Computational Prediction of MHC Class II Epitopes

MHC II 类表位的计算预测

• 批准号: 7080713
• 负责人: YANG DAI
• 金额: $ 7.17万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7080713
• 关键词: alleles; learning; model; peptides; performance

DESCRIPTION (provided by applicant): The discovery of novel T cell epitopes will greatly facilitate the design and development of improved vaccines by providing critical information needed for the selection of complexes between the major histocompatibility complex (MHC) molecules and antigen peptides that can induce T cell activation. One of the key steps for the epitope identification is the prediction of MHC-peptide binding. Two major classes of MHC molecules are involved in the generation of two types of T cell epitopes. Methods for the prediction of MHC class I epitopes have achieved relatively high accuracy, since the binding motifs of the epitopes are relatively conserved. However, the performance of the prediction methods for MHC class II epitopes are hindered by the variable lengths of the epitopes, the undetermined core region for each individual epitope, and the unknown amino acids as primary anchors. Most of the existing methods attempt to identify binding cores for a set of epitopes through various alignment techniques. Binding motifs or the position specific scoring matrices for prediction can then be assembled from the identified alignment. Motivated by a text mining technique, we have developed a prototype of an supervised learning model for the MHC class II epitope prediction. The idea is to discriminate the core binding nonamers from the non-core nonamers derived from a training set consisting of epitopes and non-epitopes through an iterative process. The characteristics of this model are the simplicity and the capacity of using information both from epitopes and non-epitopes. The preliminary study demonstrated promising performance of this model for HLA-DR4 (Bl*0401) epitopes. In this study, we plan to conduct a thorough evaluation and the optimization of this model. In Aim 1, we will develop the principle for optimization of the model and select the best variant of the method. In Aim 2, we will conduct a thorough evaluation against existing major predictors for various allele specific data. Finally, in Aim 3, we will establish a web server for the prediction of various MHC class II allele-specific epitopes. The system will be freely available to the research community. Our long-term goal is the development of computation methods for prediction of T-cell epitopes. The computational prediction can provide a rapid method for the of pathogen molecules containing immunostimulatory sequences that can serve as targets for immune intervention or diagnostics.

描述（由申请人提供）：新T细胞表位的发现将通过提供选择主要组织相容性复合体（MHC）分子和可诱导T细胞活化的抗原肽之间的复合物所需的关键信息，极大地促进改良疫苗的设计和开发。表位鉴定的关键步骤之一是预测MHC-肽结合。两种主要类型的MHC分子参与两种类型的T细胞表位的产生。用于预测MHC I类表位的方法已经实现了相对高的准确性，因为表位的结合基序相对保守。然而，MHC II类表位的预测方法的性能受到表位的可变长度、每个单独表位的不确定的核心区以及作为主要锚的未知氨基酸的阻碍。大多数现有的方法试图通过各种比对技术来鉴定一组表位的结合核心。然后可以从鉴定的比对组装用于预测的结合基序或位置特异性评分矩阵。出于文本挖掘技术，我们已经开发了一个原型的监督学习模型的MHC II类表位预测。该想法是通过迭代过程区分核心结合九聚体与衍生自由表位和非表位组成的训练集的非核心九聚体。该模型的特点是结构简单，能够同时利用表位和非表位信息。初步研究表明，该模型对HLA-DR 4（BI *0401）表位具有良好的性能。在本研究中，我们计划对该模型进行全面的评估和优化。在目标1中，我们将制定模型优化的原则，并选择方法的最佳变体。在目标2中，我们将针对各种等位基因特异性数据的现有主要预测因子进行全面评估。最后，在目标3中，我们将建立一个Web服务器，用于预测各种MHC II类等位基因特异性表位。该系统将免费提供给研究界。我们的长期目标是发展预测T细胞表位的计算方法。计算预测可以提供一种快速的方法，用于检测含有免疫刺激序列的病原体分子，所述免疫刺激序列可以用作免疫干预或诊断的靶标。