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Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning.
- Source :
-
Scientific reports [Sci Rep] 2021 Jun 04; Vol. 11 (1), pp. 11883. Date of Electronic Publication: 2021 Jun 04. - Publication Year :
- 2021
-
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%).
- Subjects :
- Computational Biology
Escherichia coli metabolism
Hot Temperature
Hydrolases chemistry
Kinetics
Molecular Conformation
Molecular Dynamics Simulation
Mutagenesis, Site-Directed
Plasmids metabolism
Polymerase Chain Reaction
Burkholderia cepacia genetics
Enzyme Stability
Lipase chemistry
Machine Learning
Mutagenesis
Mutation
Protein Engineering methods
Proteins chemistry
Proteins genetics
Subjects
Details
- Language :
- English
- ISSN :
- 2045-2322
- Volume :
- 11
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Scientific reports
- Publication Type :
- Academic Journal
- Accession number :
- 34088952
- Full Text :
- https://doi.org/10.1038/s41598-021-91339-4