通过机器学习加速可靠的蛋白质药物系统的多尺度量子精制,使其成为可能
Zeyin Yan1, Dacong Wei1, Xin Li1
1Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China.
Nature communications
|May 16, 2024
概括
机器学习潜力加速了生物宏分子结构的量子精制,提高了药物开发效率,并提供了对蛋白质药物复合物的原子洞察力.
科学领域:
- 生物化学和结构生物学.
- 计算化学是一种计算化学.
- 药物发现 药物发现
背景情况:
- 生物宏分子结构对于药物开发和生物催化剂至关重要.
- 量子精制 (QR) 提高了生物宏分子的结构质量,但在计算上是昂贵的.
- 现有的量子力学/分子力学 (QM/MM) 对QR的设置是复杂的.
研究的目的:
- 为生物宏分子结构开发一种更有效,更准确的QR方法.
- 为了降低与传统QR方法相关的计算成本.
- 为蛋白质与药物相互作用提供更深入的原子洞察力.
主要方法:
- 将机器学习潜力 (MLP) 纳入多个规模的ONIOM (QM:MM) 计划.
- 使用两级MLP来提高准确性和克服局限性.
- 将开发的MLPs+ONIOM基于QR方法应用于蛋白质药物复合体.
主要成果:
- 实现了量子力学 (QM) 级准确性,显著提高了计算效率.
- 成功提炼蛋白-药物复合体,包括SARS-CoV-2主要蛋白酶.
- 提供了不同的结合和非结合形式的nirmatrelvir.com的计算证据.
结论:
- 结合基于ONIOM的QR,MLP提供了一种强大而高效的方法来精制生物宏分子结构.
- 这种方法加快了蛋白质药物复合物的分析,有助于药物开发.
- 这种方法为了解药物机制提供了前所未有的原子细节.
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