重温蛋白质-蛋白质对接:一个系统的评估框架
Linlong Jiang1,2, Ke Zhang2,3, Kai Zhu1,2
1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Journal of chemical information and modeling
|September 18, 2025
概括
一个新的框架对蛋白质-蛋白质对接方法进行了基准测试. AlphaFold3在预测复杂结构方面表现出色,优于传统工具,但深度学习模型在分布外概括方面扎.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物学是结构生物学.
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白质与蛋白质之间的相互作用对于生物过程至关重要.
- 精确预测蛋白质复杂结构对于理解机制和药物设计至关重要.
- 蛋白质-蛋白质对接方法需要严格的评估.
研究的目的:
- 为评估蛋白质-蛋白质对接方法建立一个全面的基准测试框架.
- 为了比较传统和基于深度学习 (DL) 的对接方法.
- 评估DL模型的分发外 (OOD) 泛化能力.
主要方法:
- 使用DockingBenchmark 5.5,AACBench和PPCBench数据集开发了一个基准测试框架.
- 评估了11种对接方法,包括传统的 (HDOCK,PatchDock,PIPER,ZDOCK) 和基于DL的 (AlphaFold3,AlphaFold-Multimer等). ) 的情况.
- 评估了灵活对接,抗体-抗原复合对接和OOD概括的性能.
主要成果:
- 在对抗apo结构时,AlphaFold3表现出优越的前5名成功率 (77.98%) .
- 在对抗全息结构时,HDOCK的成功率很高 (85.24%),但对抗阿波的成功率较低.
- 在抗体-抗原对接方面,AlphaFold3是最准确的 (31.78%的成功率),并且表现优于AlphaFold-Multimer.
- 所有DL模型都在OOD PPCBench数据集上显示性能降低.
结论:
- 拟议的框架允许对各种蛋白质-蛋白质对接方法进行系统评估.
- AlphaFold3表现出强大的性能,特别是在对抗阿波结构和抗体-抗原建模中.
- 目前基于DL的对接方法在OOD泛化方面面临挑战,突出了未来改进的领域.
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