DeepNGlypred: A deep neural network-based approach for human N-linked glycosylation site prediction

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Authors
Pakhrin, Subash C.
Aoki-Kinoshita, Kiyoko F.
Caragea, Doina
KC, Dukka B.
Advisors
Issue Date
2021-12-02
Type
Article
Keywords
Post-translation modification , Sequon , Deep neural network , N-linked glycosylation
Research Projects
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Citation
Pakhrin, S. C., Aoki-Kinoshita, K. F., Caragea, D., & Kc, D. B. (2021). DeepNgGlypred: A deep neural network-based approach for human N-linked glycosylation site prediction. Molecules, 26(23) doi:10.3390/molecules26237314
Abstract

Protein N-linked glycosylation is a post-translational modification that plays an importantrole in a myriad of biological processes. Computational prediction approaches serve as comple-mentary methods for the characterization of glycosylation sites. Most of the existing predictors forN-linked glycosylation utilize the information that the glycosylation site occurs at the N-X-[S/T] se-quon, where X is any amino acid except proline. Not all N-X-[S/T] sequons are glycosylated, thus theN-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In thatregard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is animportant problem. Here, we report DeepNGlyPred a deep learning-based approach that encodes thepositive and negative sequences in the human proteome dataset (extracted from N-GlycositeAtlas)using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionaryinformation. DeepNGlyPred produces SN, SP, MCC, and ACC of 88.62%, 73.92%, 60%, and 79.41%,respectively on N-GlyDE independent test set, which is better than the compared approaches. Theseresults demonstrate that DeepNGlyPred is a robust computational technique to predict N-Linkedglycosylation sites confined to N-X-[S/T] sequon. DeepNGlyPred will be a useful resource for theglycobiology community

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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Publisher
MDPI
Journal
Book Title
Series
Molecules;Vol. 26, Iss. 23
PubMed ID
DOI
ISSN
1420-3049
EISSN