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 通过蛋白质降解和表位的识别是对抗原的加工是至关重要的 身体通过将自我与非自我歧视的能力，例如病毒。 旨在确定新型过程的结果，已经进行了实质性的研究工作 病原体使表位驱动的疫苗设计也很感兴趣 免疫学和个性化医学在识别特定主题（宿主）特定表位时，它们具有很好的表现 在治疗自我VS之间的区别的过敏和癌症方面有望 计算机。 给定给定给定抗原的遗传学含量信息的强大的IMUNE反应。 由于存在由遗传变异引起的不确定性，因此进一步综合 病原体菌株以及从个人到个体的逻辑。 评估表位引起的免疫反应的模型通常可以具有预测价值，因为 模型物种与人之间的序列差异可能会导致明显不同的结果 在抗原加工过程中形成的肽和免疫细胞受体识别的表位。 因此，对计算工具工具工具工具工具的未满足的需求是抗原的结果 以宿主依赖方式处理和表位识别是模型作为输入的抗原 和宿主特定的遗传数据。 满足这些需求：i）通过抗原加工形成的肽的预测； MHC分子和T细胞受体的识别； 在支撑工作，分子建模和机器学习中引起免疫反应 开发了与MHC分子和T细胞受体结合的抗原加工和表位结合的准确模型。 分子模型将首先允许我们确定抗原和免疫系统之间的关键相互作用 蛋白质，当与统计数据结合时，我可以理解突变将如何影响那些污垢 相互作用。 数据集可高效地捕获突变对抗原处理和表位识别的影响，并将威尔·威尔 最终将其纳入机器学习模型。 场景驱动的方法用于捕获的捕获不确定性，我们将采样 基于已知突变分布的潜在抗原和人类序列，以测量您 具有生成的表位并引起了稳健的免疫反应 计算工具，如果成功的话，可能会对表位驱动的疫苗设计区域产生重大影响， 包括个性化的癌症疫苗，以及与过敏相关表位的鉴定。