A two-layered machine learning method to identify protein O-GlcNAcylation sites with O-GlcNAc transferase substrate motifs. Issue 18 (December 2015)

• Record Type: Journal Article
• Authors: Kao, Hui-Ju; Huang, Chien-Hsun; Bretaña, Neil; Lu, Cheng-Tsung; Huang, Kai-Yao; Weng, Shun-Long; Lee, Tzong-Yi
• Is Part Of: BMC bioinformatics. Volume 16:Issue 18(2015)
• Page Start: 1
• Page End: 11
• Publication Date: 2015-12
• Subjects: O-GlcNAcylation -- O-linked glycosylation -- O-GlcNAc transferase (OGT) -- substrate motif -- profile hidden Markov model -- support vector machine; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/1471-2105-16-S18-S10
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Protein O-GlcNAcylation, involving the β-attachment of singleN -acetylglucosamine (GlcNAc) to the hydroxyl group of serine or threonine residues, is an O-linked glycosylation catalyzed by O-GlcNAc transferase (OGT). Molecular level investigation of the basis for OGT's substrate specificity should aid understanding how O-GlcNAc contributes to diverse cellular processes. Due to an increasing number of O-GlcNAcylated peptides with site-specific information identified by mass spectrometry (MS)-based proteomics, we were motivated to characterize substrate site motifs of O-GlcNAc transferases. In this investigation, a non-redundant dataset of 410 experimentally verified O-GlcNAcylation sites were manually extracted from dbOGAP, OGlycBase and UniProtKB. After detection of conserved motifs by using maximal dependence decomposition, profile hidden Markov model (profile HMM) was adopted to learn a first-layered model for each identified OGT substrate motif. Support Vector Machine (SVM) was then used to generate a second-layered model learned from the output values of profile HMMs in first layer. The two-layered predictive model was evaluated using a five-fold cross validation which yielded a sensitivity of 85.4%, a specificity of 84.1%, and an accuracy of 84.7%. Additionally, an independent testing set from PhosphoSitePlus, which was really non-homologous to the training data of predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (84.05%) and outperform other O-GlcNAcylation site prediction tools. A case study indicated that the proposed method could be a feasible means of conducting preliminary analyses of protein O-GlcNAcylation and has been implemented as a web-based system, OGTSite, which is now freely available athttp://csb.cse.yzu.edu.tw/OGTSite/ .