Title:Advances in In-silico B-cell Epitope Prediction
VOLUME: 19 ISSUE: 2
Author(s):Pingping Sun, Sijia Guo, Jiahang Sun, Liming Tan, Chang Lu and Zhiqiang Ma*
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
Keywords:Epitope prediction, Linear epitope, Conformational epitope, BCR, B-cell, Epitope.
Abstract:Identification of B-cell epitopes in target antigens is one of the most crucial steps for epitopebased
vaccine development, immunodiagnostic tests, antibody production, and disease diagnosis and
therapy. Experimental methods for B-cell epitope mapping are 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 presents 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 to stimulate the development of better tools which could improve the
identification of B-cell epitopes, and further for the development of therapeutic antibodies and diagnostic
tools.
