Quantitative Cross-linking/Mass Spectrometry Using Isotope-labeled Cross-linkers and MaxQuant

Area of Science:

• Proteomics
• Biochemistry
• Analytical Chemistry

Background:

• Quantitative cross-linking/mass spectrometry (QCLMS) faces technical hurdles.
• Existing software integrates quantification with protein identification, hindering targeted analysis of cross-linked peptides.

Purpose of the Study:

• To present a new MaxQuant software release enabling quantification from m/z feature lists.
• To evaluate the performance of automated QCLMS using the enhanced MaxQuant software.

Main Methods:

• Cross-linking of C3 and C3b proteins using BS(3) and isotope-labeled BS(3)-d4.
• Comparison of automated quantification by MaxQuant against a manually curated dataset.
• Utilizing label-swap replica experiments to identify quantification errors.

Main Results:

• Automated MaxQuant achieved 68% recall and 88% accuracy for cross-linked peptides.
• Label-swap replicas exposed hidden quantification errors, proving essential for QCLMS validation.
• Semi-automated re-quantification recovered cross-links missed by the automated process.

Conclusions:

• The integrated MaxQuant workflow with semi-automated assessment maximizes quantified cross-links.
• Full automation in MaxQuant benefits large datasets and less experienced users.
• Label-swap replicas are critical for robust QCLMS data quality control.