Identifying pivotal sites affecting thermostability of GH11 xylanase via conventional and deep learning-based energy calculation.

Abstract

The GH11 xylanase XynCDBFV, derived from Neocallimastix patriciarum, is widely used in various industries. However, its relatively low thermostability limits its potential. In this study, two computational approaches-Rosetta Cartesian_ddG and the deep learning-based tool Pythia-were employed to identify key residues affecting XynCDBFV thermostability. Both methods highlighted residues D57 and G201 as promising targets. Site-saturation mutagenesis at these positions yielded 18 variants with improved thermostability. Notably, three D57 variants (D57N/S/T) exhibited a 10°C increase in optimal temperature and retained 3.4%-21.7% higher residual activity than the wild type after 1-h incubation at 80°C. Five G201 variants (G201A/C/F/I/V) showed 5°C/10°C enhancements in optimal temperatures, with 10.1%-22.6% improved residual activity. These findings validate D57 and G201 as pivotal sites influencing thermostability. However, combining beneficial mutations from both sites led to reduced thermostability due to negative epistatic interactions. Comparative analysis revealed that while Rosetta Cartesian_ddG offers broader screening, it suffers from a high false discovery rate. In contrast, Pythia provides a balanced trade-off between precision and speed. This study offers a robust framework for enzyme thermostability enhancement and underscores the value of integrating computational predictions with experimental validation in protein engineering.