基于深度学习的预测酶最佳pH值和点突变的设计,以改善抗酸性
Sizhe Qiu1, Nan-Kai Wang2,3, Yishun Lu1,4
1Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom.
ACS synthetic biology
|November 21, 2025
概括
CatOpt是一款新的深度学习工具,可以准确预测酶的最佳pH值,并识别pH值偏好. 这种计算方法有助于在酶设计中改善pH依赖活动.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 酶工程是什么? 酶工程是什么?
背景情况:
- 酶活性受到pH值的显著影响,因此需要准确预测最佳pH值.
- 了解pH依赖的酶动力学对于各种生物技术应用至关重要.
研究的目的:
- 开发一个准确的深度学习预测器,用于酶的最佳pH值.
- 创建一个用于识别酶pH偏好和指导酶设计的计算工具.
主要方法:
- 开发了CatOpt,这是一种用于预测酶最佳pH值的深度学习模型.
- 用了残留注意重量来解释模型的可解释性.
- 应用CatOpt用于分类酸性/性酶和预测由突变引起的pH值变化.
主要成果:
- CatOpt的表现优于现有的预测指标,RMSE = 0.833和R2 = 0.479.
- 该模型通过残留注意力权重证明了可解释性.
- 成功预测了pH值的变化,并引导了一种突变,在低pH值下增强了7%左右的酶活性.
结论:
- CatOpt是一种有效的计算工具,用于预测酶的最佳pH值和pH值偏好.
- 该模型显示出对*in silico*酶设计的前景,特别是对于pH依赖的活动.
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