A Mathematical Model of Oxidative Deamination of Amino Acid Catalyzed by Two d-Amino Acid Oxidases and Influence of Aeration on Enzyme Stability

Pre-optimization and one-step preparation of cascade enzymes system with broad substrates by model guidance: Application of chiral L-norvaline and L-phenylglycine biosynthesis

Cascade biocatalyst systems with catalytic promiscuity can be used for synthesis of a class of chiral chemicals but the optimization of these systems by model guidance is poorly explored. In this study, a cascade system with broad substrate spectrum was characterized and simulated by kinetic model with substrates of DL-Norvaline (DL-Nor) and DL-Phenylglycine (DL-Phg) as examples. To evaluate the optimal cascade system, maximum accumulation of intermediate products and conversion rate in the process were investigated by simultaneous solution of the rate equations for varying enzyme quantities. According to the simulation results, the cascade system was optimized by regulating the expression of D-amino acid oxidase and formate dehydrogenase and was prepared by one-step. The conversion efficiency of DL-Nor and DL-Phg have been significantly improved compared with that of before optimization. Moreover, the total of L-Nor and L-Phg were reached 498.2 mM and 79.5 mM through a gradient fed-batch conversion strategy, respectively.

Fabrication of a porous polymer membrane enzyme reactor and its enzymatic kinetics study in an artificial kidney model

A porous polymer membrane-based d-amino acid oxidase (DAAO) reactor was developed that mimicked enzymatic activity in a renal ischemia model. Using glycidyl methacrylate as a biocompatible reactive monomer, poly(styrene-glycidyl methacrylate) was synthesized via a reversible addition fragment chain transfer polymerization technique. The prepared porous polymer membrane was used as a support to effectively immobilize DAAO. Compared to DAAO modified on nonporous polymer membrane and free DAAO in solution, the constructed porous polymer membrane-based DAAO enzyme reactor displayed 3-fold and 19-fold increase in enzymolysis efficiency, respectively. In addition, a chiral ligand exchange capillary electrophoresis system for DAAO was used to study DAAO enzymatic kinetics with d,l-methionine as the substrate. The proposed porous polymer membrane-based enzyme reactor showed excellent performance both on reproducibility and stability. Moreover, the enzyme reactor was successfully applied to mimic DAAO activity in a renal ischemia model. These results demonstrated that the enzyme could be efficiently immobilized onto a porous polymer membrane as an enzyme reactor and has great potential in mimicking the enzymatic activity in kidney.

Dextran Aldehyde in Biocatalysis: More Than a Mere Immobilization System

Dextran aldehyde (dexOx), resulting from the periodate oxidative cleavage of 1,2-diol moiety inside dextran, is a polymer that is very useful in many areas, including as a macromolecular carrier for drug delivery and other biomedical applications. In particular, it has been widely used for chemical engineering of enzymes, with the aim of designing better biocatalysts that possess improved catalytic properties, making them more stable and/or active for different catalytic reactions. This polymer possesses a very flexible hydrophilic structure, which becomes inert after chemical reduction; therefore, dexOx comes to be highly versatile in a biocatalyst design. This paper presents an overview of the multiple applications of dexOx in applied biocatalysis, e.g., to modulate the adsorption of biomolecules on carrier surfaces in affinity chromatography and biosensors design, to serve as a spacer arm between a ligand and the support in biomacromolecule immobilization procedures or to generate artificial microenvironments around the enzyme molecules or to stabilize multimeric enzymes by intersubunit crosslinking, among many other applications.

Accelerating the implementation of biocatalysis in industry

Despite enormous progress in protein engineering, complemented by bioprocess engineering, the revolution awaiting the application of biocatalysis in the fine chemical industry has still not been fully realized. In order to achieve that, further research is required on several topics, including (1) rapid methods for protein engineering using machine learning, (2) mathematical modelling of multi-enzyme cascade processes, (3) process standardization, (4) continuous process technology, (5) methods to identify improvements required to achieve industrial implementation, (6) downstream processing, (7) enzyme stability modelling and prediction, as well as (8) new reactor technology. In this brief mini-review, the status of each of these topics will be briefly discussed.

Functional Enzyme-Based Approach for Linking Microbial Community Functions with Biogeochemical Process Kinetics

The kinetics of biogeochemical processes in natural and engineered environmental systems is typically described using Monod-type or modified Monod-type models. These models rely on biomass as surrogates for functional enzymes in microbial communities that catalyze biogeochemical reactions. A major challenge of applying such models is the difficulty of quantitatively measuring functional biomass for the constraining and validation of the models. However, omics-based approaches have been increasingly used to characterize microbial community structure, functions, and metabolites. Here, we propose an enzyme-based model that can incorporate omics data to link microbial community functions with biogeochemical process kinetics. The model treats enzymes as time-variable catalysts for biogeochemical reactions and applies a biogeochemical reaction network to incorporate intermediate metabolites. The sequences of genes and proteins from metagenomes, as well as those from the UniProt database, were used for targeted enzyme quantification and to provide insights into the dynamic linkage among functional genes, enzymes, and metabolites that are required in the model. The application of the model was demonstrated using denitrification, as an example, by comparing model simulations with measured functional enzymes, genes, denitrification substrates, and intermediates.