DeepMLP: A Proteomics-Driven Deep Learning Framework for Identifying Mis-Localized Proteins across Pan-Cancer

Accurate protein subcellular localization (PSL) is essential
for
proteins to perform their biological functions, whereas protein mis-localization
can disrupt cellular processes and contribute to various diseases,
particularly cancer. Although spatial proteomics technologies have
enabled systematic studies of PSL, their high cost and technical complexity
limit large-scale analysis of mis-localized proteins (MLPs) in cancer.
To address this challenge, we propose DeepMLP, a proteomics-driven
deep learning framework that identifies MLPs across cancers using
large-scale mass spectrometry-based proteomics data. It constructs
pathway-aware protein representations via a cross-attention mechanism
and, by integrating these representations with dynamic protein–protein
interaction (PPI) networks through graph attention networks, synergistically
enhances the ability to uncover potential cancer MLPs. Benchmarking
demonstrated that DeepMLP consistently outperformed state-of-the-art
methods in both accuracy and stability for PSL prediction in normal
and tumor conditions. Furthermore, we systematically identified potential
MLPs across pan-cancer types and further uncovered a subset of potential
mis-localized protein kinases. Functional enrichment analyses of these
MLPs revealed their significant involvement in cancer-related metabolic
and signaling pathways, highlighting their potential biological relevance
in tumorigenesis.