Rapid Proteome-Wide Discovery of Protein-Protein Interactions With ppIRIS

Protein-protein interactions (PPIs) are central to cellular processes and host-pathogen dynamics across all domains of life, yet comprehensive interactome mapping remains challenging at the proteome scale. Experimental approaches provide only partial coverage, while existing computational methods often lack generalizability across species or are too resource-intensive for large-scale screening. Here, we introduce ppIRIS (protein-protein Interaction Regression via Iterative Siamese networks), a lightweight deep learning framework that integrates evolutionary and structural embeddings to predict PPIs directly from sequence. Evaluated on multi-species benchmarks, ppIRIS achieves state-of-the-art accuracy while enabling proteome-wide screening in minutes. Trained on curated bacterial datasets and applied to the Group A Streptococcus (GAS) proteome, ppIRIS identified functional clusters associated with virulence pathways, such as nutrient transport, stress response, and metal scavenging. Extending to cross-species prediction, ppIRIS recovered 56.2% of known GAS-human plasma interactions with enrichment in complement, coagulation, and protease inhibition pathways. Experimental validation confirmed novel predictions, demonstrating the applicability of ppIRIS for systematic discovery of bacterial and cross-species PPIs. The model together with a Google Colaboratory is freely available at github.com/lupiochi/ppIRIS.