Mapping Context-Aware Phosphosite Regulation of Protein–Protein Interactions Using Deep Learning and Pan-Cancer Proteomics

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Abstract
Phosphorylation dynamically orchestrates the protein-protein interaction (PPI) network that governs cellular signaling, and its dysregulation frequently drives malignant transformation and neurodegeneration. We present PhosPPI-SEQ, an interpretable deep learning framework to identify phosphosites that functionally regulate PPIs. The model integrates a pretrained Transformer architecture with a cross-attention mechanism for fine-tuning, achieving both high predictive accuracy and biologically meaningful attention patterns. Combining PhosPPI-SEQ with a regression model based on pan-cancer multiomics data, we map context-specific phosphosites involved in PPI regulation across six cancer types, which are validated experimentally in the context of epigenetic regulation. Notably, hyperphosphorylation of HDAC1 at S421 or S423 strengthens its binding to KDM1A and correlates with poor clinical outcomes in small-cell lung cancer (SCLC) patients. A dual inhibitor targeting class I HDAC and KDM1A exhibits superior antiproliferative effects on SCLC cells expressing HDAC1 S421 or S423 phosphomimetic mutations. Our study establishes a computational-experimental paradigm for decoding phosphosites that regulate PPIs, significantly expanding the targetable PPI landscape in a phosphosite biomarker-oriented manner.
Funding Information
  • Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences (GIBHBRP24-04)
  • National Natural Science Foundation of China (22377089)
  • Guangzhou Laboratory (GZNL2023A02003)

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