Multiattribute Glycan Identification and FDR Control for Glycoproteomics.

Area of Science:

• Biochemistry
• Computational Biology
• Mass Spectrometry

Background:

• Glycoproteomics enables large-scale analysis of complex glycopeptide samples.
• Accurate annotation of mass spectrometry data is a key challenge in glycoproteomics.
• Existing methods struggle to determine specific glycan composition from mass alone.

Purpose of the Study:

• To develop a novel computational method for precise glycan composition determination.
• To enhance the accuracy and sensitivity of N-glycopeptide identification in mass spectrometry.
• To integrate this method into a user-friendly pipeline with false discovery rate control.

Main Methods:

• Utilized MSFragger for glycopeptide identification, reporting peptide sequence and glycan mass.
• Developed a new algorithm integrating glycan oxonium ions (B-type, Y-type), mass accuracy, and precursor selection errors.
• Incorporated false discovery rate (FDR) estimation for glycan assignments.

Main Results:

• The new method accurately determines glycan composition from complex glycopeptide spectra.
• It effectively discriminates between glycans with similar or identical masses.
• Demonstrated sensitive and specific glycan identification with robust FDR control for both peptide and glycan components.

Conclusions:

• The developed method significantly advances N-glycopeptide data annotation in glycoproteomics.
• Integration into PTM-Shepherd and FragPipe provides a complete, robust computational pipeline.
• This approach enhances confidence in glycan identification for complex biological samples.