Advances in In-silico B-cell Epitope Prediction

Pingping Sun; Sijia Guo; Jiahang Sun; Liming Tan; Chang Lu; Zhiqiang Ma

doi:10.2174/1568026619666181130111827

Advances in In-silico B-cell Epitope Prediction

Curr Top Med Chem. 2019;19(2):105-115. doi: 10.2174/1568026619666181130111827.

Authors

Pingping Sun^{1

2

3}, Sijia Guo^{1

2

3}, Jiahang Sun^{1

2

3}, Liming Tan^{1

2

3}, Chang Lu^{1

2

3}, Zhiqiang Ma^{1

2

3}

Affiliations

¹ School of Information Science and Technology, Northeast Normal University, Changchun 130117, China.
² Key Laboratory of Intelligent Information Processing of Jilin University, Northeast Normal University, Changchun 130117, China.
³ Institute of Computational Biology, Northeast Normal University, Changchun 130117, China.

PMID: 30499399
DOI: 10.2174/1568026619666181130111827

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.

Keywords: B-cell; BCR; Conformational epitope; Epitope; Epitope prediction; Linear epitope..

Publication types

Review

MeSH terms

Antibodies / immunology
B-Lymphocytes / immunology*
Computer Simulation
Epitopes / chemistry
Epitopes / immunology*
Humans
Machine Learning
Protein Conformation

Substances

Antibodies
Epitopes