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Poster Presentations
Day 3, June 24（Tue.）
Room P (Maesato East, Foyer, Ocean Wing)
- 3P-AM-10
A machine learning model for site-specific classification of N-glycoprotein fucosylation using tandem mass spectrometry and deep neural network
(1KBSI, 2KRIBB, 3UST)
oMina Park1, Jin Young Kim1,2, Heeyoun Hwang1,3
Protein fucosylation is a key post-translational modification affecting protein structure, stability, and interactions. N-glycopeptide complexity arises from various combinations of HexNAc, Hex, Fuc, and Sia. Fucosylation is classified into core and outer types, both linked to cancer, immune responses, and protein regulation, requiring precise structural analysis. This study presents a method to classify N-glycopeptide fucosylation into none, core, outer, and dual types using deep neural networks (DNN). To classify fucosylation types, we selected training and test sets from over 50,000 N-glycopeptide MS/MS spectra derived from Immunoglobulin G (IgG) and alpha-1-acid glycoprotein (AGP). The N-glycopeptide MS/MS spectra were identified characteristic fragment ions of N-glycopeptides, and peak m/z and intensity values were applied to machine learning models. Various hyperparameters were tested to optimize performance, and the model was validated on human plasma samples to classify fucosylated N-glycopeptides. DNN approaches accurately predicted fucosylation types in complex plasma samples, demonstrating the effectiveness of combining machine learning with MS/MS analysis.