Dataset Information

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

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.

SUBMITTER: Hwang H

PROVIDER: S-EPMC6962204 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

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

Hwang Heeyoun H Jeong Hoi Keun HK Lee Hyun Kyoung HK Park Gun Wook GW Lee Ju Yeon JY Lee Soo Youn SY Kang Young-Mook YM An Hyun Joo HJ Kang Jeong Gu JG Ko Jeong-Heon JH Kim Jin Young JY Yoo Jong Shin JS

Scientific reports 20200115 1

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 ...[more]

PMID: 31941975

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Dataset Information

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

Publications

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

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