Can We Boost N-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure...

Area of Science:

• Analytical Chemistry
• Biochemistry
• Proteomics

Background:

• Bottom-up mass spectrometric identification of N-glycopeptides faces challenges in confidence and reproducibility.
• Collision energy in MS/MS and database search engines are critical factors for accurate N-glycopeptide identification.

Purpose of the Study:

• To investigate how N-glycopeptide structural features and search engine choice impact optimal collision energy for high-confidence identification.
• To develop an improved MS/MS workflow for N-glycopeptide analysis.

Main Methods:

• LC-MS/MS measurements with varied collision energies on diverse N-glycopeptides.
• Evaluation of Byonic, pGlyco, and GlycoQuest search engine performance.
• Statistical and machine learning analyses to identify influential parameters.

Main Results:

• Search engine behavior varies (peptide-centric vs. glycan-centric), affecting optimal collision energy dependence on m/z.
• Peptide hydrophobicity, glycan/peptide masses, and mobile proton count significantly influence results, depending on the search engine.
• A proposed workflow using hydrophobicity and glycan mass for smart collision energy selection.

Conclusions:

• The developed workflow can significantly increase N-glycopeptide identification confidence (up to 100%), especially for low-scoring hits.
• Enhanced confidence and reproducibility in N-glycopeptide analysis are achievable.
• This approach has the potential to improve the reliability of glycopeptide research.