对深度学习和经典建模方法进行比较研究,以预测冠状病毒主要蛋白质酶中的蛋白质 - 连接物结合姿势和亲和力
Yue Liu1, Haocheng Tang1, Taoyu Niu1
1Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
Journal of chemical information and modeling
|December 22, 2025
概括
像AlphaFold3这样的深度学习模型擅长预测蛋白质-配体结合的姿势,这对于药物设计至关重要. 这项研究表明,AlphaFold3生成的姿势可以改善药物效能的预测,为发现新疗法提供了强大的工具.
科学领域:
- 计算化学和结构生物学
- 药物发现和药物化学
背景情况:
- 准确预测蛋白质 - 连接体结合姿势和亲和关系对于基于结构的药物设计至关重要.
- 现有的方法包括分子对接,基于联体的叠加和深度学习方法.
研究的目的:
- 为了对不同的姿势生成策略进行基准测试,以预测蛋白质 - 配体结合.
- 为了评估结合姿势预测和随后的亲和度估计的准确性.
- 评估深度学习方法在药物发现中的潜力,特别是在没有晶体结构的情况下.
主要方法:
- 分子对接 (Glide,AutoDock Vina),基于联体的叠加 (FlexS) 和深度学习 (AlphaFold3,Boltz-2,DiffDock,Gnina) 的基准测试用于姿势预测.
- 利用了针对SARS-CoV-2和MERS-CoV主要蛋白酶 (Mpro) 的ASAP抗病毒挑战2025数据集.
- 采用基于机器学习的评分功能 (LRIP-SF),将MM-GBSA与ML集成,用于亲和度估计.
主要成果:
- 使用AlphaFold3的基于深度学习的建模显示出优越的姿势预测准确度 (88.1%的成功率,1.12 Å LRMSD).
- 通过LRIP-SF,AlphaFold3预测的姿势产生了最准确的功效预测,MERS-CoV和SARS-CoV-2 Mpro的MAE和RMSE低.
- 基于质的叠加 (FlexS) 提供了具有竞争力的亲和力预测,计算成本较低,尽管姿势准确性较低.
结论:
- 高质量的姿势预测对于准确的结合亲和度估计至关重要.
- 深度学习方法,特别是AlphaFold3,即使没有实验结构,也显示出基于结构的药物设计的重大前景.
- 全球灵敏度分析确定了有助于带结合的关键残留物,有助于理解药物向相互作用.
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