Analysis of T cell epitope repertoire.

T 细胞表位库分析。

• 批准号: 6916432
• 负责人: Yoram Louzoun
• 金额: $ 13.3万
• 依托单位: BAR-ILAN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-15 至 2008-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6916432
• 关键词: Internet; MHC class I antigen; T lymphocyte; alleles; antigen presentation; antigenic peptide transporter; autoimmunity; bioengineering /biomedical engineering; bioinformatics; combinatorial chemistry; computational biology; cytotoxic T lymphocyte; gene expression; immunoglobulin G; information dissemination; insulin dependent diabetes mellitus; intermolecular interaction; major histocompatibility complex; multiple sclerosis; peptide library; proteomics; vaccine development; virus diseases; virus genetics

DESCRIPTION (provided by applicant): At the core of transplant rejection, immune defense against viruses and autoimmunity and is the recognition of peptides in the context of MHC-I molecules by CD8 T cells and NK cells. Here, we will combine our skills in immunology, computer modeling, database design and mathematical analysis to generate libraries of predicted self and viral peptides for most major MHC-I alleles in mouse and men. We collaborate with two experimental groups to validate the predicted peptides and study their correlation with autoimmunity. The main elements we will develop and merge are estimates of peptide MHC-I binding, TAP binding and peptide cleavage probability. We will combine the resulting scores and apply them to existing genomic data. To produce the human libraries, we will develop new bioinformatics tools and merge them with existing tools. The result will be a score determining the probability of a peptide to be presented. We will apply this score to the human, mouse and to some viral genomes, in order to determine the self and non-self repertoire. The expected libraries will be validated through a collaboration with an experimental group. Each validated library will contain a list of peptides (from human, murine or viral source), which can be presented on a given MHC-I allele, accompanied by a score representing the "quality" of the peptide. This score will include the estimated binding affinity to the MHC binding groove, the TAP binding probability, the cleavage probability (either by the regular or the immuno-proteasome) and the average expression level of the gene carrying the peptide. Viral peptides will also include a score representing their maximal similarity to self peptides. The resulting validated libraries will be publicly accessible through a web based server. The accurate prediction of allele specific MHC-peptide interactions may enable us to manipulate the autoimmune response in organ specific autoimmune diseases, and the immune response against viruses. It may also help immunologist improve their ability to optimize MHC-matching for transplantation. The main applications of the libraries that will be developed in the current research program are: A) A peptide vaccine development methodology. We will use the viral peptide libraries in order to detect peptides from conserved viral genomic regions present in these libraries. We will use these peptides to produce genes sequences that code for proteins having the same immunological profile as the viruses, without its pathogenic effects. These peptides are good candidates for peptide vaccines in cases where an attenuated virus is too dangerous or unavailable. B) Analysis of the profiles presented to the immune system in autoimmune diseases (mainly IDDM and MS) and the link between MHC usage and autoimmunity. This part of the research will be done in colaboration with an experimental group studying the B and T cell repertoire in autoimmunity.

描述（由申请人提供）： 移植排斥、针对病毒和自身免疫的免疫防御的核心是 CD8 T 细胞和 NK 细胞对 MHC-I 分子背景下的肽的识别。在这里，我们将结合我们在免疫学、计算机建模、数据库设计和数学分析方面的技能，为小鼠和人类的大多数主要 MHC-I 等位基因生成预测的自身肽和病毒肽库。我们与两个实验组合作来验证预测的肽并研究它们与自身免疫的相关性。我们将开发和合并的主要元素是肽 MHC-I 结合、TAP 结合和肽裂解概率的估计。我们将结合所得分数并将其应用于现有的基因组数据。 为了创建人类图书馆，我们将开发新的生物信息学工具并将其与现有工具合并。结果将是确定肽被呈现的概率的分数。我们将把这个分数应用于人类、小鼠和一些病毒基因组，以确定自我和非自我的全部。预期的库将通过与实验小组的合作进行验证。每个经过验证的文库将包含一系列肽（来自人类、鼠类或病毒来源），这些肽可以呈现在给定的 MHC-I 等位基因上，并附有代表肽“质量”的分数。该分数将包括与 MHC 结合槽的估计结合亲和力、TAP 结合概率、切割概率（通过常规蛋白酶体或免疫蛋白酶体）以及携带肽的基因的平均表达水平。病毒肽还将包括代表它们与自身肽的最大相似性的分数。由此产生的经过验证的库将可以通过基于网络的服务器公开访问。 等位基因特异性 MHC-肽相互作用的准确预测可能使我们能够操纵器官特异性自身免疫性疾病中的自身免疫反应，以及针对病毒的免疫反应。它还可以帮助免疫学家提高优化 MHC 移植匹配的能力。当前研究计划中将开发的文库的主要应用是： A) 肽疫苗开发方法。我们将使用病毒肽文库来检测这些文库中存在的保守病毒基因组区域的肽。我们将使用这些肽来产生基因序列，编码具有与病毒相同的免疫学特征的蛋白质，而没有其致病作用。在减毒病毒太危险或无法获得的情况下，这些肽是肽疫苗的良好候选者。 B) 分析自身免疫性疾病（主要是 IDDM 和 MS）中免疫系统的概况以及 MHC 使用与自身免疫之间的联系。这部分研究将与研究自身免疫中 B 和 T 细胞库的实验组合作完成。