Evidence map›Paper›PMID 25521204›Full record

ArticleBMC bioinformatics2014

Characterization and identification of protein O-GlcNAcylation sites with substrate specificity.

Hsin-Yi Wu, Cheng-Tsung Lu, Hui-Ju Kao, Yi-Ju Chen, Yu-Ju Chen, Tzong-Yi Lee

Open access · goldAbstract read
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Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.0field-weighted citation impact, top 13% of its field
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 35 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Hsin-Yi Wu
Cheng-Tsung Lu
Hui-Ju Kao
Yi-Ju Chen
Yu-Ju Chen
Tzong-Yi Lee
Institute of Chemistry, Academia Sinica · TWYuan Ze University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProtein O-GlcNAcylation, involving the attachment of single N-acetylglucosamine (GlcNAc) to the hydroxyl group of serine or threonine residues. Elucidation of O-GlcNAcylation sites on proteins is required in order to decipher its crucial roles in regulating cellular processes and aid in drug design. With an increasing number of O-GlcNAcylation sites identified by mass spectrometry (MS)-based proteomics, several methods have been proposed for the computational identification of O-GlcNAcylation sites. However, no development that focuses on the investigation of O-GlcNAcylated substrate motifs has existed. Thus, we were motivated to design a new method for the identification of protein O-GlcNAcylation sites with the consideration of substrate site specificity.

resultsIn this study, 375 experimentally verified O-GlcNAcylation sites were collected from dbOGAP, which is an integrated resource for protein O-GlcNAcylation. Due to the difficulty in characterizing the substrate motifs by conventional sequence logo analysis, a recursively statistical method has been applied to obtain significant conserved motifs. To construct the predictive models learned from the identified substrate motifs, we adopted Support Vector Machines (SVMs). A five-fold cross validation was used to evaluate the predictive model, achieving sensitivity, specificity, and accuracy of 0.76, 0.80, and 0.78, respectively. Additionally, an independent testing set, which was really blind to the training data of predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (0.94) and outperform three other O-GlcNAcylation site prediction tools.

conclusionThis work proposed a computational method to identify informative substrate motifs for O-GlcNAcylation sites. The evaluation of cross validation and independent testing indicated that the identified motifs were effective in the identification of O-GlcNAcylation sites. A case study demonstrated that the proposed method could be a feasible means of conducting preliminary analyses of protein O-GlcNAcylation. We also anticipated that the revealed substrate motif may facilitate the study of extensive crosstalk between O-GlcNAcylation and phosphorylation. This method may help unravel their mechanisms and roles in signaling, transcription, chronic disease, and cancer.

Indexed as

Protein Processing, Post-TranslationalAcetylglucosamineAmino Acid MotifsComputational BiologyGlycosylationHumansMass SpectrometryModels, MolecularPhosphorylationProteinsProteomicsSignal TransductionSubstrate SpecificitySupport Vector MachineAcetylglucosamineProteins

Identifiers

PMID25521204
PMCPMC4290634
OpenAlexW2149376055

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.