将 PROTAC 与 POI-E3L 综合体对接是多么可行的? 测试基于物理和基于ML的对接工具
Roberto Jiménez-Boi1, Raúl Miñán2, Chiara Pallara2
1Data Science Dpt., Almirall S.A., Laureà Miró 408-410, 08980 St. Feliu de Llobregat, Barcelona, Spain.
Journal of chemical information and modeling
|November 12, 2025
概括
向蛋白质降解 (TPD) 使用分子来降解蛋白质. 这项研究对对接工具进行了基准测试,用于建模化向嵌合体 (PROTACs),发现rDock是有效的,并强调了在PROTAC计算建模中需要接受器灵活性.
科学领域:
- 计算化学和药物发现
- 结构生物学和分子建模.
- 药理学和化学生物学的化学生物学.
背景情况:
- 向蛋白质降解 (TPD) 是一种新的治疗策略.
- 蛋白质分解向嵌合体 (PROTACs) 是一个关键的TPD模式.
- 由于它们的复杂性,PROTACs的计算建模存在重大挑战.
研究的目的:
- 评估基于物理和机器学习 (ML) 的对接工具来建模PROTAC-E3L三元复合体.
- 建立PROTAC对接策略的绩效基准.
- 为 PROTAC discovery 中的计算建模提供指导方针.
主要方法:
- 对43个经过实验解决的POI-PROTAC-E3L结构进行分析.
- 基准测试包括GLIDE,MOE,rDock,DiffDock和GeoDirDock. 这三种类型的测试都是非常重要的.
- 纳入范德瓦尔斯缩放,键约束,NMA以获得灵活性,以及AlphaFold2结构.
主要成果:
- rDock具有高采样性,其性能优于其他基于物理的对接工具.
- 基于ML的工具显示出具有竞争力的RMSD,但普遍性有限,需要后处理.
- 正常模式分析 (NMA) 通过结合受体灵活性,显著提高了对接精度.
结论:
- rDock是PROTAC对接的强有力的候选者,特别是在高采样的情况下.
- 在PROTAC应用中,ML对接工具需要经过仔细的验证和改进.
- 通过NMA整合受体灵活性对于准确的PROTAC三元复合模型至关重要.
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