SPRINT-Gly: predicting N- and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties.

Journal: Bioinformatics (Oxford, England)
PMID: 30903686

Abstract

MOTIVATION: Protein glycosylation is one of the most abundant post-translational modifications that plays an important role in immune responses, intercellular signaling, inflammation and host-pathogen interactions. However, due to the poor ionization efficiency and microheterogeneity of glycopeptides identifying glycosylation sites is a challenging task, and there is a demand for computational methods. Here, we constructed the largest dataset of human and mouse glycosylation sites to train deep learning neural networks and support vector machine classifiers to predict N-/O-linked glycosylation sites, respectively.

Authors

  • Ghazaleh Taherzadeh
    School of Information and Communication Technology, Griffith University, Parklands Drive, Southport, Queensland, 4215, Australia.
  • Abdollah Dehzangi
    1] Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia [2] Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.
  • Maryam Golchin
    School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia.
  • Yaoqi Zhou
    Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong, 518106, China. Electronic address: zhouyq@szbl.ac.cn.
  • Matthew P Campbell
    Institute for Glycomics, Griffith University, Parklands Drive, Gold Coast, QLD, Australia.

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