Prediction Method of Protein Disulfide Bond Based on Pattern Selection

Pengfei Sun, Yuanquan Cui, Tiankai Chen, Ying Zhao

College of Computer Science and Technology Harbin Normal University, Harbin, China.

DOI: 10.4236/eng.2013.510B083                

Abstract

The effect of the different training samples is different for the classifier when pattern recognition system is established. The training samples were selected randomly in the past protein disulfide bond prediction methods, therefore the prediction accuracy of protein contact was reduced. In order to improve the influence of training samples, a prediction method of protein disulfide bond on the basis of pattern selection and Radical Basis Function neural network has been brought forward in this paper. The attributes related with protein disulfide bond are extracted and coded in the method and pattern selection is used to select training samples from coded samples in order to improve the precision of protein disulfide bond prediction. 200 proteins with disulfide bond structure from the PDB database are encoded according to the encoding approach and are taken as models of training samples. Then samples are taken on the pattern selection based on the nearest neighbor algorithm and corresponding prediction models are set by using RBF neural network. The simulation experiment result indicates that this method of pattern selection can improve the prediction accuracy of protein disulfide bond.

Keywords

Protein Disulfide Bond; Neural Network; Nearest Neighbor Algorithm; Pattern Selection

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	S. M. Muskal, R. S. Holbrook and S. H. Kim, “Prediction of the Disulfide-Bonding State of Cysteine in Proteins,” Protein Engineering, Vol. 3, 1990, pp. 667-672. http://dx.doi.org/10.1093/protein/3.8.667
[2]	P. Fariselli, P. Riccobelli and R. Casadio, “Role of evolutionary Information in Predicting the Disulfide-Bonding State of Cysteine in Proteins,” Proteins, Vol. 36, 1999, pp. 340-346. http://dx.doi.org/10.1002/(SICI)1097-0134(19990815)36:3<340::AID-PROT8>3.0.CO;2-D
[3]	M. H. Mucchielli-Giorgi, S. Hazout and P. Tuffery, “Predicting the Disulfide Bonding State of Cysteines Using Protein Descriptors,” Proteins, Vol. 46, 2002, pp. 243- 249. http://dx.doi.org/10.1002/prot.10047
[4]	X.-H. Shi and Y. Wang, “Prediction of Disulfide Bonding in Protein Structure Based on Method of Graph Matching,” Computer Engineering and Applications, Vol. 43, 2007, pp. 30-32.
[5]	P. E. Hart, “The Condensed Nearest Neighbour Rule,” IEEE Transactions on Information Theory, Vol. 14, 1968, pp. 515-516. http://dx.doi.org/10.1109/TIT.1968.1054155
[6]	J.-M. Chandonia and M. Karplus, “New Methods for Accurate Prediction of Protein Secondary Structure,” Proteins, Vol. 35, 1999, pp. 293-306.
[7]	C. Dodge, R. Schneider and C. Sander, “The HSSP Database of Protein Structure-Sequence Alignments and Family Profiles,” Nucleic Acids Research, Vol. 26, 1998, pp. 313-315.
[8]	H.-W. Hao and R.-R. Jiang, “Training Sample Selection Method for Neural Networks Based on Nearest Neighbor Rule,” Acta Automatica Sinica, Vol. 33, 2007, pp. 1247- 1251.
[9]	W. Li and Y. Hori, “An Algorithm for Extracting Fuzzy Rules Based on RBF Neural Network,” IEEE Transactions on Industrial Electronics, Vol. 53, No. 4, 2006.