Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach
2026-05-11 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors studied how proteins interact, which is important for cell functions and diseases. They noticed that current computer methods often miss out on using special biological clues when deciding if two proteins connect. Using the "L3 rule," which says that proteins with many paths of length 3 between them are more likely to interact, they designed a new method called L3-PPI. This method builds a special graph to better decide interactions and can easily work with existing predictors. Their tests showed that adding this biological insight helps improve the accuracy of predicting protein interactions.
protein-protein interactionsL3 rulegraph prompt learningprotein embeddingsgraph-level classificationmachine learningbiological networkspath length in graphsinteraction prediction
Authors
Ziqi Gao, Chenyi Zi, Zijing Liu, Ziqiao Meng, Yu Li, Jia Li
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.