Computational prediction of N-linked glycosylation incorporating structural properties and patterns

Gwo Yu Chuang, Jeffrey C. Boyington, M. Gordon Joyce, Jiang Zhu, Gary J. Nabel, Peter D. Kwong, Ivelin Georgiev

Research output: Contribution to journalArticlepeer-review

Abstract

Motivation: N-linked glycosylation occurs predominantly at the N-X-T/S motif, where X is any amino acid except proline. Not all N-X-T/S sequons are glycosylated, and a number of web servers for predicting N-linked glycan occupancy using sequence and/or residue pattern information have been developed. None of the currently available servers, however, utilizes protein structural information for the prediction of N-glycan occupancy.Results: Here, we describe a novel classifier algorithm, NGlycPred, for the prediction of glycan occupancy at the N-X-T/S sequons. The algorithm utilizes both structural as well as residue pattern information and was trained on a set of glycosylated protein structures using the Random Forest algorithm. The best predictor achieved a balanced accuracy of 0.687 under 10-fold cross-validation on a curated dataset of 479 N-X-T/S sequons and outperformed sequence-based predictors when evaluated on the same dataset. The incorporation of structural information, including local contact order, surface accessibility/composition and secondary structure thus improves the prediction accuracy of glycan occupancy at the N-X-T/S consensus sequon.

Original languageEnglish (US)
Article numberbts426
Pages (from-to)2249-2255
Number of pages7
JournalBioinformatics
Volume28
Issue number17
DOIs
StatePublished - Sep 2012
Externally publishedYes

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability
  • Medicine(all)

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