在氨氨酶中以深度学习驱动的半理性设计,以提高催化效率
Xudong Lü1, Shuai Fan1, Ruijie Lü1
1NHC Key Laboratory of Biotechnology of Antibiotics, Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100050, China.
International journal of biological macromolecules
|February 21, 2025
概括
深度学习确定了增强Anabaena variabilis氨氨酸氨酶 (AvPAL) 活性的关键部位. 突变分子的催化效率提高了3.4倍,提供了一个新的酶工程策略.
科学领域:
- 酶工程是什么?酶工程是什么?
- 生物催化剂是一种生物催化剂.
- 计算生物学是一种计算生物学.
背景情况:
- 氨氨酶 (PAL) 在农业,工业和医药领域具有广泛的应用.
- 阿纳贝纳变异性PAL (AvPAL) 在临床上用于基尿症 (PKU).
- 提高酶催化效率仍然是一个重大挑战.
研究的目的:
- 采用深度学习引导的策略来识别AvPAL中的修改站点.
- 为了提高AvPAL的催化效率和活性.
- 了解增强酶活性的结构基础.
主要方法:
- 深度学习用于网站识别.
- 高通量查和酶定量测试用于突变验证.
- 组合性突变发生是组合性突变发生.
- 分子动力学模拟的模拟.
- 结构分析 (叠加,残留相互作用网络,动态交叉相关矩阵)
主要成果:
- 在33种向突变物中,26种突变物表现出增强的活性.
- 催化效率 (kcat/KM) 提高了高达 3.4 倍 (M222N/N36S).
- 活动提升至2.4倍 (M222N/I149D).
- 接近攻击形状的稳定与活动增强相关.
- 突变M222N/I149D在循环81-94.4中显示出有利的形状变化.
结论:
- 深度学习有效地指导了AvPAL的工程.
- 组合性突变发生显著提高了AvPAL的催化效率和活性.
- 结构和动态分析揭示了改善酶功能的机制.
- 这项研究提出了一种可行的方法,用于设计高效的PAL酶.
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