Fundamentals and Methods for T- and B-Cell Epitope Prediction

被引:439
作者
Sanchez-Trincado, Jose L. [1 ]
Gomez-Perosanz, Marta [1 ]
Reche, Pedro A. [1 ]
机构
[1] Univ Complutense Madrid, Lab Immunomed, Fac Med, Ave Complutense S-N, Madrid 28040, Spain
关键词
MHC CLASS-I; ARTIFICIAL NEURAL-NETWORK; AMINO-ACID-COMPOSITION; SVM BASED METHOD; PEPTIDE-BINDING; QUANTITATIVE PREDICTIONS; ANTIGENIC DETERMINANTS; PROTEASOMAL CLEAVAGE; VACCINE DEVELOPMENT; COMPUTER-PROGRAM;
D O I
10.1155/2017/2680160
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Adaptive immunity is mediated by T- and B-cells, which are immune cells capable of developing pathogen-specific memory that confers immunological protection. Memory and effector functions of B- and T-cells are predicated on the recognition through specialized receptors of specific targets (antigens) in pathogens. More specifically, B- and T-cells recognize portions within their cognate antigens known as epitopes. There is great interest in identifying epitopes in antigens for a number of practical reasons, including understanding disease etiology, immune monitoring, developing diagnosis assays, and designing epitope-based vaccines. Epitope identification is costly and time-consuming as it requires experimental screening of large arrays of potential epitope candidates. Fortunately, researchers have developed in silico prediction methods that dramatically reduce the burden associated with epitope mapping by decreasing the list of potential epitope candidates for experimental testing. Here, we analyze aspects of antigen recognition by T- and B-cells that are relevant for epitope prediction. Subsequently, we provide a systematic and inclusive review of the most relevant B- and T-cell epitope prediction methods and tools, paying particular attention to their foundations.
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页数:14
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