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通过传统和基于深度学习的能量计算来确定影响GH11西兰酶热稳定的关键位置
Sisi Zhang1, Diao Xiong1, Xuejun Lin1
1School of Life Sciences, Yunnan Normal University, Kunming, 650500, China.
FEMS microbiology letters
|July 11, 2025
概括
计算方法确定了关键残留物D57和G201,以提高GH11西兰酶XynCDBFV的热稳定性. 突变酶改善了酶的性能,为蛋白质工程提供了一个框架.
科学领域:
- 生物化学 生物化学
- 蛋白质工程是指蛋白质工程.
- 计算生物学 计算生物学
背景情况:
- 来自Neocallimastix patriciarum的GH11氧化酶XynCDBFV具有工业应用,但热稳定性有限.
- 提高酶的热稳定性对于更广泛的工业用途至关重要.
研究的目的:
- 使用计算方法识别影响XynCDBFV热稳定的关键残留物.
- 通过局部定向突变发生,设计具有更好的热稳定性的XynCDBFV变体.
主要方法:
- 采用Rosetta Cartesian_ddG和Pythia (深度学习) 来进行残留物识别.
- 在确定的位置 (D57和G201) 进行了位点和突变发生.
- 通过测量最佳温度和热处理后的残留活性来评估热稳定性.
主要成果:
- 证实D57和G201的残留物对热稳定性至关重要.
- D57变种显示最佳温度增加10°C,残留活性更高.
- G201变种在最佳温度和改善的残留活性方面表现出5°C/10°C的增强.
- 结合D57和G201的突变导致了因表皮病的热稳定性下降.
- 与罗塞塔相比,Pythia表现出了更好的精度和速度平衡.
结论:
- D57和G201是增强XynCDBFV热稳定的关键地点.
- 计算预测与实验验证相结合,对于酶工程是有效的.
- 阴性表观影响了结合有益突变的有效性.
- 皮提亚为预测酶热稳定性改进提供了一种有价值的工具.
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