基于人工智能预测PROTAC和分子接介导的三角复合体:对AlphaFold 3和Boltz-2进行比较评估
Lino Riepenhausen1, Anne-Christin Sarnow1, Dina Robaa1
1Department Medicinal Chemistry, Institute of Pharmacy, Martin-Luther-Universität Halle-Wittenberg, Halle, Saale, Germany.
Archiv der Pharmazie
|March 14, 2026
概括
像AlphaFold 3和Boltz-2这样的AI模型显示出预测由蛋白质溶解向金马 (PROTAC) 和分子合剂形成的三元复合体的前景,在准确性和速度上优于现有的方法.
科学领域:
- 计算生物学 计算生物学
- 结构生物学 结构生物学
- 人工智能在药物发现中的作用
背景情况:
- 蛋白质溶解向嵌合体 (PROTACs) 和分子剂通过形成三元复合体来促进向蛋白质降解.
- 这些三元复合体的in silico建模是具有挑战性的,因为它们的结构灵活性和蛋白质-蛋白质相互作用较弱.
研究的目的:
- 对基于扩散的AI模型进行基准测试,特别是AlphaFold 3和Boltz-2,用于预测PROTAC和分子接介导的三元复合体.
- 与现有的计算方法相比,评估这些AI模型的准确性和效率.
主要方法:
- 策划了来自蛋白质数据库的40个实验解析的PROTAC (25) 和分子 (15) 三元复合物的数据集.
- 使用复杂的根平均平方偏差 (RMSD) 和DockQ得分对结晶结构进行结构预测的评估.
- 使用内部信心指标和运行时间比较模型性能.
主要成果:
- 与其他当前方法相比,AlphaFold 3和Boltz-2都表现出卓越的准确性和运行时间.
- 根据复杂的RMSD和DockQ得分,Boltz-2显示出更高的预测准确度.
- 基于VHL的PROTAC的预测比基于CRBN的PROTAC更准确,对分子合物复合物的总体准确性很好.
- 常见的故障模式包括错误的全球安排和灵活的复合体中的扭曲,而单个组件通常是很好的建模.
结论:
- 基于扩散的AI模型,包括AlphaFold类型的模型,显示出基于PROTAC和分子三元复合物的结构预测的巨大潜力.
- 需要进一步开发以解决概括性的局限性,特别是对于较新的结构和复杂的结构动态.
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