Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• Scientific reports, 2021-06, Vol.11 (1), p.1-11, Article 11883

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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%).