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Updated: Jan 8, 2026
Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
DeepMLP: A Proteomics-Driven Deep Learning Framework for Identifying Mis-Localized Proteins across Pan-Cancer.
Bing Wang1,2, Qilei Lin3, Xin He4
1Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Institute of Precision Medicine, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350122, China.
DeepMLP, a novel deep learning framework, identifies mis-localized proteins (MLPs) in cancer using proteomics data. This approach enhances cancer research by accurately predicting protein subcellular localization (PSL) and uncovering potential cancer drivers.
Area of Science:
- Proteomics and Bioinformatics
- Cancer Biology
- Computational Biology
Background:
- Accurate protein subcellular localization (PSL) is crucial for protein function; mis-localization is linked to diseases like cancer.
- Existing spatial proteomics methods are costly and complex, limiting large-scale analysis of cancer-related mis-localized proteins (MLPs).
Purpose of the Study:
- To develop a deep learning framework, DeepMLP, for identifying MLPs in cancer using mass spectrometry-based proteomics data.
- To enhance the accuracy and stability of PSL prediction in both normal and tumor conditions.
Main Methods:
- Developed DeepMLP, a proteomics-driven deep learning framework integrating pathway-aware protein representations with dynamic protein-protein interaction (PPI) networks.
- Utilized cross-attention mechanisms for representation construction and graph attention networks for integrating PPI networks.
- Benchmarked DeepMLP against state-of-the-art methods for PSL prediction.
Main Results:
- DeepMLP demonstrated superior accuracy and stability in PSL prediction compared to existing methods.
- Systematically identified potential MLPs across various cancer types, including mis-localized protein kinases.
- Functional enrichment analyses revealed significant involvement of identified MLPs in cancer-related metabolic and signaling pathways.
Conclusions:
- DeepMLP offers a powerful, cost-effective approach for identifying MLPs in cancer.
- The identified MLPs and kinases hold potential as novel biomarkers or therapeutic targets in tumorigenesis.
- This framework advances the understanding of protein mis-localization's role in cancer development.
