在GPCR药物发现中的深度学习:对准准确的结路径的基准测试
Luuk R Hoegen Dijkhof1,2, Teemu K E Rönkkö1,2, Hans C von Vegesack1,2
1Department of Drug Design and Pharmacology, University of Copenhagen, Jagtvej 160, 2100 Ø, Copenhagen, Denmark.
Briefings in bioinformatics
|April 26, 2025
概括
深度学习模型准确地预测G蛋白合受体 (GPCR) 和激素相互作用,有助于药物发现. 与较新的模型相比,AlphaFold 2.3显示出优越的结合姿势预测.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 深度学习 (DL) 通过从序列中预测蛋白质结构,彻底改变了基于结构的药物发现.
- 最近的DL模型显示,预测多链蛋白质复合体的准确性增加.
研究的目的:
- 评估DL工具来预测G蛋白结合受体 (GPCR) 和它们的联体之间的相互作用.
- 为了对AlphaFold 2.3 (AF2),AlphaFold 3 (AF3) 和其他DL模型的GPCR-结合预测进行基准测试.
主要方法:
- 包括AF2,AF3,Chai-1,NeuralPLexer,RoseTTAFold-AllAtom,Peptriever,ESMFold和D-SCRIPT在内的DL工具的基准测试. 这是一个非常好的方法.
- 评估结分类,配体发现和结合姿势的准确性.
- 对预测结构准确性的信心评分进行分析.
主要成果:
- 结构感知DL模型在结分类中表现优于语言模型 (AUC 0.86).
- 与AF3和Chai-1相比,AF2表现出优异的结合姿势预测 (94%的准确性).
- 信任分数与绑定模式准确性相关,指导界面预测解释.
结论:
- DL模型可靠地重新发现结体,并可以帮助GPCRs的药物发现.
- 该研究为GPCR向治疗选择最佳DL工具提供了一份指南.
- 为未来的模型评估提供了一个独立的基准测试集.
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