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

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

Volume: 12 Issue: 8

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

Affiliation:

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

Abstract: 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.

Cite this article as:

Huang Xin, Huang De-Shuang and Zhang Guang-Zheng, Prediction of Protein Secondary Structure Using Improved Two-Level Neural Network Architecture, Protein & Peptide Letters 2005; 12 (8) . https://dx.doi.org/10.2174/0929866054864328