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Artificial neural network method for predicting protein secondary structure content.

Yu-Dong Cai·Xiao-Jun Liu·Xue-Biao Xu·Kuo-Chen Chou
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Abstract

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.

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Yu-Dong Cai, Xiao-Jun Liu, Xue-Biao Xu, Kuo-Chen Chou. 2002. Artificial neural network method for predicting protein secondary structure content.. https://doi.org/10.1016/s0097-8485(01)00125-5

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