Molero-Abraham Magdalena, Lafuente Esther M, Reche Pedro
Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain.
Methods Mol Biol. 2014;1184:319-32. doi: 10.1007/978-1-4939-1115-8_18.
Peptide binding to major histocompatibility complex (MHC) molecules is the most selective requisite for T-cell recognition. Therefore, prediction of peptide-MHC binding is the main basis for anticipating T-cell epitopes. A very popular and accurate method to predict peptide-MHC binding is based on motif-profiles and here we show how to make them using EPIMHC (http://imed.med.ucm.es/epimhc/). EPIMHC is a database of T-cell epitopes and MHC-binding peptides that unlike any related resource provides a framework for computational vaccinology. In this chapter, we describe how to derive peptide-MHC binding motif-profiles in EPIMHC and use them to predict peptide-MHC binding and T-cell epitopes. Moreover, we show evidence that customization of peptide-MHC binding predictors can lead to enhanced epitope predictions.
肽与主要组织相容性复合体(MHC)分子的结合是T细胞识别最具选择性的必要条件。因此,肽-MHC结合的预测是预测T细胞表位的主要依据。一种非常流行且准确的预测肽-MHC结合的方法是基于基序谱,在这里我们展示如何使用EPIMHC(http://imed.med.ucm.es/epimhc/)来制作这些基序谱。EPIMHC是一个T细胞表位和MHC结合肽的数据库,与任何相关资源不同的是,它为计算疫苗学提供了一个框架。在本章中,我们描述了如何在EPIMHC中推导肽-MHC结合基序谱,并使用它们来预测肽-MHC结合和T细胞表位。此外,我们还证明了定制肽-MHC结合预测器可以提高表位预测的准确性。