针对蛋白质模拟的一般神经网络力场:完善蛋白质中的分子内相互作用
1Department of Chemistry, Duke University, Durham, North Carolina 27708, USA.
The Journal of chemical physics
|July 11, 2023
概括
神经网络力场 (CHARMM-NN) 为蛋白质模拟提供准确的QM级能量近似值. 虽然对于结合相互作用是成功的,但对于蛋白质-水和碎片间相互作用需要进一步改进.
科学领域:
- 计算化学计算化学
- 生物物理学的生物物理.
- 机器学习 机器学习
背景情况:
- 分子动力学 (MD) 模拟对于了解原子细节中的蛋白质动力学至关重要.
- MD模拟的准确性在很大程度上取决于力场的质量.
- 量子力学 (QM) 计算提供了高精度,但对于大型系统来说,计算成本昂贵.
研究的目的:
- 为蛋白质开发通用和可转移的神经网络 (NN) 力场,命名为CHARMM-NN.
- 为了提高分子机械 (MM) 力场中QM级精度的计算效率.
- 为了使这些新型力场能够在现有的MD模拟程序中实现.
主要方法:
- 基于CHARMM力场的NN力场的构造,使用基于残留的系统分子碎片 (rSMF) 方法的27个蛋白质碎片.
- 在原子类型和MM兼容的输入特征 (键,角度,二面体,无键项) 上训练NN模型.
- 混合方法结合rSMF/NN用于初级能量计算和CHARMM力场用于通过机械嵌入的非绑定相互作用.
主要成果:
- 在CHARMM-NN中,CHARMM-NN证明了对二中结合相互作用的QM局部最小值的高度准确的近似.
- 对几何数据,相对潜在能量和结构重组能量的验证证实了绑定项的准确性.
- MD模拟突出了改善蛋白质-水和碎片间非结合相互作用的表现的需要.
结论:
- CHARMM-NN代表了开发蛋白质准确和可转移的机器学习力场的重大进展.
- 该方法在MD模拟中增强QM级准确性,特别是在绑定交互方面,非常有希望.
- 未来的工作应该集中在完善非结合相互作用和蛋白质-水表示,以超越当前的QM/MM准确度水平.
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