Matsui Laboratory 

The Matsui Laboratory was established in April 2024 in the Department of Applied Chemistry and Bioscience, Faculty of Science and Technology, Chitose Institute of Science and Technology (CIST), Japan.

We aim to contribute to society through enzyme research by discovering novel enzymes and engineering their functions to develop biocatalysts for precise L-amino acid quantification and bio-based manufacturing. For instance, we successfully modified an enzyme derived from an environmental microorganism to achieve high selectivity for L-amino acid quantification (Matsui, J. Soc. Biotechnol., 2022). Additionally, we developed a method to enhance soluble expression of target enzymes that tend to form inclusion bodies by introducing both site-directed and random mutations (Matsui, Biosci. Biotech. Biochem., 2023). These technologies are now being expanded for applications in analytical diagnostics and industrial biomanufacturing.

Development of Selective Amino Acid Quantification Using Oxidoreductases

Our laboratory is developing highly selective and sensitive quantification methods for amino acids using oxidoreductases. We isolate new microorganisms from nature and identify unique enzymes for potential applications. We previously isolated a novel L-arginine oxidase (EC 1.4.3.25) from Pseudomonas sp. TPU 7192 and applied it to L-Arg quantification (Matsui et al., Enzyme Microb. Technol., 2016). We also identified a PLP-dependent enzyme with dual oxidative and decarboxylase functions from Burkholderia sp. AIU 395 and developed a selective L-lysine quantification system via coupled enzyme reactions (Sugawara, J. Biosci. Bioeng., 2015). Furthermore, an enzyme from Achromobacter sp. TPU 5009 exhibits specific oxidative activity toward L-histidine, demonstrating its applicability in L-His quantification (Matsui, J. Biosci. Bioeng., 2021).

Enzyme Engineering via Directed Evolution

To expand the utility of microbial enzymes, we apply directed evolution techniques to modify enzyme functions. For example, we constructed a mutant library of L-lysine oxidase from Pseudomonas sp. AIU 813, a bifunctional enzyme possessing both oxidase and monooxygenase activity, and successfully obtained mutants with enhanced oxidase activity (Matsui, FEBS Open Bio, 2014). In another case, we improved the thermal stability of L-tryptophan dehydrogenase (EC 1.4.1.19) from Nostoc punctiforme ATCC 29133 through random mutagenesis and functional screening (Matsui, J. Biotechnol., 2015). We also established a soluble expression system for L-lysine ε-oxidase (EC 1.4.3.20) from Marinomonas mediterranea NBRC 103028 by introducing random mutations and employing antibiotic resistance-based screening (Matsui, Biosci. Biotechnol. Biochem., 2015).

Original Technologies for High-Yield Enzyme Production

One of the major challenges in heterologous expression is that enzymes often form inactive inclusion bodies. To address this, we constructed random mutant libraries and analyzed the relationship between structural features and soluble expression. Based on these insights, we developed two novel methods for improving soluble expression:

• The α-Helix Method, which targets hydrophobicity in α-helices

• The HiSol Method, which uses both hydrophobicity and amino acid sequence conservation as indicators
  (Matsui, Sci. Rep., 2017).
  
  

Using solubility data from 441 enzymes derived from thermophilic bacteria and their primary sequences, we also built a machine-learning-based model that predicts expression solubility with 78% accuracy (Patent: JP2023-80992). Additionally, we introduced 108 systematic mutations in a target enzyme and conducted logistic regression analysis, successfully identifying amino acid properties and residue locations that contribute to soluble expression (Nakahara et al., ChemBioChem, 2024).