High Sensitivity Crosslink Detection Coupled With Integrative Structure Modeling in the Mass Spec Studio.

Area of Science:

• Biochemistry
• Structural Biology
• Computational Biology

Background:

• Mass spectrometry (MS)-based workflows are crucial for analyzing protein structure and interactions.
• Existing tools facilitate hydrogen-deuterium exchange and covalent labeling data extraction.
• Integration with protein modeling requires robust methods for identifying structural constraints.

Purpose of the Study:

• To extend the Mass Spec Studio framework with a plug-in for crosslink (XL) detection.
• To develop an efficient data processing strategy for flexible XL methods.
• To integrate XL data with existing plug-ins for enhanced protein modeling.

Main Methods:

• A peptide library reduction strategy using presearch of tandem-MS data.
• A probabilistic approach for prescoring linear unmodified peptide tags.
• Integration with the Haddock plug-in for combining multiple data sources.

Main Results:

• Substantial reduction in search space for XL identification.
• Highly sensitive XL peptide identification with a low false positive rate.
• Successful generation of a structural model for porcine transferrin bound to TbpB using XL restraints.

Conclusions:

• The new Mass Spec Studio plug-in enables sensitive and accurate crosslink detection.
• Integration with Haddock facilitates comprehensive protein modeling.
• The developed method confirms the mechanism of TbpB-transferrin interaction and highlights the need for diverse restraint data.