Beyond the Sequon: Sites of N-Glycosylation

Schulz, Benjamin L.

doi:10.5772/50260

Cited by 6 publications

(2 citation statements)

References 85 publications

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“…N-linked glycosylation involves the attachment of N-glycans (oligosaccharide) to the amine group (NH2) of asparagine (Asn(N)) and often occurs at the conserved motifs N-X-S or N-X-T sequons where X can be any amino acid except proline [ 6 , 16 ]. However, the presence of such sequon in the peptide does not sufficiently confirm that it is N-linked glycosylated because about one-third to half sequons are buried deep inside the proteins that are not accessible to glycosylation enzymes [ 17 , 18 , 19 , 20 ]. In addition, various factors like sequences surrounding a potential glycosylation site, distance to the next glycosylation, etc.…”

Section: Introductionmentioning

confidence: 99%

DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

et al. 2021

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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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Section: Introductionmentioning

confidence: 99%

DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

et al. 2021

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“…This common core is assembled on the endoplasmic reticulum (ER) membrane, catalyzed by a series of glycosyltransferases encoded by asparagine-linked glycosylation (ALG) genes [13]. Oligosaccharyltransferase (OTase) catalyzes the attachment of the precursor oligosaccharide to selected Asn residues in glycosylation sequons (N-X-S/T; X¹P) in nascent polypeptides [14]. Mutations in ALG genes can cause altered glycan structures due to the accumulation and transfer of truncated glycans, and altered site-specific glycan occupancy due to inefficient transfer of these truncated glycans by OTase [15,16].…”

Section: Introductionmentioning

confidence: 99%

Glycopeptide variable window SWATH for improved data independent acquisition glycoprotein analysis

Zhou

Schulz

2020

Analytical Biochemistry

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Novel “extended sequons” of human N-glycosylation sites improve the precision of qualitative predictions: an alignment-free study of pattern recognition using ProtDCal protein features

et al. 2016

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N-Glycosylation is a common post-translational modification that plays an important role in the proper folding and function of many proteins. This modification is largely dependent on the presence of a sequence motif called a "sequon" defined as Asn-Xxx-Ser/Thr. However, evidence has shown that the presence of such a "sequon" is insufficient to determine the occurrence of N-glycosylation with high precision. This study aims to elucidate patterns that can more accurately predict N-glycosylation sites in human proteins. The novel motifs are evaluated using benchmarking data from 188 organisms. Performance is largely sustained compared to the human data, which validates the robustness of the novel extracted "extended sequons". We, therefore, introduce new knowledge about sequence-related factors that control N-glycosylation.

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Beyond the Sequon: Sites of N-Glycosylation

Cited by 6 publications

References 85 publications

DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

Glycopeptide variable window SWATH for improved data independent acquisition glycoprotein analysis

Novel “extended sequons” of human N-glycosylation sites improve the precision of qualitative predictions: an alignment-free study of pattern recognition using ProtDCal protein features

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