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PhoXplex: Combining Phospho-enrichable Cross-Linking with Isobaric Labeling for Quantitative Proteome-Wide Mapping of
Runa D Hoenger Ramazanova1, Theodoros I Roumeliotis1, James C Wright1
1Functional Proteomics team, Chester Beatty Laboratories, The Institute of Cancer Research, London SW3 6JB, United Kingdom.
Journal of Proteome Research
|October 18, 2024
Summary
This study introduces PhoXplex, a new workflow combining cross-linking mass spectrometry (XL-MS) and TMT labeling for large-scale protein structural analysis. It enables detailed mapping of protein interactions and structural changes across cell lines.
Area of Science:
- Structural Biology
- Proteomics
- Biochemistry
Background:
- Cross-linking mass spectrometry (XL-MS) provides insights into protein structure and interactions.
- Applying XL-MS to whole-cell samples faces challenges in depth and throughput.
- Enrichable cross-linkers and quantitative methods enhance protein interface detection.
Purpose of the Study:
- To develop a streamlined workflow for global detection of differential structural features in cells.
- To combine phospho-enrichable cross-linking with TMT labeling for enhanced analysis.
- To enable comparative characterization of protein structural attributes and interactions.
Main Methods:
- Developed the PhoXplex workflow integrating phospho-enrichable cross-linking and TMT labeling.
- Applied the workflow to a panel of cell lines for global proteome-wide analysis.
- Utilized AlphaFold predictions and disorder protein annotations for data interpretation.
Main Results:
- Achieved deep coverage, quantifying over 9000 cross-links and long loop-links.
- Identified potentially novel protein-protein interactions and structural features.
- Enabled exploration of quantitative cross-linking data to associate mutations with protein structures.
Conclusions:
- PhoXplex offers a powerful approach for large-scale, comparative profiling of protein interfaces.
- The workflow facilitates the study of differential structural features across diverse cell populations.
- Future perspectives include addressing limitations for even deeper whole-cell profiling.
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