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Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry. | LitMetric

AI Article Synopsis

  • The study explores the role of fucosylation in glycoproteins, highlighting its importance in cell functions such as recognition and growth, while addressing the challenges of classifying different fucosylated N-glycoproteins.* -
  • Researchers used tandem mass spectrometry (MS/MS) and machine learning techniques, specifically deep neural networks (DNN) and support vector machines (SVM), to classify N-glycopeptides into core- and outer-fucosylated types, achieving over 99% accuracy.* -
  • The results showed that outer fucosylation was prevalent in complex N-glycopeptides, while core fucosylation dominated in simpler forms, demonstrating that machine learning can effectively differentiate f

Article Abstract

Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function 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 such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 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 more than 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 total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as 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 in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962204PMC
http://dx.doi.org/10.1038/s41598-019-57274-1DOI Listing

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