基于生物规则设计蛋白相互作用预测新方法，提升准确率。
Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach
- 引入生物启发的L3路径规则，构建带虚拟路径的图提示
- 在多个数据集上显著提升预测性能，最高增益达12.3%
- 可作为插件模块适配各类蛋白预测模型
蛋白质-蛋白质相互作用（PPI）是细胞功能和疾病机制的基础。现有学习型预测方法侧重于学习强大的蛋白表示，却忽视了设计专门的分类头，主要依赖串联或点积等通用聚合方式，缺乏生物学意义。受生物“L3规则”启发——一对蛋白间存在多个长度为3的路径则表明其相互作用可能性高，本研究填补了这一空白，提出一种基于生物先验的新型分类器。我们实证发现主流PPI数据集强烈支持该规则。为此，我们提出一种名为L3-PPI的图提示学习方法，通过蛋白表示生成带有虚拟L3路径的提示图，并控制路径数量。该方法将蛋白嵌入对的分类转化为生成图的图级分类任务。此轻量级模块可作为即插即用组件无缝集成至各类PPI预测器中，注入互补性交互先验以增强性能。大量实验表明，相比先进基线模型，L3-PPI在多个基准上实现显著提升，最高增益达12.3%。
原文摘要 · Abstract (English)
Protein-protein interactions (PPIs) are fundamental to cellular function and disease mechanisms. Current learning-based PPI predictors focus on learning powerful protein representations but neglect designing specialized classification heads. They mainly rely on generic aggregating methods like concatenation or dot products, which lack biological insight. Motivated by the biological "L3 rule", where multiple length-3 paths between a pair of proteins indicate their interaction likelihood, our study addresses this gap by designing a biologically informed PPI classifier. In this paper, we provide empirical evidence that popular PPI datasets strongly support the L3 rule. We propose an L3-path-regularized graph prompt learning method called L3-PPI, which can generate a prompt graph with virtual L3 paths based on protein representations and controls the number of paths. L3-PPI reformulates the classification of protein embedding pairs into a graph-level classification task over the generated prompt graph. This lightweight module seamlessly integrates with PPI predictors as a plug-and-play component, injecting the interaction prior of complementarity to enhance performance. Extensive experiments show that L3-PPI achieves superior performance enhancements over advanced competitors.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。