An Efficient Method for Protein Secondary Structure Prediction

The secondary structure prediction of protein plays an important role to obtain its tertiary structure and function. In the past thirty years, a huge amount of algorithms have been employed to this task. The better predicators are based on machine learning techniques, especially based on neural networks. But the architecture of neural network is hard to define, and the training process is time-consuming. In this paper, a constructive machine learning approach is used to predict protein secondary structure with five different encoding schemes, the results show that the constructive algorithm can achieve high predicting accuracies and the encoding schemes have influence on predicting result.

[1]  Sun Hai-jun.  Research on Encode Influencing Protein Secondary Structure Prediction , 2005 .

[2]  T. Sejnowski,et al.  Predicting the secondary structure of globular proteins using neural network models. , 1988, Journal of molecular biology.

[3]  W. Kabsch,et al.  Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features , 1983, Biopolymers.

[4]  Arthur M. Lesk,et al.  Introduction to bioinformatics , 2002 .

[5]  P. Y. Chou,et al.  Prediction of protein conformation. , 1974, Biochemistry.

[6]  Bo Zhang,et al.  A geometrical representation of McCulloch-Pitts neural model and its applications , 1999, IEEE Trans. Neural Networks.

[7]  S. Hua,et al.  A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. , 2001, Journal of molecular biology.

[8]  S. Akkaladevi,et al.  Protein secondary structure prediction using neural network and simulated annealing algorithm , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[10]  Bo Zhang,et al.  A neural network based classifier for handwritten Chinese character recognition , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[11]  Juan Liu,et al.  A comparison of two machine learning methods for protein secondary structure prediction , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).