ArticleScientific reports2019
N-GlyDE: a two-stage N-linked glycosylation site prediction incorporating gapped dipeptides and pattern-based encoding.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- ViralMap: predicting features in viral proteins from primary sequence.Journal of virology · 2026Article
- SGGly: a web server for whole-protein, structure-guided analysis of candidate N-linked glycosylation sites.Nucleic acids research · 2026Article
- Integration of RNAseq transcriptomics andChemical science · 2025Article
- StackGlyEmbed: prediction of N-linked glycosylation sites using protein language models.Bioinformatics advances · 2025Article
- Advances in Prediction of Posttranslational Modification Sites Known to Localize in Protein Supersecondary Structures.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Glycan masking in immunogen design: computational and experimental methods.Frontiers in immunology · 2025Review
- Future Sequon Finder - A novel approach for predicting future N-linked glycosylation sequon locations on viral surface proteins.PloS one · 2025Article
- Sitetack: a deep learning model that improves PTM prediction by using known PTMs.Bioinformatics (Oxford, England) · 2024Article
- Boltzmann Model Predicts Glycan Structures from Lectin Binding.Analytical chemistry · 2024Article
- Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network.bioRxiv : the preprint server for biology · 2024Article
- Protein feature engineering framework for AMPylation site prediction.Scientific reports · 2024Article
- A quest for cytosolic sequons and their functions.Scientific reports · 2024Article
- A Boltzmann model predicts glycan structures from lectin binding.bioRxiv : the preprint server for biology · 2024Article
- Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins.PLoS computational biology · 2024Article
- Mechanisms of thyrotropin receptor-mediated phenotype variability deciphered by gene mutations and M453T-knockin model.JCI insight · 2024Article
- Importance of N-Glycosylation for the Expression and Function of Human Organic Anion Transporting Polypeptide 2B1.ACS pharmacology & translational science · 2023Article
- Evolutionary conservation of sequence motifs at sites of protein modification.The Journal of biological chemistry · 2023Article
- Engineering receptors in the secretory pathway for orthogonal signalling control.Nature communications · 2022Article
- Insilico prediction and functional analysis of nonsynonymous SNPs in human CTLA4 gene.Scientific reports · 2022Article
- Thirty years of molecular dynamics simulations on posttranslational modifications of proteins.Physical chemistry chemical physics : PCCP · 2022Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
N-linked glycosylation is one of the predominant post-translational modifications involved in a number of biological functions. Since experimental characterization of glycosites is challenging, glycosite prediction is crucial. Several predictors have been made available and report high performance. Most of them evaluate their performance at every asparagine in protein sequences, not confined to asparagine in the N-X-S/T sequon. In this paper, we present N-GlyDE, a two-stage prediction tool trained on rigorously-constructed non-redundant datasets to predict N-linked glycosites in the human proteome. The first stage uses a protein similarity voting algorithm trained on both glycoproteins and non-glycoproteins to predict a score for a protein to improve glycosite prediction. The second stage uses a support vector machine to predict N-linked glycosites by utilizing features of gapped dipeptides, pattern-based predicted surface accessibility, and predicted secondary structure. N-GlyDE's final predictions are derived from a weight adjustment of the second-stage prediction results based on the first-stage prediction score. Evaluated on N-X-S/T sequons of an independent dataset comprised of 53 glycoproteins and 33 non-glycoproteins, N-GlyDE achieves an accuracy and MCC of 0.740 and 0.499, respectively, outperforming the compared tools. The N-GlyDE web server is available at http://bioapp.iis.sinica.edu.tw/N-GlyDE/ .
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