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.
Highlights
The protein disulfide bond is important component for many proteins; it can maintain the stability and function activity of proteins
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
Samples are taken on the pattern selection based on the nearest neighbor algorithm and corresponding prediction models are set by using RBF neural network
Summary
The protein disulfide bond is important component for many proteins; it can maintain the stability and function activity of proteins. The correct orientation of protein disulfide bond is very important to grasp the relationship of the protein structure and its biological function. In order to enhance the predicting precision of the protein disulfide bond, a prediction method of protein disulfide bond on the basis of pattern selection and RBF neural network have been brought forward in this paper. In the method proteins with disulfide bond structure from the PDB database are encoded according to the encoding. 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 experiment indicates that the method could enhance the predicting accuracy of the disulfide bond effectively
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