Improving the thermostability of ulvan lyase from polysaccharide lyase family 25 based on multiple computational...

A rational design strategy to improve the thermal stability of enzymes is essential for advancing industrial applications. This study proposes a computer-aided rational design strategy combining deep learning and multiple energy function methods to enhance the thermal stability of ulvan lyase. ColabFold was employed for structure prediction. FoldX, Rosetta, and Schrödinger used to screen mutants for thermal stability. Three single-point mutants, A117V, K145L, and A237V, exhibited significantly enhanced enzyme thermal stability. Further combination of mutations revealed synergistic effects, with the most prominent double mutant A117V/A237V showing a half-life of 31.11 min, extending by 8.91 and 13.95 min compared to A117V and A237V, respectively. It also showed a 10 °C increase in T_m and retained 5.12-fold residual activity after 30 min of incubation at 40 °C compared to those of wild type, respectively. Molecular dynamics simulations revealed that the A117V/A237V reduced the flexibility of the loop regions and increased the intramolecular hydrogen bonds. Additionally, the A237V exhibited an optimal pH of 10.0, while the double mutants A117V/A237V and K145L/A237V showed a shift to pH 9.0, compared to pH 8.0 for the wild type. This study demonstrates that computer-aided rational design as a promising strategy to improve the thermostability of ulvan lyase.