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.

项目概要 通过蛋白水解降解和表位识别来处理抗原是抗原的核心。 身体通过区分自我和非自我来对抗病原体（如病毒）的能力。 已经进行了大量的研究工作，旨在确定这些过程的结果 病原体以实现表位驱动的疫苗设计也引起了人们的极大兴趣。 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很大的作用 自我与非自我之间的区别成为治疗过敏和癌症的希望 计算方法已成为识别（预测）表位的有前景的方法。 给定抗原的遗传信息，引发强大的免疫反应，这是一项非常具有挑战性的任务， 由于遗传变异引起的不确定性的存在，情况变得更加复杂。 致病菌株，以及从个体到个体，按照这个逻辑，使用动物也是很明显的。 评估表位引发的免疫反应的模型通常具有有限的预测价值，因为 模型物种和人类之间的序列差异可能导致显着不同的结果 术语是在抗原加工过程中形成的肽和免疫细胞受体识别的表位。 ly，对可以预测抗原结果的计算工具的需求尚未得到满足 以宿主依赖性方式进行处理和表位识别，模型将两种抗原作为输入 我们建议在三个相关领域开发计算工具。 满足这些需求： i) 预测通过抗原处理形成的肽 ii) 预测表位； MHC 分子和 T 细胞受体的识别；以及 iii) 最有可能的表位的概率分析 在拟议的工作中，将使用分子建模和机器学习来引发免疫反应。 开发抗原加工和表位与 MHC 分子和 T 细胞受体结合的准确模型。 分子模型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，我们可以了解突变如何影响这些蛋白质 突变影响的统计分析将应用于大量公开的数据。 数据集足以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中。所提出的概率模型将应用 我们将采用场景驱动的方法来捕获表位生成和识别中的不确定性。 基于已知的突变流行率分布的潜在抗原和人类序列来测量 产生已识别表位并引发强烈免疫反应的可能性。 计算工具如果成功，可能会对表位驱动的疫苗设计领域产生重大影响， 包括个性化癌症疫苗以及过敏相关表位的鉴定。