Characterizing protein conformers by cross-linking mass spectrometry and pattern recognition

Area of Science:

• Biochemistry
• Structural Biology
• Proteomics

Background:

• Chemical cross-linking coupled to mass spectrometry (XLMS) is vital for protein structure and interaction studies.
• Current XLMS software struggles with analyzing multiple protein conformers, limiting its use in dynamic systems.
• This limitation hinders the identification of less abundant conformers crucial for understanding protein dynamics.

Purpose of the Study:

• To develop and present QUIN-XL, a novel software solution for analyzing XLMS data with multiple protein conformers.
• To enable the identification of conformational changes within protein systems across different biological conditions.
• To validate QUIN-XL's performance using the well-characterized HSP90 protein and its known conformers.

Main Methods:

• Developed QUIN-XL, a software tool employing unsupervised clustering.
• Grouped cross-link identifications based on quantitative profiles across diverse samples.
• Applied QUIN-XL to analyze conformational differences in HSP90 across three distinct conformers.

Main Results:

• QUIN-XL successfully groups cross-link data by quantitative profiles, revealing conformational changes.
• Analysis of HSP90 demonstrated that QUIN-XL clusters directly correlate with known 3D protein structures.
• The software effectively highlights regions with altered conformations between different biological states.

Conclusions:

• QUIN-XL addresses a critical limitation in XLMS analysis by effectively handling multiple protein conformers.
• The software facilitates the study of dynamic protein systems and conformational heterogeneity.
• QUIN-XL provides a validated approach for identifying and characterizing protein conformational changes.