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. 2020 Jan 15;10:318. doi: 10.1038/s41598-019-57274-1

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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The computational workflow for classifying the fucosylation of N-glycopeptides using machine learning. The relative intensities of 14 fucosylation features extracted from CID tandem MS spectra of identified N-glycopeptides were calculated and used to classify fucosylation using the DNN and SVM. Training and testing data sets were constructed with N-glycopeptides identified from standard IgG and AGP glycoproteins using IQ-GPA. The DNN and SVM models were constructed with TensorFlow (ver. 0.12.0) and the R package e1071 (ver. 3.4.3), respectively. The best-performing model was selected from each machine learning method, and classified N-glycopeptides were filtered with <1% FDR using a random decoy. Finally, the DNN and SVM were used to classify an unknown data set from human plasma according to four types of fucosylation: none, core, outer, and dual. Green circles = nomannose; yellow circles = angalactose; blue squares = N-acetylglucosamine; red triangles = fucose; and pink diamonds = N-acetylneuraminic acid.