Artificial neural network method for predicting protein secondary structure content.
In this paper, the neural network method was applied to predict the content of protein secondary structure elements that was based on 'pair-coupled amino acid composition', in which the sequence coupling effects are explicitly included through a series of conditional probability elements. The prediction was examined by a self-consistency test and an independent-dataset. Both indicated good results obtained when using the neural network method to predict the contents of alpha-helix, beta-sheet, parallel beta-sheet strand, antiparallel beta-sheet strand, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend, and random coil.
Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences. y.cai@umist.ac.uk
Yu-Dong
YD
Cai
Xiao-Jun
XJ
Liu
Xue-Biao
XB
Xu
Kuo-Chen
KC
Chou
D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary