在一组多样化的蛋白质循环复合体上对10个对接程序的全面评估
Huifeng Zhao1,2, Dejun Jiang1,2, Chao Shen1,2
1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang China.
Journal of chemical information and modeling
|March 14, 2024
概括
这项研究评估了10个分子对接程序,用于循环的发现. rDock在采样方面表现出色,而Rosetta则是最适合合形状选择的,尽管对所有测试的程序来说,绑定亲和度预测仍然具有挑战性.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物物理学的生物物理.
背景情况:
- 循环是一种有前途的治疗药物,具有有利的特性.
- 分子对接对于循环的发现和应用扩展至关重要.
- 需要对蛋白循环系统的对接程序进行系统的评估.
研究的目的:
- 综合评估10个分子对接程序在蛋白质循环系统上的性能.
- 评估各种程序的采样,对接和评分能力.
- 为选择适合循环研究的对接工具提供见解.
主要方法:
- 构建一个广泛的基准数据集 (CPSet),包含493个蛋白质循环复合体.
- 评估了10个对接程序:罗塞塔,AutoDock CrankPep和8个蛋白质小分子对接程序.
- 评估采样功率,对接功率和评分功率,使用晶体和tLEaP生成的形状.
主要成果:
- 蛋白质小分子程序成功地从晶体结构中取样构造;rDock实现了94.3%的顶级-100采样成功.
- 从tLEaP生成的结构中,AutoDock CrankPep显示了最高的前100名抽样成功率 (29.6%).
- 罗塞塔的得分函数为符合性选择提供了87.6%的顶级-1对接成功率.
- 在所有评分函数中,绑定亲和度预测是有限的;MOE@Affinity dG显示了最高的相关性 (0.378).
结论:
- rDock和Rosetta在蛋白质循环对接的特定方面表现出强的表现.
- 结合不同的对接程序可能是最佳的各种应用程序.
- 本评估为在循环药物发现中选择合适的对接工具提供了指导.
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