SAMPL9主机-客户盲目挑战:对具有约束力的自由能源预测准确性的概述
Martin Amezcua1, Jeffry Setiadi2, David L Mobley1,3
1Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, California 92697, USA. dmobley@mobleylab.org.
Physical chemistry chemical physics : PCCP
|March 6, 2024
概括
在SAMPL9挑战中,对接方法在预测柱[n]-竞技场 (WP6) 系统中结合的自由能量方面表现优于其他方法. 循环德克斯-提亚的预测不那么准确,突出了需要更好的计算模型的需求.
科学领域:
- 超分子化学 超分子化学
- 计算化学计算化学
- 化学物理 化学物理
背景情况:
- 在SAMPL9 (蛋白质和体建模的统计评估) 盲目挑战中,评估了用于预测结合自由能量的计算方法.
- 挑战的重点是宏循环宿主,特别是支柱[n]场 (WP6) 和循环德克斯特林 (bCD,HbCD),与各种客人相互作用.
研究的目的:
- 评估各种计算方法在预测宿主-客人结合自由能量的准确性.
- 确定不同主机-客户系统最有效的方法,并为未来的研究提供见解.
主要方法:
- 参与者使用了一系列计算技术,包括分子动力学 (MD),基于分子描述器的机器学习 (ML) 和对接.
- 使用力场,ML模型和对接模拟来预测特定的主机-客机复合体的结合自由能量.
主要成果:
- 对于柱[n] - 阵地 (WP6) 系统,对接方法实现了最高的精度 (RMSE 1.70 kcal mol-1),超过了MD和ML方法.
- 机器学习模型显示WP6的准确性很好 (RMSE 2.04 kcal mol-1),而力场方法提供了更好的实验相关性.
- 在循环德林-提亚挑战中,ATM方法表现最好 (RMSE < 1.86 kcal mol-1),尽管与WP6相比,整体相关性指标较低.
结论:
- 对接和ML方法对预测结合的自由能量具有前景,但力场可能为某些系统 (如WP6) 提供更好的实验相关性.
- 了解宿主微态,宿主方向和特定的化学相互作用 (例如WP6-G4) 对于准确的结合能量预测至关重要.
- 未来的研究应该考虑结合性自由能量计算对客人定位的敏感性,并使用ML探索先进的力场参数化.
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