Application of a fast sorting algorithm to the assignment of mass spectrometric cross-linking data

Area of Science:

• Proteomics
• Biochemistry
• Computational Biology

Background:

• Cross-linking combined with mass spectrometry (MS) is crucial for identifying protein interactions.
• Current methods for assigning cross-linked peptides are computationally intensive and become impractical with complex protein systems or relaxed enzyme specificity.

Purpose of the Study:

• To present a novel, fast sorting algorithm for efficient cross-linked peptide assignment in large sequence databases.
• To adapt and apply an existing algorithm, previously used for disulfide-bridged peptides, to protein cross-linking studies.

Main Methods:

• Development and application of a fast sorting algorithm for mass-based searching of cross-linked peptides.
• Utilizing mass spectrometry (MS) and enzymatic digestion for protein analysis.

Main Results:

• The algorithm enables rapid searching of large sequence databases for cross-linked peptide assignments.
• Demonstrates the applicability of the algorithm to protein cross-linking, overcoming previous computational bottlenecks.

Conclusions:

• The developed algorithm significantly enhances the efficiency of analyzing protein cross-linking data.
• This computational approach provides a scalable solution for complex proteomic studies involving cross-linking.