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

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Title:	Characterization and identification of protein O-GlcNAcylation sites with substrate specificity
Authors:	吳忻怡;Wu, Hsin-Yi;Lu, Cheng-Tsung;Kao, Hui-Ju;Chen, Yi-Ju;Chen, Yu-Ju;Lee, Tzong-Yi
Contributors:	總教學中心通識教育中心
Keywords:	Accuracy;Acetylglucosamine - chemistry;Acetylglucosamine - metabolism;Algorithms;Amino Acid Motifs;Bioinformatics;Biomedical and Life Sciences;Cellular;Computation;Computational Biology - methods;Computational Biology/Bioinformatics;Computer Appl. in Life Sciences;Glycosylation;Humans;Hydroxyl groups;Life Sciences;Mass Spectrometry;Mathematical models;Microarrays;Models, Molecular;Peer review;Phosphorylation;Prediction models;Protein Processing, Post-Translational;Proteins;Proteins - chemistry;Proteins - metabolism;Proteomics;Signal Transduction;Statistical analysis;Statistical methods;Studies;Substrate Specificity;Substrates;Support Vector Machine
Date:	2014-12-08
Issue Date:	2026-04-23 12:52:26 (UTC+8)
Publisher:	BioMed Central Ltd.;London: BioMed Central
Abstract:	摘要： Background Protein 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. Results In 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. Conclusion This 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. 其他題名： BMC Bioinformatics 出版者： London: BioMed Central 出版日期： 2014-12-08 出處： BMC bioinformatics, 2014-12, Vol.15 (Suppl 16), p.S1-S1, Article S1 資源來源： Publicly Available Content Database (Proquest) 版權： Wu et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated. 版權： 2014 Wu et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. 版權： Copyright © 2014 Wu et al.; licensee BioMed Central Ltd. 2014 Wu et al.; licensee BioMed Central Ltd. 識別號： ISSN: 1471-2105 識別號： EISSN: 1471-2105 識別號： DOI: 10.1186/1471-2105-15-S16-S1 識別號： PMID: 25521204
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