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2 results on '"Shun Kawai"'

1. Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning

Author

Kazunori Yoshida, Shun Kawai, Masaya Fujitani, Satoshi Koikeda, Ryuji Kato, and Tadashi Ema

Subjects
Medicine, Science
Abstract

Abstract We developed a method to improve protein thermostability, “loop-walking method”. Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as a hot-spot loop having an impact on thermostability, and the P233G/L234E/V235M mutant was found from 214 variants in the L7 library. Although a more excellent mutant might be discovered by screening all the 8000 P233X/L234X/V235X mutants, it was difficult to assay all of them. We therefore employed machine learning. Using thermostability data of the 214 mutants, a computational discrimination model was constructed to predict thermostability potentials. Among 7786 combinations ranked in silico, 20 promising candidates were selected and assayed. The P233D/L234P/V235S mutant retained 66% activity after heat treatment at 60 °C for 30 min, which was higher than those of the wild-type enzyme (5%) and the P233G/L234E/V235M mutant (35%).

Published
2021
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2. Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning

Author

Ryuji Kato, Masaya Fujitani, Satoshi Koikeda, Kazunori Yoshida, Shun Kawai, and Tadashi Ema

Subjects
0301 basic medicine, Hot Temperature, Hydrolases, Mutant, Molecular Conformation, Burkholderia cepacia, computer.software_genre, Protein Engineering, 01 natural sciences, Biochemistry, Polymerase Chain Reaction, Machine Learning, Enzyme Stability, Thermostability, chemistry.chemical_classification, Multidisciplinary, biology, Chemistry, Enzymes, Medicine, Plasmids, Science, In silico, Mutagenesis (molecular biology technique), Molecular Dynamics Simulation, 010402 general chemistry, Machine learning, Article, 03 medical and health sciences, Escherichia coli, Lipase, business.industry, Computational Biology, Proteins, biology.organism_classification, 0104 chemical sciences, Computational biology and bioinformatics, Loop (topology), Kinetics, 030104 developmental biology, Burkholderia, Enzyme, Mutagenesis, Mutation, biology.protein, Mutagenesis, Site-Directed, Artificial intelligence, business, computer
Abstract

We developed a method to improve protein thermostability, “loop-walking method”. Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as a hot-spot loop having an impact on thermostability, and the P233G/L234E/V235M mutant was found from 214 variants in the L7 library. Although a more excellent mutant might be discovered by screening all the 8000 P233X/L234X/V235X mutants, it was difficult to assay all of them. We therefore employed machine learning. Using thermostability data of the 214 mutants, a computational discrimination model was constructed to predict thermostability potentials. Among 7786 combinations ranked in silico, 20 promising candidates were selected and assayed. The P233D/L234P/V235S mutant retained 66% activity after heat treatment at 60 °C for 30 min, which was higher than those of the wild-type enzyme (5%) and the P233G/L234E/V235M mutant (35%).

Published
2021

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