Prediction Method of Protein Disulfide Bond Based on Pattern Selection

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.