Improving protein secondary structure prediction by using the residue conformational classes

Authors:
Guang-Zheng Zhang;D. S. Huang;Y. P. Zhu;Y. X. Li
Affiliations:
Chinese Academy of Science, Hefei Institute of Intelligent Machines, CAS, P.O. Box 1130, HeFei, Anhui 230031, China and University of Science and Technology of China, HeFei, Anhui 230031, China an ...;Chinese Academy of Science, Hefei Institute of Intelligent Machines, CAS, P.O. Box 1130, HeFei, Anhui 230031, China;Beijing Institute of Radiation Medicine, Beijing 100850, China;Bioinformatics Center, Shanghai Institutes for Biological Sciences, CAS, Shanghai 200031, China
Venue:
Pattern Recognition Letters
Year:
2005

Citing 0
Cited 3

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Abstract

In this paper, based on the 340 protein sequences and their corresponding secondary structures retrieved from the protein data bank (PDB), we group the 20 different amino acid residues into 3 conformational categories: f (Former), b (Breaker) and n (Neutral), which reflect the intrinsic preference of the residue for a given type of secondary structure (@a-helix, @b-sheets and Coil). Then, based on radial basis function neural network (RBFNN) technique, we use this information to reconstruct the input vectors and try to improve globulin protein secondary structure prediction (SSP) accuracy. The experimental results indicate that our approach outperforms the previous conventional methods.