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.