Enhancement of protein thermostability

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.


Fig: (a) Front and back views of LPS (PDB: 1OIL) with the B-factor, where the catalytic triad (S87/H286/D264) is shown in red. Twelve loop regions are indicated: L1_A74/A75/T76, L2_V199/G200/G201, L3_L127/A128/Y129, L4_P216/T217/I218, L5_S219/V220/F221, L6_G222/V223/T224, L7_P233/L234/V235, L8_R258/G259/S260, L9_Q292/L293/L294, L10_G25/V26/L27, L11_P58/N59/G60, L12_Q39/R40/G41. (b) A concise summary of thermostability enhancement achieved in this work.

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%).

Yoshida, K., Kawai, S., Fujitani, M. et al. Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning. Sci Rep 11, 11883 (2021). https://doi.org/10.1038/s41598-021-91339-4

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