title: A two-layered machine learning method to identify protein O-GlcNAcylation sites with O-GlcNAc transferase substrate motifs.
abstract: Protein O-GlcNAcylation, involving the β-attachment of single N-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 at http://csb.cse.yzu.edu.tw/OGTSite/.
authors.forename: Hui-Ju
authors.initials: HJ
authors.lastname: Kao
authors.forename: Chien-Hsun
authors.initials: CH
authors.lastname: Huang
authors.forename: Neil Arvin
authors.initials: NA
authors.lastname: Bretaña
authors.forename: Cheng-Tsung
authors.initials: CT
authors.lastname: Lu
authors.forename: Kai-Yao
authors.initials: KY
authors.lastname: Huang
authors.forename: Shun-Long
authors.initials: SL
authors.lastname: Weng
authors.forename: Tzong-Yi
authors.initials: TY
authors.lastname: Lee
mesh_terms: D000117:Acetylglucosamine / Q000378:metabolism; D000465:Algorithms; D020816:Amino Acid Motifs; D006031:Glycosylation; D020407:Internet; D000069550:Machine Learning; D013058:Mass Spectrometry; D017351:N-Acetylglucosaminyltransferases / Q000378:metabolism*; D010455:Peptides / Q000032:analysis / Q000378:metabolism; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D013379:Substrate Specificity; D060388:Support Vector Machine; D014584:User-Computer Interface
chemical_list: D010455:Peptides; D011506:Proteins; D017351:N-Acetylglucosaminyltransferases; C449880:O-GlcNAc transferase; D000117:Acetylglucosamine