title: Prediction of protein secondary structure content by artificial neural network.
abstract: The neural network method was applied to the prediction of the content of protein secondary structure elements, including alpha-helix, beta-strand, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend, and random coil. The "pair-coupled amino acid composition" originally proposed by K. C. Chou [J Protein Chem 1999, 18, 473] was adopted as the input. Self-consistency and independent-dataset tests were used to appraise the performance of the neural network. Results of both tests indicated high performance of the method.
authors.affiliation: Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, Shanghai, 200233, China. y.cai@umist.ac.uk
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Xiao-Jun
authors.initials: XJ
authors.lastname: Liu
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D008958:Models, Molecular; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary
chemical_list: D000596:Amino Acids