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

2025-07-03

AI Summary

To address the challenge of cross-species protein–protein interaction (PPI) prediction for experimentally under-characterized species, this paper proposes HIPPO, a hierarchical contrastive learning framework. HIPPO jointly models protein sequences and functional annotations to align biological features across multiple levels—sequence, domain, and pathway—enabling fine-grained structural and functional correspondence. It introduces a novel hierarchical contrastive loss coupled with a data-driven domain penalty, explicitly enforcing embedding space geometry to conform to the hierarchical organization of protein functions. This design enables zero-shot transfer to unseen species without requiring species-specific labeled PPI data. Evaluated on multiple benchmark datasets, HIPPO consistently outperforms state-of-the-art methods, demonstrating superior robustness, minimal dependence on labeled training data, and strong generalization capability—particularly for PPI prediction and functional inference in rare or poorly studied species.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Predict protein-protein interactions across diverse organisms
• Align protein sequences with hierarchical biological attributes
• Enable zero-shot transferability to understudied species

Innovation

• Hierarchical contrastive learning for PPI prediction
• Data-driven penalty mechanism for embedding consistency
• Zero-shot transferability to other species