机器学习/分子力学最终状态校正与机械嵌入的评估,以计算相对蛋白质-质结合的自由能量
Johannes Karwounopoulos1, Mateusz Bieniek1, Zhiyi Wu1
1Exscientia, Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K.
Journal of chemical theory and computation
|January 3, 2025
概括
机器学习 (ML) 潜能提高了准确性,但速度很慢. 混合ML / MM方法提供了一个妥协,但重新调整分子力学 (MM) 力场更有效,并为药物发现提供类似的结合能量结果.
科学领域:
- 计算化学的计算化学
- 分子建模分子建模
- 药物发现 药物发现 药物发现
背景情况:
- 机器学习 (ML) 潜力通过结合量子力学效应来提高分子模拟的准确性,超越了传统的分子力学 (MM).
- 然而,机器学习模拟带来了大量的计算成本,与更快的MM方法相比,创造了速度和准确性的权衡.
- 机械嵌入的混合ML / MM方法提供了一个潜在的解决方案,处理连接物与ML的分子内相互作用和MM的蛋白质-连接物相互作用.
研究的目的:
- 评价声称机械嵌入的ML/MM通过准确描述连接体分子内相互作用,改善了蛋白质-连接体结合的自由能量计算.
- 将ML/MM与机械嵌入的性能与将MM二面电位与ML或量子力学 (QM) 理论水平相匹配的替代策略进行比较.
- 评估这些不同方法的计算效率和准确性,以计算蛋白质 - 配体结合的自由能量.
主要方法:
- 在四个基准系统中执行了108个相对约束力的自由能源计算.
- 经过测试的混合ML/MM与机械嵌入使用ML潜力 (ANI-2x).
- 开发并测试了一种方法,以将MM二面电位与ML (ANI-2x,AIMNet2) 和QM (ωB97M-D3(BJ) /def2-TZVPPD) 理论水平相匹配.
主要成果:
- 在标准MM,具有ML配备的扭力潜力的MM和ML/MM模拟之间没有观察到平均绝对误差 (0.8-0.9 kcal mol-1) 的统计学上显著差异.
- 与ML/MM最终状态校正相比,重新装配策略导致具有约束力的自由能量计算的差异较小.
- 在计算上,重新设置MM扭矩潜力比对蛋白质-联结体复合物的密集ML/MM模拟更好.
结论:
- 一个参数化良好的MM力场的性能可与蛋白质-联体结合的简单机械嵌入ML/MM模拟相提并论.
- 修复MM扭矩潜力的参数是比使用机械嵌入的ML/MM模拟更有效的.
- 先进的ML/MM方案和改进的融合对于计算机引导药物发现的可靠应用是必要的.
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