模拟酶反应和突变通过直接机器学习/分子力学模拟
Xinhu Sha1, Zhuo Chen1, Daiqian Xie1,2
1Institute of Theoretical and Computational Chemistry, State Key Laboratory of Coordination Chemistry, Key Laboratory of Mesoscopic Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.
Journal of chemical theory and computation
|April 24, 2025
概括
这项研究引入了一种用于酶反应建模的新机器学习方法,提高了量子力学/分子力学模拟的准确性. REANN 方法可以更快,更准确地预测酶活性和突变.
科学领域:
- 计算化学是一种计算化学.
- 生物物理学的生物物理.
- 酶动力学 酶动力学
背景情况:
- 使用量子力学/分子力学 (QM/MM) 模拟的酶反应的准确建模受阻于描述静电合的挑战.
- 现有的方法难以高效准确地捕捉QM和MM子系统之间的相互作用.
研究的目的:
- 开发一种基于机器学习的新方法,用于改进酶反应的QM/MM模拟.
- 准确建模静电合并预测酶活性和突变性质.
主要方法:
- 提出了一种重量化机械嵌入 (ME) 递归嵌入原子神经网络 (REANN) 方法.
- 在真空中使用电荷平衡,训练了QM子系统的潜在能量和点电荷.
- 在分子动力学模拟后使用热力学扰动纠正了MM极化效应.
主要成果:
- 成功复制了阿司匹林化COX-1和COX-2的自由能量曲线,并且具有化学准确性.
- 准确地预测了COX-2突变体 (R513A) 的自由能量屏障,<0.5 kcal mol−1 的差异.
- 与传统的QM/MM方法相比,计算速度提高了80倍.
结论:
- 该REANN方法提供精确和快速的预测酶活性和突变效应.
- 这种方法显著提高了生物化学反应的QM/MM模拟的效率.
- 该方法对未来在虚拟查和药物发现方面的应用具有前景.
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