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Predict the Tertiary Structure of Protein with Flexible Neural Tree

  • Guangting Shao
  • Yuehui Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)

Abstract

Predicting the tertiary structure of protein from its primary amino acid sequence is a challenging mission for bioinformatics. In this paper we proposes a novel approach of predicting the tertiary structure of protein using the flexible neural tree (FNT) to construct a tree classification model. Two feature extraction methods (the physicochemical composition (PCC)) and the recurrence quantification analysis (RQA)) are employed to extract the features of protein sequence. To value the efficiencies of the proposed method we select two benchmark protein sequence datasets (1189 dataset and 640 dataset), as the test data set. The experimental results show that the proposed method is efficient for the protein structure prediction.

Keywords

Tertiary structure of protein Tree classification model FNT PCC RQA 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Guangting Shao
    • 1
  • Yuehui Chen
    • 1
  1. 1.Computational Intelligence Lab, Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina

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