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Advances in in-silico B-cell epitope prediction.

Author(s):

Pingping Sun, Sijia Guo, Jiahang Sun, Liming Tan , Chang Lu and Zhiqiang Ma*  

Abstract:


Identification of B-cell epitopes in target antigens is one of the most crucial steps for epitope-based vaccine development, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. Experimental methods for B-cell epitope mapping is time consuming, costly and labor intensive; in the meantime, various in-silico methods are proposed to predict both linear and conformational B-cell epitopes. The accurate identification of B-cell epitopes present major challenges for immunoinformaticians. In this paper, we have comprehensively reviewed in-silico methods for B-cell epitope identification. The aim of this review is for stimulating the development of better tools which could improve the identification of B-cell epitopes, and further for development of therapeutic antibodies and diagnostic tools.

Keywords:

epitope prediction, linear epitope, conformational epitope

Affiliation:

School of Information Science and Technology, Northeast Normal University, Changchun 130117, School of Information Science and Technology, Northeast Normal University, Changchun 130117, School of Information Science and Technology, Northeast Normal University, Changchun 130117, School of Information Science and Technology, Northeast Normal University, Changchun 130117, School of Information Science and Technology, Northeast Normal University, Changchun 130117, School of Information Science and Technology, Northeast Normal University, Changchun 130117



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