通过使用多尺度分子模拟来理解整个酶反应周期
Shingo Ito1, Chigusa Kobayashi1, Kiyoshi Yagi2
1Computational Biophysics Research Team, RIKEN Center for Computational Science, 7-1-26 Minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.
Current opinion in structural biology
|September 12, 2025
概括
先进的分子模拟,包括分子动力学 (MD) 和QM/MM,加上机器学习 (ML),现在可以准确地模拟酶催化,包括构造变化和化学反应.
科学领域:
- 生物化学 生物化学
- 计算化学的计算化学
- 分子生物学分子生物学
背景情况:
- 酶通过基质结合,化学转化和产品释放来催化反应.
- 酶的结构变化经常伴随着这些催化事件.
- 理论分析是复杂的,因为偶联事件和形态动态.
研究的目的:
- 要总结最近的酶功能的多尺度分子模拟.
- 要突出在酶催化过程中多个微观事件的整合.
- 讨论机器学习在模拟酶催化中的作用.
主要方法:
- 先进的分子模拟,包括分子动力学 (MD).
- 混合量子力学/分子力学 (QM/MM) 模拟.
- 多尺度模拟包括多个微观事件.
- 机器学习 (ML) 方法用于描述微观事件.
主要成果:
- 多尺度模拟可以预测酶反应周期的方向.
- 准确的自由能量变化对于预测反应周期至关重要.
- 在描述催化事件时,ML方法实现了量子化学的准确性.
- ML/MM模拟为研究酶催化提供了新的发展.
结论:
- 将酶构造变化与化学反应相结合,是理解催化循环的关键.
- 机器学习显著提高了酶催化模拟的准确性和范围.
- 先进的计算方法为酶机制提供了前所未有的洞察力.
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