. The kinetic parameters (Km, kcat), reaction parameters (temperature, pH), stability (thermal and storage), and cofactor dependent applications were studied for the dual enzymes conjugates. The kinetic results indicated an improved turn over number (kcat) for PL, while the kcat and catalytic efficiency was significantly decreased in case of PiLi. For cofactor dependent application, in which the ability of ADP monitoring and ATP synthesis by the conjugates were studied, the activity of PL was found to be nearly 2-fold better than that of PiLi. These results indicated that the influence of spacing between the enzymes is an important factor in optimization of multienzyme immobilization on the support.