Iterative enhancement of cutinase thermostability by multiple strategies based on combined directed evolution and computationally assisted design

Cutinase exhibits versatile biocatalytic potential in polymer degradation, textile processing, and industrial biocatalysis, where enhancing the thermal stability under extreme conditions is essential for practical applications. To enhance the thermal stability of   Humicola insolens cutinase (HiC), a combination of strategies approach integrating error-prone PCR, computational design, and machine learning were implemented.   Through systematic iterative recombination, an octuple mutant (M8) was developed with thermal tolerance and preserved catalytic activity. The engineered mutant demonstrated exceptional stability enhancements, exhibiting 8110- and 3982-fold increases in half-life at 65 °C and 70 °C, respectively, compared with wild-type HiC. Differential scanning calorimetry revealed a 13.0 °C elevation in melting temperature (T m ), while thermal inactivation analysis showed a 26.2 °C improvement in T (temperature causing 50 % activity loss in 60 min). Notably, M8 retained full activity at standard assay temperature and exhibited a broad pH profile despite incorporating eight stabilizing mutations. Molecular dynamics simulations and structural analyses revealed that the redistribution of surface electrostatic charges and a more compact overall structure were key factors in enhancing thermal stability. In summary, this study established a framework for rational thermostabilization of cutinases while providing molecular level insights into thermal adaptation mechanisms, with methodological implications for α/β-hydrolase family enzyme optimization. The engineered HiC mutant present significant potential for industrial processes requiring high-temperature operations.