Accurate Protein-Protein Interaction Prediction: Based on Multiview Heterogeneous Graph Autoencoders and Random...

Protein-protein interaction (PPI) and their interaction sites [PPI site (PPIS)] hold immense potential for elucidating cellular mechanisms and advancing targeted drug development. While deep learning has driven progress in PPI research by capturing protein features, it remains limited by its overreliance on sequence information and inability to effectively integrate protein internal structural features. To address these challenges, we propose MEGAE, a novel model capable of achieving high-precision prediction of PPI and PPIS. MEGAE reconstructs amino acid microenvironments through a vector quantization autoencoder, integrating physicochemical properties, structural details, and sequence data to provide a comprehensive representation of proteins. We innovatively introduce a multiview random masking training strategy, introducing controlled randomness during the reconstruction process to enhance the robustness of microenvironment embeddings. The model combines these fused embeddings with protein graphs and protein interaction networks, leveraging graph neural networks (GNNs) to capture multilevel relationships from local amino acid interactions to global signal network connections-thereby achieving precise predictions. Experimental results demonstrate that MEGAE outperforms state-of-the-art sequence- and structure-based methods across multiple datasets, exhibiting higher accuracy in predicting interaction types and interaction sites. This advancement underscores the potential of microenvironment-aware modeling in uncovering complex protein interactions.