High-Sensitivity Proteome-Scale Searches for Crosslinked Peptides Using CRIMP 2.0.

Area of Science:

• Proteomics
• Biochemistry
• Computational Biology

Background:

• Crosslinking mass spectrometry (XL-MS) provides crucial distance constraints for protein structures.
• Existing XL-MS software often uses database filtering, potentially reducing sensitivity.
• Efficient and sensitive detection of crosslinked peptides is vital for cell-based XL-MS studies.

Purpose of the Study:

• To develop a novel scoring method for sensitive and efficient detection of crosslinked peptides in XL-MS.
• To improve the identification of protein-protein interactions using XL-MS data.
• To provide a robust software solution for complex proteome-level XL-MS analyses.

Main Methods:

• Implemented a rapid presearch strategy combined with computer vision-inspired algorithms for crosslink resolution.
• Developed a new scoring method incorporating compositional terms to enhance detection sensitivity.
• Integrated the functionality into the CRIMP 2.0 software within the Mass Spec Studio.

Main Results:

• Achieved high crosslink detection rates across multiple curated XL-MS datasets.
• Demonstrated efficient proteome-level XL-MS searches, even with cleavable and noncleavable crosslinkers, on standard hardware.
• Showcased a twofold increase in protein-protein interaction detection by including compositional terms in the scoring.

Conclusions:

• The new scoring method and CRIMP 2.0 software significantly enhance sensitivity and efficiency in XL-MS.
• This approach facilitates more comprehensive protein interaction network analysis.
• CRIMP 2.0 offers a powerful and accessible tool for researchers conducting cell-based XL-MS experiments.