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Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins

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Computational Methods for Predicting Post-Translational Modification Sites

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2499))

Abstract

Protein glycosylation is one of the most complex posttranslational modifications (PTM) that play a fundamental role in protein function. Identification and annotation of these sites using experimental approaches are challenging and time consuming. Hence, there is a demand to build fast and efficient computational methods to address this problem. Here, we present the SPRINT-Gly framework containing the largest dataset and a prediction model of glycosylation sites for a given protein sequence. In this framework, we construct a large dataset containing N- and O-linked glycosylation sites of human and mouse proteins, collected from different sources. We then introduce the SPRINT-Gly method to predict putative N- and O-linked sites. SPRINT-Gly is a machine learning-based approach consisting of a number of trained predictive models for glycosylation sites in both human and mouse proteins, separately. The method is built by incorporating sequence-based, predicted structural, and physicochemical information of the neighboring residues of each N- and O-linked glycosylation site and by training deep learning neural network and support vector machine as classifiers. SPRINT-Gly outperformed other existing methods by achieving 18% and 50% higher Matthew’s correlation coefficient for N- and O-linked glycosylation site prediction, respectively. SPRINT-Gly is publicly available as an online and stand-alone predictor at https://sparks-lab.org/server/sprint-gly/.

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Acknowledgments

We acknowledge the use of the High-Performance Computing Cluster “Gowonda,” resources provided by the Queensland Cyber Infrastructure Foundation, and Australian Research Data Commons. We also acknowledge the authors of previous methods for providing the web servers. This work was supported in part by Institute for Glycomics to M.P.C., and by the Australian Government Research Training Program Scholarship for G.T.

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Author notes

  1. Ghazaleh Taherzadeh

    Present address: Department of Mathematics and Computer Science, Wilkes University, Wilkes-Barre, PA, USA

Authors and Affiliations

  1. Institute for Glycomics, Griffith University, Southport, QLD, Australia

    Matthew Campbell

  2. Institute for Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China

    Yaoqi Zhou

Authors
  1. Ghazaleh Taherzadeh
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  2. Matthew Campbell
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  3. Yaoqi Zhou
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Corresponding author

Correspondence to Ghazaleh Taherzadeh .

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Editors and Affiliations

  1. Michigan Technological University, Houghton, MI, USA

    Dukka B. KC

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Taherzadeh, G., Campbell, M., Zhou, Y. (2022). Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins. In: KC, D.B. (eds) Computational Methods for Predicting Post-Translational Modification Sites. Methods in Molecular Biology, vol 2499. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2317-6_9

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  • DOI: https://doi.org/10.1007/978-1-0716-2317-6_9

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2316-9

  • Online ISBN: 978-1-0716-2317-6

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