ArticleMolecules (Basel, Switzerland)2021
DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- SGGly: a web server for whole-protein, structure-guided analysis of candidate N-linked glycosylation sites.Nucleic acids research · 2026Article
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Post-translational Modifications in Proteins: Prediction Methods, Biological Functions, and Diseases.MedComm · 2026Review
- Flux sampling and context-specific genome-scale metabolic models for biotechnological applications.Trends in biotechnology · 2026Review
- Challenges and Insights in Absolute Quantification of Recombinant Therapeutic Antibodies by Mass Spectrometry: An Introductory Review.Antibodies (Basel, Switzerland) · 2025Review
- Multimodal deep learning for predicting protein ubiquitination sites.Bioinformatics advances · 2025Article
- Future Sequon Finder - A novel approach for predicting future N-linked glycosylation sequon locations on viral surface proteins.PloS one · 2025Article
- StackGlyEmbed: prediction of N-linked glycosylation sites using protein language models.Bioinformatics advances · 2025Article
- Discovery of Carbonic Anhydrase 9 as a Novel CLEC2 Ligand in a Cellular Interactome Screen.Cells · 2024Article
- Sitetack: a deep learning model that improves PTM prediction by using known PTMs.Bioinformatics (Oxford, England) · 2024Article
- Prediction of human O-linked glycosylation sites using stacked generalization and embeddings from pre-trained protein language model.Bioinformatics (Oxford, England) · 2024Article
- Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network.bioRxiv : the preprint server for biology · 2024Article
- A quest for cytosolic sequons and their functions.Scientific reports · 2024Article
- Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins.PLoS computational biology · 2024Article
- SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model.NAR genomics and bioinformatics · 2024Article
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
- EMNGly: predicting N-linked glycosylation sites using the language models for feature extraction.Bioinformatics (Oxford, England) · 2023Article
- Evolutionary conservation of sequence motifs at sites of protein modification.The Journal of biological chemistry · 2023Article
- Artificial intelligence in the analysis of glycosylation data.Biotechnology advances · 2022Review
- Glycoinformatics in the Artificial Intelligence Era.Chemical reviews · 2022Review
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Authors and funding
4 authors.
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Abstract
Protein N-linked glycosylation is a post-translational modification that plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of glycosylation sites. Most of the existing predictors for N-linked glycosylation utilize the information that the glycosylation site occurs at the N-X-[S/T] sequon, where X is any amino acid except proline. Not all N-X-[S/T] sequons are glycosylated, thus the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In that regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. Here, we report DeepNGlyPred a deep learning-based approach that encodes the positive and negative sequences in the human proteome dataset (extracted from N-GlycositeAtlas) using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. DeepNGlyPred produces SN, SP, MCC, and ACC of 88.62%, 73.92%, 0.60, and 79.41%, respectively on N-GlyDE independent test set, which is better than the compared approaches. These results demonstrate that DeepNGlyPred is a robust computational technique to predict N-Linked glycosylation sites confined to N-X-[S/T] sequon. DeepNGlyPred will be a useful resource for the glycobiology community.
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Registered trials
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