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

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.