Development of computational tools for accounting for host variability in predicting T-cell epitopes

开发计算工具来解释预测 T 细胞表位时的宿主变异性

• 批准号: 10502033
• 负责人: Chris A. Kieslich
• 金额: $ 37.25万
• 依托单位: AUBURN UNIVERSITY AT AUBURN
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10502033
• 关键词: Accounting; Affect; Animal Model; Antigens; Area; Base Sequence; Binding; Cancer Vaccines; Communicable Diseases; Computing Methodologies; Coupled; Data; Data Set; Development; Epitopes; Generations; Genetic; Health; Human; Hypersensitivity; Immune; Immune response; Immune system; Immunology; Individual; Life; Logic; Machine Learning; Malignant Neoplasms; Measures; Modeling; Molecular Machines; Mutation; Outcome; Peptides; Persons; Predictive Value; Prevalence; Process; Proteins; Receptor Cell; Research; Safety; Sampling; Societies; Statistical Data Interpretation; Statistical Models; T-Cell Receptor; T-Lymphocyte Epitopes; Technology; Training; Uncertainty; Vaccine Design; Vaccine Production; Virus; Work; antigen processing; combat; computerized tools; emerging pathogen; genetic information; improved; interest; machine learning model; molecular modeling; outcome prediction; pathogen; peptide based vaccine; personalized medicine

PROJECT SUMMARY The processing of antigens through proteolytic degradation and the recognition of epitopes is central to the body’s ability to combat pathogens, like viruses, through discriminating self from non-self. As a result, there has been substantial research effort aimed at determining the outcomes of these processes for novel pathogens to enable epitope-driven vaccine design. There has also been great interest at the intersection of immunology and personalized medicine in identifying subject (host) specific epitopes, as these have great promise in the treatment of allergies and cancer where the distinction between self vs. non-self becomes blurred. Computational methods have emerged as promising approaches for identifying (predicting) epitopes that elicit a robust immune response given genetic information for an antigen. This is a very challenging task, which is compounded further due to the existence of uncertainty caused by genetic variability between pathogen strains, as well as, from individual to individual. Following this logic, it is also clear that using animal models in evaluating the immune response elicited by epitopes can often have limited predictive value, since sequence differences between a model species and humans can result in significantly different outcomes in terms of the peptides formed during antigen processing and epitopes recognized by immune cell receptors. Accordingly, there is an unmet need for computational tools that can predict the outcomes of antigen processing and epitope recognition in a host-dependent fashion, where the models take as input both antigen and host-specific genetic data. We propose the development of computational tools in three related areas to meet these needs: i) Prediction of peptides formed through antigen processing; ii) Prediction of epitope recognition by MHC molecules and T-cell receptors; and iii) Probabilistic analysis of epitopes most likely to elicit an immune response. In the proposed work, molecular modeling and machine learning will be used to develop accurate models of antigen processing and epitope binding to MHC molecules and T-cell receptors. Molecular models will first allow us to identify key interactions between the antigen and immune system proteins, which when coupled with statistical data can allow us to understand how mutations would affect those interactions. The statistical analysis of the effects of mutations will be applied to large publicly available datasets to sufficiently capture the effects of mutations on antigen processing and epitope recognition and will ultimately be incorporated into machine learning models. The proposed probabilistic models will apply a scenario-driven approach for capturing uncertainty in epitope generation and recognition. We will sample potential antigen and human sequences based on known distributions of mutation prevalence to measure the likelihood that an identified epitope will be generated and elicit a robust immune response. The proposed computational tools, if successful, could have substantial impact on the areas of epitope-driven vaccine design, including personalized cancer vaccines, and the identification of allergy related epitopes.

项目摘要 通过蛋白水解降解和表位识别对抗原的加工是免疫调节的核心。 身体对抗病原体（如病毒）的能力，通过区分自我和非自我。结果是 已经进行了大量的研究工作，旨在确定这些过程的结果， 病原体，使表位驱动的疫苗设计。也有很大的兴趣在交叉点， 免疫学和个性化医学在识别受试者（宿主）特异性表位方面具有重要意义 在治疗过敏症和癌症方面，自我与非自我之间的区别变得 模糊不清计算方法已经成为识别（预测）表位的有前途的方法 在给定抗原的遗传信息的情况下引发强有力的免疫应答。这是一项非常具有挑战性的任务， 这是进一步复杂化，由于存在的不确定性所造成的遗传变异之间 病原体菌株，以及从个体到个体。按照这个逻辑，很明显，使用动物 评估由表位引起的免疫应答的模型通常具有有限的预测价值， 模式物种和人类之间的序列差异可能导致在基因组学上的显著不同结果。 抗原处理过程中形成的肽和免疫细胞受体识别的表位的术语。 因此，对于可以预测抗原免疫的结果的计算工具存在未满足的需求。 处理和表位识别的宿主依赖性的方式，其中模型作为输入的抗原 和宿主特异性基因数据。我们建议在三个相关领域开发计算工具， 满足这些需求：i）预测通过抗原加工形成的肽; ii）预测表位 通过MHC分子和T细胞受体的识别;和iii）最有可能识别的表位的概率分析。 引发免疫反应在拟议的工作中，分子建模和机器学习将用于 开发抗原加工和表位与MHC分子和T细胞受体结合的精确模型。 分子模型将首先使我们能够识别抗原和免疫系统之间的关键相互作用 蛋白质，当与统计数据相结合时，可以让我们了解突变如何影响这些蛋白质， 交互.突变效应的统计分析将应用于大型公开可用的 数据集，以充分捕获突变对抗原加工和表位识别的影响，并将 最终被纳入机器学习模型。所提出的概率模型将适用于 用于捕获表位生成和识别中的不确定性的神经元驱动方法。我们将取样 潜在的抗原和人类序列的基础上的突变流行的已知分布，以衡量 这意味着将产生鉴定的表位并引发稳健的免疫应答的可能性。拟议 计算工具，如果成功的话，可能对表位驱动的疫苗设计领域产生重大影响， 包括个体化癌症疫苗和过敏相关表位的鉴定。