Hierarchical Multi-Label Contrastive Learning for Protein-Protein Interaction Prediction Across Organisms

TL;DR

This paper introduces HIPPO, a hierarchical contrastive learning framework for protein-protein interaction prediction across species, leveraging biological hierarchies to improve accuracy, robustness, and zero-shot transferability in low-data scenarios.

Contribution

HIPPO is the first hierarchical contrastive learning approach that aligns protein features with biological hierarchies for cross-species PPI prediction, enhancing transferability and robustness.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Demonstrates strong zero-shot transferability to other species.

Shows robustness in low-data regimes.

Abstract

Recent advances in AI for science have highlighted the power of contrastive learning in bridging heterogeneous biological data modalities. Building on this paradigm, we propose HIPPO (HIerarchical Protein-Protein interaction prediction across Organisms), a hierarchical contrastive framework for protein-protein interaction(PPI) prediction, where protein sequences and their hierarchical attributes are aligned through multi-tiered biological representation matching. The proposed approach incorporates hierarchical contrastive loss functions that emulate the structured relationship among functional classes of proteins. The framework adaptively incorporates domain and family knowledge through a data-driven penalty mechanism, enforcing consistency between the learned embedding space and the intrinsic hierarchy of protein functions. Experiments on benchmark datasets demonstrate that HIPPO achieves state-of-the-art performance, outperforming existing methods and showing robustness in low-data regimes. Notably, the model demonstrates strong zero-shot transferability to other species without retraining, enabling reliable PPI prediction and functional inference even in less characterized or rare organisms where experimental data are limited. Further analysis reveals that hierarchical feature fusion is critical for capturing conserved interaction determinants, such as binding motifs and functional annotations. This work advances cross-species PPI prediction and provides a unified framework for interaction prediction in scenarios with sparse or imbalanced multi-species data.