Approaching boiling point stability of an alcohol dehydrogenase through computationally-guided enzyme engineering


Enzyme instability is an important limitation for the investigation and application of enzymes. Therefore, methods to rapidly and effectively improve enzyme stability are highly appealing. In this study we applied a computational method (FRESCO) to guide the engineering of an alcohol dehydrogenase. Of the 177 selected mutations, 25 mutations brought about a significant increase in apparent melting temperature (ΔTm ≥ +3 °C). By combining mutations, a 10-fold mutant was generated with a Tm of 94 °C (+51 °C relative to wild type), almost reaching water’s boiling point, and the highest increase with FRESCO to date. The 10-fold mutant’s structure was elucidated, which enabled the identification of an activity-impairing mutation. After reverting this mutation, the enzyme showed no loss in activity compared to wild type, while displaying a Tm of 88 °C (+45 °C relative to wild type). This work demonstrates the value of enzyme stabilization through computational library design.

Maximilian JLJ Fürst
Maximilian JLJ Fürst
Assistant Professor of Computational Protein Design

I research computational protein design and high-throughput protein engineering.