对目标蛋白质降解的SE(3) - 等价三元复合体预测
Fanglei Xue1, Meihan Zhang2, Shuqi Li3
1ReLER Lab, AAII, University of Technology Sydney, Sydney, NSW, 2007, Australia.
Nature communications
|July 2, 2025
概括
DeepTernary是一种新的深度学习方法,可以准确地预测蛋白质降解的三元结构. 这种计算工具加速了针对挑战性疾病的新型向蛋白质降解剂 (TPD) 的设计.
科学领域:
- 生物化学和结构生物学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 向性蛋白质降解 (TPD) 使用像PROTAC和分子降解剂 (MGD) 这样的小分子来消除引起疾病的蛋白质.
- 由于难以确定目标蛋白,E3结合酶和降解剂之间形成的三元复合物的结构,TPD的合理设计具有挑战性.
研究的目的:
- 开发一种新的深度学习方法,DeepTernary,用于准确和快速预测蛋白质-合体-E3合酶三元复杂结构.
- 评估DeepTernary在PROTAC和MGD基准上的表现及其与实验降解强度的相关性.
主要方法:
- 一个端到端的深度学习模型,DeepTernary,采用一个SE(3) -equivariant编码器和基于查询的解码器.
- 关于策划的TernaryDB数据集的培训.
- 使用PROTAC和MGD基准与盲目对接协议进行评估.
主要成果:
- 在没有事先接触的情况下,DeepTernary在PROTAC基准上取得了最先进的表现.
- 在更具挑战性的MGD基准测试中表现出了显著的预测能力.
- 预测的结构显示埋藏的表面积和实验降解强度之间的相关性.
结论:
- DeepTernary提供了一种强大的计算工具,可以加速向蛋白质降解剂的合理设计和开发.
- 该方法推进了以结构为基础的药物设计领域,用于以前无法使用的目标.
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