CMV-GLA: contrastive multi-view graph layer attention for predicting phosphorylation site-disease associations

Protein phosphorylation is a critical posttranslational modification involved in cell signaling and metabolic regulation, and phosphorylation dysregulation is closely associated with various diseases. However, studies on phosphorylation site-disease associations remain scarce, and existing methods often fail to fully leverage complementary information across heterogeneous data sources, resulting in suboptimal predictive performance. We present CMV-GLA (Contrastive Multi-View Graph Layer Attention Network), a novel framework integrating protein sequences, disease semantics, and known associations into three complementary graph views (site similarity, disease similarity, association network). Built on a GAT backbone, CMV-GLA employs layer attention to mitigate over-smoothing and contrastive learning to maximize cross-view node embedding consistency while minimizing inter-node confusion, enhancing discriminative power. Specifically, the contrastive learning module encourages consistent representations of the same node across different graph views while explicitly separating embeddings of unrelated nodes, leading to more distinguishable and robust feature representations. Evaluated on benchmark data, CMV-GLA significantly outperforms state-of-the-art methods in AUC and AUPRC. Ablation studies confirm the critical roles of multi-view fusion and the contrastive module. Case studies demonstrate high-confidence, literature-supported predictions, highlighting CMV-GLA's utility for elucidating phosphorylation mediated mechanisms and guiding therapeutic discovery. Code and dataset available at https://github.com/ljr078/CMV-GLA.