QUIN-XL

QUIN-XL employs unsupervised clustering to analyze chemical cross-linking coupled with mass spectrometry (XLMS) data to identify protein conformers and map conformational changes across multiple samples.

Key Features:

• XLMS input: Operates on chemical cross-linking coupled with mass spectrometry (XLMS) cross-link identifications and their quantitative profiles.
• Unsupervised clustering: Groups cross-link identifications based on quantitative profiles across multiple samples to reveal co-varying cross-links.
• Conformer resolution: Disentangles mutually exclusive high-quality cross-link identifications to detect multiple, including less abundant, protein conformers.
• Conformational mapping: Highlights regions within proteins and systems that exhibit conformational changes under different biological conditions.
• Structural correlation: Compares cluster patterns to known 3D structures to validate conformer assignments, as demonstrated on HSP90 identifying three distinct conformers.
• Protein interaction analysis: Supports analysis of protein conformations and large-scale protein-protein interactions from XLMS datasets.

Scientific Applications:

• Conformational ensemble mapping: Determining distinct protein conformers present in heterogeneous XLMS samples.
• Condition-specific conformational analysis: Detecting conformational changes across physiological or experimental conditions using quantitative cross-link profiles.
• Protein-protein interaction studies: Interpreting cross-link patterns to inform on large-scale protein-protein interactions.
• Structural validation: Correlating cross-link clusters with known 3D structures to validate conformational assignments.

Methodology:

Processes XLMS cross-link identifications and their quantitative profiles, applies unsupervised clustering to group co-varying cross-links across multiple samples, and compares resulting clusters to known 3D structures (demonstrated on HSP90).