Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach

TL;DR

This paper introduces L3-PPI, a biologically inspired, plug-and-play graph prompt learning method that improves protein-protein interaction prediction by leveraging the L3 rule, supported by empirical evidence.

Contribution

It proposes a novel, biologically informed graph prompt learning approach that incorporates the L3 rule as an interaction prior, enhancing PPI prediction performance.

Findings

L3-PPI outperforms existing PPI predictors in experiments.

Empirical evidence supports the biological L3 rule in PPI datasets.

The method seamlessly integrates with existing predictors as a plug-and-play module.

Abstract

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.