热力学优化的机器学习反应坐标,用于疏水性联体解离
Eric R Beyerle1, Pratyush Tiwary1,2
1Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States.
The journal of physical chemistry. B
|January 11, 2024
概括
占主导地位的自由能量屏障,用于疏水性联体解绑. 一个深度学习模型揭示了甲和烯从结合口袋中解离的独特的溶解和湿机制.
科学领域:
- 计算化学和生物物理学
- 分子动力学和自由能量计算.
- 机器学习在热力学中的应用.
背景情况:
- 连接体解结的自由能量是分子相互作用的关键决定因素.
- 量化个人对释放自由能量的热和热贡献是具有挑战性的.
- 疏水性相互作用在配体结合和解离中起着重要作用.
研究的目的:
- 开发和应用一个深度学习框架来建模疏水性联结体解结.
- 识别和分析热力学贡献 (和) 解束自由能量概况.
- 阐明不同尺寸的疏水性联体 (甲和C60富勒伦) 的解结的具体机制.
主要方法:
- 使用修改后的深度学习框架来学习热力学优化的反应坐标.
- 模拟的甲和C60富勒烯的全原子模拟从水中的疏水口袋中解结.
- 在学习反应坐标上进行特征重要性分析,以解释热力学力.
主要成果:
- 甲和C60富勒解结的自由能量屏障主要由性因素决定.
- 甲解结是由甲溶解驱动的,而富勒解结则涉及口袋湿接着是富勒湿.
- 学习的反应坐标表明,从绑定口袋的简单距离的直接重要性很低.
结论:
- 开发的深度学习方法为疏水性联体解离提供了宝贵的热力学见解.
- 在疏水性联体解结的自由能量障碍中起着主导作用.
- 独特的溶解和湿机制标志着小型与大型疏水性联体的解结路径.
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