超大尺度时代的药物设计:从大规模并行QM/MM模拟的角度来看
Bharath Raghavan1,2, Mirko Paulikat1, Katya Ahmad1
1Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany.
Journal of chemical information and modeling
|June 15, 2023
概括
量子力学/分子力学 (QM/MM) 分子动力学 (MD) 模拟加速药物发现. 可扩展的MiMiC QM/MM框架在超大尺度机器上实现了强大的扩展,使得准确的联体蛋白结合研究成为可能.
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.
- 药物发现 药物发现
背景情况:
- 药物设计依赖于精确的分子模拟,但计算限制限制了第一原理量子力学/分子力学 (QM/MM) 分子动力学 (MD) 模拟的时间范围.
- 克服这些时间尺度的限制对于详细研究联结蛋白相互作用和反应机制至关重要.
研究的目的:
- 介绍计算化学 (MiMiC) QM/MM框架中的可大规模扩展的多尺度建模.
- 用第一原则准确度证明其在研究大型酶中的联结和反应方面的能力.
- 展示其在超大规模计算资源上的表现.
主要方法:
- 开发一个大规模可扩展的QM/MM MD接口 (MiMiC) 使用密度函数理论 (DFT) 用于QM区域.
- 将MiMiC框架应用于两项涉及与大型酶相关联体相互作用的案例研究.
- 通过对超过8万个核心进行强大的扩展测试来评估性能.
主要成果:
- 在MiMiC QM/MM框架允许第一原则准确模拟的联结蛋白相互作用和反应.
- 证明了MiMiC-QM/MM MD模拟的强大扩展,并行效率约为70%,最高可达>80,000个核心.
- 成功应用于研究在药理上相关的酶中的联体结合.
结论:
- MiMiC QM/MM 框架克服了先前在第一原理模拟中的时间尺度限制.
- 它是药物发现中的超大规模应用的一个有希望的工具,它结合了机器学习和统计力学.
- 能够准确地研究酶机制和对药物研究至关重要的配体结合.
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