Abstract
In order to identify continuous B-cell epitopes effectively and to increase the success rate of experimental identification, the modified Back Propagation artificial neural network (BP neural network) was used to predict the continuous B-cell epitopes, and finally the predictive model for the B-cells epitopes was established. Comparing with the other predictive models, the prediction performance of this model is more excellent (AUC = 0.723). For the purpose of verifying the performance of the model, the prediction to the SWISS PROT NUMBER: P08677 was carried on, and the satisfying results were obtained.
Highlights
Epitope, called antigenic determinant, refers to specific structure site of antigen molecular which is recognized by specific effect molecules or T, B lymphocyte cells in immune response
The modified BP neural network was used to predict the continuous B-cell epitopes, and the predictive model for the B-cells epitopes was established to prediction of the SWISS PROT NUMBER: P08677, and the establishment of modified BP neural network provided a new method for studying some protein system
If predicted value is more than threshold, the peptide is considered as B cell epitope; otherwise, it is not considered as B cell epitope
Summary
Called antigenic determinant, refers to specific structure site of antigen molecular which is recognized by specific effect molecules or T, B lymphocyte cells in immune response. (2016) Theoretical Study of Continuous B-Cell Epitopes with Developed BP Neural Network. Methods to predict discontinuous B cell epitopes are mainly structure-based and combination of sequence and structure. Current prediction focuses on the continuous epitopes. The modified BP neural network was used to predict the continuous B-cell epitopes, and the predictive model for the B-cells epitopes was established to prediction of the SWISS PROT NUMBER: P08677, and the establishment of modified BP neural network provided a new method for studying some protein system
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