Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach
For researchers in computational biology and drug discovery, this work provides a model-agnostic way to inject biological priors into PPI prediction, improving accuracy without requiring changes to existing protein representation learning methods.
The paper addresses the lack of biologically informed classification heads in protein-protein interaction (PPI) prediction by introducing L3-PPI, a plug-and-play module that incorporates the L3 rule (multiple length-3 paths between proteins indicate interaction). The method achieves superior performance enhancements over advanced competitors on popular PPI datasets.
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.