title: Identify Beta-Hairpin Motifs with Quadratic Discriminant Algorithm Based on the Chemical Shifts.
abstract: Successful prediction of the beta-hairpin motif will be helpful for understanding the of the fold recognition. Some algorithms have been proposed for the prediction of beta-hairpin motifs. However, the parameters used by these methods were primarily based on the amino acid sequences. Here, we proposed a novel model for predicting beta-hairpin structure based on the chemical shift. Firstly, we analyzed the statistical distribution of chemical shifts of six nuclei in not beta-hairpin and beta-hairpin motifs. Secondly, we used these chemical shifts as features combined with three algorithms to predict beta-hairpin structure. Finally, we achieved the best prediction, namely sensitivity of 92%, the specificity of 94% with 0.85 of Mathew's correlation coefficient using quadratic discriminant analysis algorithm, which is clearly superior to the same method for the prediction of beta-hairpin structure from 20 amino acid compositions in the three-fold cross-validation. Our finding showed that the chemical shift is an effective parameter for beta-hairpin prediction, suggesting the quadratic discriminant analysis is a powerful algorithm for the prediction of beta-hairpin.
authors.affiliation: College of Science, Inner Mongolia Agriculture University, Hohhot, PR China.
authors.forename: Feng
authors.initials: F
authors.lastname: YongE
authors.affiliation: College of Science, Inner Mongolia Agriculture University, Hohhot, PR China.
authors.forename: Kou
authors.initials: K
authors.lastname: GaoShan
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000596:Amino Acids / Q000737:chemistry*; D016002:Discriminant Analysis; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
chemical_list: D000596:Amino Acids; D011506:Proteins