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Protein & Peptide Letters


ISSN (Print): 0929-8665
ISSN (Online): 1875-5305

Prediction of Protein Secondary Structure Using Improved Two-Level Neural Network Architecture

Author(s): Xin Huang, De-Shuang Huang and Guang-Zheng Zhang

Volume 12, Issue 8, 2005

Page: [805 - 811] Pages: 7

DOI: 10.2174/0929866054864328

Price: $65


In this paper we propose constructing an improved two-level neural network to predict protein secondary structure. Firstly, we code the whole protein composition information as the inputs to the first-level network besides the evolutionary information. Secondly, we calculate the reliability score for each residue position based on the output of the first-level network, and the role of the second-level network is to take full advantage of the residues with a higher reliability score to impact the neighboring residues with a lower one for improving the whole prediction accuracy. Thirdly, considering it is indeed a problem that the target protein can be lost in the multiple sequence alignment we propose to code single sequence into the second-level network. The experimental results show that our proposed method can efficiently improve the prediction accuracy.

Keywords: protein secondary structure prediction, neural network, two-level network architecture, protein composition, reliability score

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