MaXLinker: Proteome-wide Cross-link Identifications with High Specificity and Sensitivity

Area of Science:

• Proteomics
• Structural Biology
• Biochemistry

Background:

• Protein-protein interactions are fundamental to cellular processes and disease.
• Cross-linking mass spectrometry (XL-MS) maps these interactions and their spatial constraints.
• Current MS2-centric XL-MS algorithms yield high rates of misidentified cross-links.

Purpose of the Study:

• To address limitations in current XL-MS search algorithms.
• To develop a more accurate method for identifying cross-links using MS2-MS3 XL-MS data.
• To improve the understanding of protein interaction networks and their role in biological systems.

Main Methods:

• Development of a novel MS3-centric cross-link identification approach.
• Implementation of the approach as a search engine named MaXLinker.
• Evaluation using quality assessment metrics: fraction of mis-identifications (FMI) and fraction of interprotein cross-links from known interactions (FKI).
• Application to human proteome-wide XL-MS using K562 cells.

Main Results:

• MaXLinker demonstrates superior performance over existing search engines with lower misidentification rates, higher sensitivity, and specificity.
• Identification of 9319 unique cross-links (8051 intraprotein, 1268 interprotein) at a 1% false discovery rate in human K562 cells.
• Experimental validation confirmed the high quality and reliability of novel interactions identified by MaXLinker.

Conclusions:

• The MS3-centric approach implemented in MaXLinker significantly enhances the accuracy and reliability of XL-MS data analysis.
• MaXLinker provides a robust tool for comprehensive proteome-wide interaction mapping.
• This advancement facilitates deeper insights into cellular mechanisms and disease pathologies driven by protein interactions.