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Updated: Aug 5, 2026
Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
AI proteomics: from protein identification to virtual cells
Yingying Sun1,2, Jun A1,2, Zhiwei Liu1,2
1Affiliated Hangzhou First People's Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, School of Future Biomedicine, Westlake University, Hangzhou, China.
Artificial intelligence (AI) is revolutionizing proteomics research using mass spectrometry (MS). AI enhances protein identification, quantification, and complex analysis, paving the way for AI virtual cells and collaborative data ecosystems.
Area of Science:
- Proteomics
- Bioinformatics
- Computational Biology
Background:
- Artificial intelligence (AI) is increasingly impacting scientific research methodologies.
- Mass spectrometry (MS)-based proteomics generates vast datasets requiring advanced analytical tools.
- AI offers novel approaches to address complex challenges in large-scale biological data analysis.
Purpose of the Study:
- To highlight key areas in MS-based proteomics where AI is driving innovation.
- To discuss the potential of AI in advancing proteomics techniques and applications.
- To advocate for a collaborative ecosystem to foster AI development in proteomics.
Main Methods:
- Review of current AI applications in MS-based proteomics.
- Identification of AI-driven advancements in protein analysis.
- Discussion of future directions including AI virtual cells and multi-omics integration.
Main Results:
- AI significantly improves peptide and protein identification and quantification.
- AI facilitates the characterization of protein-protein interactions and complexes.
- AI advancements are enabling spatial and perturbation proteomics and multi-omics data integration.
Conclusions:
- AI is a transformative force in MS-based proteomics, enhancing data analysis and enabling new research frontiers.
- The development of AI virtual cells represents a significant future application.
- Global collaboration is essential to build an AI-friendly ecosystem for proteomics advancement.
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