使用DegradeMaster精确的PROTAC目标降解预测
Jie Liu1, Michael J Roy1, Luke Isbel1,2
1South Australian Immunogenomics Cancer Institute (SAiGENCI), The University of Adelaide, Adelaide, South Australia 5005, Australia.
Bioinformatics (Oxford, England)
|July 15, 2025
概括
新的人工智能工具DegradeMaster通过使用3D分子结构和未标记的数据,准确地预测蛋白质溶解向嵌合体 (PROTAC) 降解能力. 这促进了向蛋白质降解,并加速了药物发现.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
背景情况:
- 向蛋白解酶的仿真体 (PROTACs) 提供了一种新的方法来降解引起疾病的蛋白质,包括以前的蛋白质.
- 没有药物可用的无毒药.
- 目标,目标,目标.
- 目前用于预测PROTAC有效性的深度学习方法经常忽视关键的3D结构信息或依赖有限的标记数据集.
研究的目的:
- 开发一种更准确的计算方法来预测PROTAC向的蛋白质降解.
- 通过结合3D空间信息和利用未标记的数据来解决现有的深度学习模型的局限性.
主要方法:
- 开发了DegradeMaster,这是一个基于神经网络的半监督的E(3) -等价图形预测器.
- 集成的3D几何约束使用一个E(3) -equivariant图编码器.
- 使用基于内存的伪标记策略来增强使用未标记数据的训练.
- 设计了一个相互关注的聚合模块,用于可解释的图形表示.
主要成果:
- 德格拉德马斯特显著超过了最先进的基线,改善了AUROC的10.5%.
- 在预测特定的PROTAC候选物的可降解性方面取得了很高的准确性 (例如,BRD9上的VZ185的88.33%).
- 通过注意力重量可视化,证明了在弹头和E3连接体区域中结构信息的重要性.
结论:
- 德格拉德马斯特显示出加速发现新型PROTAC化合物的巨大潜力.
- 该模型能够解释功能性PROTAC组件,这突显了3D结构数据在预测中的价值.
- 这项工作为推进有针对性的蛋白质降解策略提供了一个强大的工具.
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