title: Prediction of conformational B-cell epitopes.
abstract: Conformational B-cell epitopes play an important role in the epitope-based vaccine design. The increase of available data promotes the development of computational methods. Compared with the wet experiments, the computational methods are faster and more economic. In the past few years, a number of computational methods (especially the machine learning-based methods) have been developed to predict the conformational B-cell epitopes. In this chapter, we introduce important data resources and computational methods, which are publicly available. Moreover, we introduce our ensemble learning-based method that can predict the conformational epitopes from sequences. These promising methods may assist immunologists in identifying potential vaccine candidates.
authors.affiliation: School of Computer, Wuhan University, No. 37, Luoyu Road, Wuchang, Wuhan, 430072, China, zhangwen@whu.edu.cn.
authors.forename: Wen
authors.initials: W
authors.lastname: Zhang
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Niu
authors.forename: Yi
authors.initials: Y
authors.lastname: Xiong
authors.forename: Meng
authors.initials: M
authors.lastname: Ke
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry* / Q000276:immunology; D006801:Humans; D008968:Molecular Conformation
chemical_list: D018985:Epitopes, B-Lymphocyte