title: Prediction of four kinds of simple supersecondary structures in protein by using chemical shifts.
abstract: Knowledge of supersecondary structures can provide important information about its spatial structure of protein. Some approaches have been developed for the prediction of protein supersecondary structure. However, the feature used by these approaches is primarily based on amino acid sequences. In this study, a novel model is presented to predict protein supersecondary structure by use of chemical shifts (CSs) information derived from nuclear magnetic resonance (NMR) spectroscopy. Using these CSs as inputs of the method of quadratic discriminant analysis (QD), we achieve the overall prediction accuracy of 77.3%, which is competitive with the same method for predicting supersecondary structures from amino acid compositions in threefold cross-validation. Moreover, our finding suggests that the combined use of different chemical shifts will influence the accuracy of prediction.
authors: College of Science, Inner Mongolia Agriculture University, Hohhot 010018, China.
authors: Feng
authors: F
authors: Yonge
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D008958:Models, Molecular; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results
chemical_list: D011506:Proteins