pGlycoQuant

pGlycoQuant performs quantitative analysis of intact glycopeptides to enable precise site-specific N-glycosylation quantification for glycoproteomics and biomarker studies.

Key Features:

• Intact glycopeptide identification and quantification: Supports identification and quantitation of intact N-glycopeptides to enable site-specific glycosylation analysis.
• Quantitative analysis (MS1 and MS2): Supports both primary (MS1) and tandem mass spectrometry (MS2) quantitation across multiple experimental strategies.
• Deep residual network integration: Employs a deep residual network (deep learning) to substantially reduce missing values and improve quantitation precision.
• Compatibility with search engines: Integrates quantitative outputs from pGlyco 2.0, pGlyco3, Byonic, and MSFragger-Glyco.

Scientific Applications:

• Site-specific glycosylation analysis: Enables comparative analysis of site-specific N-glycosylation across samples and conditions.
• Biomarker discovery: Facilitates identification of glycosylation-based biomarkers through quantitative intact N-glycopeptide profiling.
• Cancer glycoproteomics: Applied to quantify 6435 intact N-glycopeptides across three hepatocellular carcinoma cell lines and to identify core fucosylation at site 979 of L1CAM as a potential regulator of metastasis.

Methodology:

Uses a deep learning deep residual network to reduce missing values and improve quantitation precision and processes quantitative outputs from pGlyco 2.0, pGlyco3, Byonic, and MSFragger-Glyco for MS1- and MS2-based analyses.