Dual Cross-Attention Network for Hierarchical Feature Fusion in Protein-Protein Interaction Prediction

Abstract

Characterizing protein-protein interactions (PPIs) is essential for deciphering core biological processes, including signal transduction, metabolic pathway regulation, immune recognition, and cell cycle control. However, experimental PPI determination remains time-consuming and expensive, driving the adoption of deep learning as an efficient and accurate computational approach. Current deep-learning-based PPI prediction models typically process both intra- and inter-protein as isolated units in feature extraction, thereby ignoring mutual information transfer within a single protein and the interacting pair. To address this limitation, we propose DCAPPI (Dual Cross-Attention network for Protein-Protein Interaction prediction), a novel framework leveraging dual cross-attention modules for hierarchical feature fusion at both intra- and inter-protein levels. First, the Channel Cross-Attention module processes protein sequence and structure as distinct input channels. It generates deep intra-protein representations by performing cross-attention between sequence-derived and structure-derived tokens, achieving multimodal feature integration. Second, the Partner Cross-Attention module models the target protein and its interacting partner as a pair of correlative units. By performing cross-attention operations across these units, it enables collaborative feature fusion and constructs context-aware inter-protein interaction features. Evaluation results indicate that DCAPPI achieves superior performance over state-of-the-art methods on benchmark datasets.