吸引空洞2.0:提高小分子对接的灵活性和稳定性
Ute F Röhrig1, Mathilde Goullieux1, Marine Bugnon1
1Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland.
Journal of chemical information and modeling
|June 7, 2023
概括
改进的吸引空洞2.0 (AC 2.0) 算法增强了分子对接的准确性和灵活性. AC 2.0在重复和盲目对接方面表现优于其他方法,为药物发现提供了可靠的采样.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 分子对接预测了连接体-宏分子结合.
- 像GOLD和AutoDock Vina这样的现有算法都有自己的局限性.
- 吸引空洞 (AC) 算法以前显示出具有竞争力的性能.
研究的目的:
- 为了介绍和评估AC 2.0,一个增强的分子对接算法.
- 为了提高采样稳定性和对接灵活性 (速度与准确性).
- 通过使用PDBbind.bind来对AC 2.0与已建立的方法进行基准测试.
主要方法:
- 使用了PDBbind核心集 (2016版本) 与285个综合体进行基准测试.
- 在重组,盲目对接和交叉对接场景中评估性能.
- 在虚拟选中评估了评分功能的准确性和丰富因素.
主要成果:
- AC 2.0在重制中取得了73.3%的成功率,超过了GOLD (63.9%) 和Vina (58.0%).
- 由于其力场得分和采样,在盲目对接中表现出强的性能.
- 在交叉对接中显示了42.5%的成功率,与GOLD相比,优于Vina.
结论:
- AC 2.0 在分子对接的准确性和效率上提供了显著的改进.
- 该算法的稳定性和灵活性使其适合各种对接任务.
- AC 2.0有助于识别潜在的候选药物和评估实验数据的质量.
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