Allied Journal of Medical Research

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Allied Journal of Medical Research 44 7897 074717

News Of Glycomics

Protein glycosylation is understood to be involved in biological progresses like cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays a crucial role for structural stability and performance of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins like core or outer isoforms remains a challenge. Here, we report for the primary time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms like the deep neural network (DNN) and support vector machine (SVM). Training and test sets of quite 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The best-performing model had an accuracy of quite 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. a complete of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified because the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri- and tetra-antennary N-glycopeptides, while core fucosylation was dominant within the mono-, bi-antennary and hybrid sorts of N-glycoproteins in human plasma. Thus, the machine learning methods are often combined with MS/MS to differentiate between different isoforms of fucosylated N-glycopeptides.

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