title: Gly-PseAAC: Identifying protein lysine glycation through sequences.
abstract: BACKGROUND
  Similar to the regular enzymatic glycosylation, glycation also attaches a sugar molecule to a peptide, but does not need the help of an enzyme. Glycation may occur both inside and outside the host body, and will compete with the glycosylation procedure for functional regulation of mature protein products. The glycated residues do not show significant patterns, which make both in silico sequence-level predictors and wet-lab validations a major challenge. This study hypothesizes that a better feature set formulated from the glycated flanking peptides may lead to a good glycation prediction program.
  
  
  RESULTS
  We explored the application of sequence order information and position specific amino acid propensity (PSAAP) in the glycation residue prediction problem. The PSAAP demonstrated its ability to discriminate the glycated residues from the background control peptides. A Support Vector Machine (SVM) model was constructed from the training dataset and achieved 68.91% in the overall accuracy. The model also achieves 0.7258 and 0.3198 in the Area under the ROC and Matthew's Correlation Coefficient, respectively. The user-friendly online version of the proposed algorithm may be found on the web server Gly-PseAAC at http://app.aporc.org/Gly-PseAAC/.
  
  
  CONCLUSIONS
  The feature set PSAAP was calculated and led to a useful classification of glycation residues.
authors.affiliation: Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China. Electronic address: xuyan@ustb.edu.cn.
authors.forename: Yan
authors.initials: Y
authors.lastname: Xu
authors.affiliation: Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China. Electronic address: s20157158@ustb.edu.cn.
authors.forename: Li
authors.initials: L
authors.lastname: Li
authors.affiliation: Department of Information and Computer Science, University of Science and Technology Beijing, Beijing 100083, China. Electronic address: artin@ustb.edu.cn.
authors.forename: Jun
authors.initials: J
authors.lastname: Ding
authors.affiliation: Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: wulingyun@gmail.com.
authors.forename: Ling-Yun
authors.initials: LY
authors.lastname: Wu
authors.affiliation: Center for Synthetic Biology Engineering Research, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China. Electronic address: gq.mai@siat.ac.cn.
authors.forename: Guoqin
authors.initials: G
authors.lastname: Mai
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China.. Electronic address: ffzhou@jlu.edu.cn.
authors.forename: Fengfeng
authors.initials: F
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D030562:Databases, Protein; D006020:Glycopeptides / Q000737:chemistry* / Q000378:metabolism*; D006031:Glycosylation; D008239:Lysine / Q000378:metabolism*; D011499:Protein Processing, Post-Translational; D060388:Support Vector Machine
chemical_list: D006020:Glycopeptides; D008239:Lysine
keywords: Amino acid propensity; Glycation; Support Vector Machine