ArticleJournal of theoretical & computational chemistry2020
Predicting mucin-type O-Glycosylation using enhancement value products from derived protein features.
Article in Journal of theoretical & computational chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Charge matters: how flanking substrate charge modulates O-glycan Core elongation.Glycobiology · 2025Article
- Polypeptide N-acetylgalactosaminyltransferase (GalNAc-T) isozyme surface charge governs charge substrate preferences to modulate mucin type O-glycosylation.Glycobiology · 2023Article
- The effect of a methyl group on structure and function: Serine vs. threonine glycosylation and phosphorylation.Frontiers in molecular biosciences · 2023Review
- The mucin protein MUCL1 regulates melanogenesis and melanoma genes in a manner dependent on threonine content.The British journal of dermatology · 2022Article
- ISOGlyP: de novo prediction of isoform-specific mucin-type O-glycosylation.Glycobiology · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Mucin-type O-glycosylation is one of the most common post-translational modifications of proteins. This glycosylation is initiated in the Golgi by the addition of the sugar N-acetylgalactosamine (GalNAc) onto protein Ser and Thr residues by a family of polypeptide GalNAc transferases. In humans there are 20 isoforms that are differentially expressed across tissues that serve multiple important biological roles. Using random peptide substrates, isoform specific amino acid preferences have been obtained in the form of enhancement values (EV). These EVs alone have previously been used to predict O-glycosylation sites via the web based ISOGlyP (Isoform Specific O-Glycosylation Prediction) tool. Here we explore additional protein features to determine whether these can complement the random peptide derived enhancement values and increase the predictive power of ISOGlyP. The inclusion of additional protein substrate features (such as secondary structure and surface accessibility) was found to increase sensitivity with minimal loss of specificity, when tested with three different published
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Registered trials
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