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GlycoMinestruct : A new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features

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dc.contributor.authorLi, F.
dc.contributor.authorLi, C.
dc.contributor.authorRevote, J.
dc.contributor.authorZhang, Y.
dc.contributor.authorWebb, G.I.
dc.contributor.authorLi, J.
dc.contributor.authorSong, J.
dc.contributor.authorLithgow, T.
dc.date.issued2016
dc.description.abstractGlycosylation plays an important role in cell-cell adhesion, ligand-binding and subcellular recognition. Current approaches for predicting protein glycosylation are primarily based on sequence-derived features, while little work has been done to systematically assess the importance of structural features to glycosylation prediction. Here, we propose a novel bioinformatics method called GlycoMine<sup>struct</sup>(http://glycomine.erc.monash.edu/Lab/GlycoMine_Struct/) for improved prediction of human N- and O-linked glycosylation sites by combining sequence and structural features in an integrated computational framework with a two-step feature-selection strategy. Experiments indicated that GlycoMine<sup>struct</sup> outperformed NGlycPred, the only predictor that incorporated both sequence and structure features, achieving AUC values of 0.941 and 0.922 for N- and O-linked glycosylation, respectively, on an independent test dataset. We applied GlycoMine<sup>struct</sup> to screen the human structural proteome and obtained high-confidence predictions for N- and O-linked glycosylation sites. GlycoMinestruct can be used as a powerful tool to expedite the discovery of glycosylation events and substrates to facilitate hypothesis-driven experimental studies.
dc.description.statementofresponsibilityFuyi Li, Chen Li, Jerico Revote, Yang Zhang, Geoffrey I.Webb, Jian Li, Jiangning Song, Trevor Lithgow
dc.identifier.citationScientific Reports, 2016; 6(1):1-16
dc.identifier.doi10.1038/srep34595
dc.identifier.issn2045-2322
dc.identifier.issn2045-2322
dc.identifier.orcidLi, F. [0000-0001-5216-3213]
dc.identifier.urihttps://hdl.handle.net/2440/139646
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/1092262
dc.rights© The Author(s) 2016. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
dc.source.urihttps://doi.org/10.1038/srep34595
dc.subjectGlycosylation; protein structure predictions
dc.subject.meshHumans
dc.subject.meshGlycoproteins
dc.subject.meshProteome
dc.subject.meshSequence Analysis, Protein
dc.subject.meshComputational Biology
dc.subject.meshGlycosylation
dc.subject.meshSoftware
dc.titleGlycoMinestruct : A new bioinformatics tool for highly accurate mapping of the human N-linked and O-linked glycoproteomes by incorporating structural features
dc.typeJournal article
pubs.publication-statusPublished

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