PLiSAGE:通过多模式的表面和几何编码来增强蛋白质-连接体相互作用预测
Tianci Wang1, Guanyu Qiao2, Guohua Wang2
1College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China.
Bioinformatics (Oxford, England)
|November 9, 2025
概括
我们开发了PLiSAGE,这是一种使用3D结构和表面几何学的新方法,可以准确预测蛋白质-连接体相互作用. 这种方法通过捕获详细的约束性信息来增强药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 预测蛋白质-连接体相互作用对于理解药物发现和生物过程中的分子识别至关重要.
- 当前的方法通常依赖于有限的结构或序列数据,阻碍特征学习,忽视表面几何/化学细节.
- 这限制了对具有约束力的事件的解释性和机制性洞察力.
研究的目的:
- 开发一个多式模式框架,用于准确预测蛋白质 - 配体相互作用.
- 整合3D结构和表面几何信息,以提高预测能力.
- 提高结合相互作用的解释性和机制理解.
主要方法:
- 介绍了PLiSAGE,这是一个结合3D结构和表面几何嵌入的框架.
- 通过无监督对比学习和点云重建,利用编码器的联合预训练.
- 代表蛋白质表面作为细分的点云补丁,并使用变压器编码器对空间依赖.
主要成果:
- 与基线相比,PLiSAGE在结合亲和力预测和相互作用分类方面取得了卓越的表现.
- 该模型有效地从蛋白质表面捕获细粒度的几何和化学线索.
- 废弃性研究证实了表面特征的重要性以及预训练策略的概括性.
结论:
- 通过整合结构和表面数据,PLiSAGE提供了一种可靠和准确的方法来预测蛋白质-连接体相互作用.
- 该框架增强了蛋白质表示的表达能力,提高了预测性能.
- 这种方法有望促进药物发现和理解分子识别.
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