Hierarchical Contrastive Learning for Protein–Protein Interaction Prediction Across Organisms

Abstract

With advances in biomedical technologies and the continued expansion of experimental resources, biological data are growing rapidly in both scale and complexity. Contrastive learning provides an effective framework for integrating heterogeneous biological information. However, many protein–protein interaction (PPI) prediction methods still represent protein sequences and annotations as flat features and do not explicitly model hierarchical biological relationships among protein families, clans, and functional annotations. Here, we introduce HIPPO (HIerarchical Protein–Protein interaction prediction across Organisms), a hierarchical contrastive learning framework for PPI prediction. HIPPO aligns protein sequence representations with structured biological attributes. Across intra-species benchmark PPI datasets, HIPPO improves the average micro-F1 by 2.9% compared with the best baseline across the evaluated splits. In the host–pathogen interaction benchmark, HIPPO achieves the highest AUROC under the standard split (0.731) and the second-best AUPRC (0.332). Under leave-one-virus-family-out evaluation, HIPPO obtains the best AUROC on Papillomaviridae (0.603) and Retroviridae (0.612), while also showing family-dependent transfer behavior. Ablation experiments support the contribution of hierarchical feature integration, and attention-based residue attribution provides preliminary evidence that the learned representations highlight interface-related residues. Together, these results suggest that structured biological knowledge can improve representation learning for PPI prediction across diverse and imbalanced datasets.