Prediction of MHC-Peptide Binding: A Systematic and Comprehensive Overview

被引:109
|
作者
Lafuente, Esther M. [1 ]
Reche, Pedro A. [1 ,2 ]
机构
[1] Univ Complutense Madrid, Fac Med, Dept Immunol, E-28040 Madrid, Spain
[2] Univ Complutense Madrid, Fac Med, Lab ImmuneMed, E-28040 Madrid, Spain
关键词
Peptide; T cells; epitopes; MHC; HLA; prediction; T-CELL EPITOPES; MAJOR HISTOCOMPATIBILITY COMPLEX; CLASS-I MOLECULES; ARTIFICIAL NEURAL-NETWORK; SUPPORT VECTOR MACHINE; HIDDEN MARKOV-MODELS; QUANTITATIVE PREDICTION; SILICO IDENTIFICATION; STRUCTURAL PREDICTION; INDEPENDENT BINDING;
D O I
10.2174/138161209789105162
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
T cell immune responses are driven by the recognition of peptide antigens (T cell epitopes) that are bound to major histocompatibility complex (MHC) molecules. T cell epitope immunogenicity is thus contingent on several events, including appropriate and effective processing of the peptide from its protein source, stable peptide binding to the MHC molecule, and recognition of the MHC-bound peptide by the T cell receptor. Of these three hallmarks, MHC-peptide binding is the most selective event that determines T cell epitopes. Therefore, prediction of MHC-peptide binding constitutes the principal basis for anticipating potential T cell epitopes. The tremendous relevance of epitope identification in vaccine design and in the monitoring of T cell responses has spurred the development of many computational methods for predicting MHC-peptide binding that improve the efficiency and economics of T cell epitope identification. In this report, we will systematically examine the available methods for predicting MHC-peptide binding and discuss their most relevant advantages and drawbacks.
引用
收藏
页码:3209 / 3220
页数:12
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