Zhang Sisi, Xiong Diao, Lin Xuejun, Jiang Lihong, Pi Wenhua, Dai Xinghua, Han Nanyu
School of Life Sciences, Yunnan Normal University, Kunming, 650500, China.
Key Laboratory of Yunnan for Biomass Energy and Biotechnology of Environment, Yunnan Normal University, Kunming, 650500, China.
FEMS Microbiol Lett. 2025 Jan 10;372. doi: 10.1093/femsle/fnaf072.
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.
源自多枝新丽盲蝽的GH11木聚糖酶XynCDBFV在各个行业中得到广泛应用。然而，其相对较低的热稳定性限制了它的潜力。在本研究中，采用了两种计算方法——Rosetta Cartesian_ddG和基于深度学习的工具Pythia，来识别影响XynCDBFV热稳定性的关键残基。两种方法均突出显示残基D57和G201是有前景的靶点。在这些位置进行位点饱和诱变产生了18个热稳定性得到改善的变体。值得注意的是，三个D57变体（D57N/S/T）的最适温度提高了10°C，并且在80°C孵育1小时后，残留活性比野生型高3.4%-21.7%。五个G201变体（G201A/C/F/I/V）的最适温度提高了5°C/10°C，残留活性提高了10.1%-22.6%。这些发现证实D57和G201是影响热稳定性的关键位点。然而，由于负上位性相互作用，将两个位点的有益突变组合会导致热稳定性降低。比较分析表明，虽然Rosetta Cartesian_ddG提供了更广泛的筛选，但它的错误发现率较高。相比之下，Pythia在精度和速度之间提供了平衡的权衡。本研究为提高酶的热稳定性提供了一个强大的框架，并强调了在蛋白质工程中将计算预测与实验验证相结合的价值。
