Proteome-wide structural and interaction analysis using cross-linking mass spectrometry and its applications

Area of Science:

• Structural Biology
• Proteomics
• Systems Biology

Background:

• Understanding protein-protein interactions (PPIs) and structural changes in native cellular environments is vital for drug discovery.
• Conventional methods struggle to detect weak, transient, and higher-order interactions, especially under altered physiological conditions.

Purpose of the Study:

• To review the applications of in-vivo chemical cross-linking coupled with mass spectrometry (XL-MS) in complex biological samples.
• To highlight the transformative impact of artificial intelligence (AI) integration on XL-MS workflows and structural biology.

Main Methods:

• In-vivo chemical cross-linking coupled with mass spectrometry (XL-MS) to capture protein interactions.
• Quantitative approaches for comparing different physiological conditions.
• Integration of machine learning (ML) and AI tools for peptide identification and topology mapping.
• Synergistic coupling of XL-MS data with AI-assisted structural modeling platforms (e.g., AlphaFold).

Main Results:

• In-vivo XL-MS enables targeted mapping of PPIs and large-scale interactome network identification.
• AI algorithms improve the accuracy of cross-linked peptide identification and interaction topology mapping.
• AI-assisted structural modeling facilitates dynamic and high-throughput prediction of protein networks.

Conclusions:

• AI integration is revolutionizing in-vivo XL-MS, enhancing depth and efficiency.
• The synergy of XL-MS and AI is advancing structural biology towards a systems-level understanding of proteome architecture.
• This approach expands the application of XL-MS from cells to whole tissues.