Accurate classification of protein–protein interaction (PPI) categories is critical for elucidating intracellular signaling pathways and disease mechanisms. Reliable computational prediction methods can substantially reduce the cost of high-throughput wet-lab screening. However, existing multimodal PPI predictors generalize poorly, primarily for two reasons. First, most multi-category PPI predictors integrate only one or two of the three key feature modalities: evolutionary sequence signatures, three-dimensional (3D) structural profiles, and Gene Ontology (GO) functional annotations. This incomplete modal coverage prevents them from building comprehensive protein representations. Second, even models that cover all three modalities typically rely on shallow fusion strategies, which fail to capture the deep complementary relationships among orthogonal biological signals. This limitation leads to performance degradation on low-homology proteins and rare PPI categories. To address these limitations, we propose Tri-modal Chained Cross-Attention Protein–Protein Interaction (TriCCA-PPI), a framework with two key contributions. First, a fully decoupled feature extraction pipeline independently generates ESM-2 sequence, ESM-IF1 structural, and GO-anc2vec functional embeddings. This modular architecture supports independent replacement and upgrading of each modality’s feature extractor, and compensates for the incomplete biological characterization inherent in single- or dual-modal inputs. Second, a chained pairwise cross-attention module performs three rounds of progressive modal alignment to capture layered cross-modal complementary relationships. We further employ a global–local dual-channel Graph Isomorphism Network (GIN) with Jumping Knowledge aggregation to enhance graph topological representation. Asymmetric loss (ASL) is applied to mitigate class imbalance. Evaluations on the SHS27K and SHS148K datasets across Random, BFS, and DFS splits show that TriCCA-PPI outperforms state-of-the-art methods, with substantial gains observed on low-homology proteins and rare PPI categories. Ablation experiments validate the contribution of each modality and the advantage of chained cross-attention over shallow fusion. TriCCA-PPI addresses two key limitations of existing multimodal PPI predictors. Its decoupled, replaceable feature extraction pipeline integrates multi-dimensional biological cues into comprehensive protein representations, while chained cross-attention enables deep progressive fusion that leverages cross-modal complementary relationships. The method improves generalization for low-homology proteins and rare PPI categories, and its modular design offers a practical, interpretable, and extensible multimodal fusion framework for large-scale multi-category PPI prediction.
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