title: Prediction of protein supersecondary structures based on the artificial neural network method.
abstract: The sequence patterns of 11 types of frequently occurring connecting peptides, which lead to a classification of supersecondary motifs, were studied. A database of protein supersecondary motifs was set up. An artificial neural network method, i.e. the back propagation neural network, was applied to the predictions of the supersecondary motifs from protein sequences. The prediction correctness ratios are higher than 70%, and many of them vary from 75 to 82%. These results are useful for the further study of the relationship between the structure and function of proteins. It may also provide some important information about protein design and the prediction of protein tertiary structure.
authors.affiliation: Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing, P.R. China.
authors.forename: Z
authors.initials: Z
authors.lastname: Sun
authors.forename: X
authors.initials: X
authors.lastname: Rao
authors.forename: L
authors.initials: L
authors.lastname: Peng
authors.forename: D
authors.initials: D
authors.lastname: Xu
mesh_terms: D000595:Amino Acid Sequence; D016208:Databases, Factual; D008958:Models, Molecular; D016571:Neural Networks, Computer; D015202:Protein Engineering; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D012984:Software
chemical_list: D011506:Proteins