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

项目总结 通过蛋白质降解和识别抗原表位来处理抗原是 身体通过区分自我和非我来对抗病原体的能力，比如病毒。因此，在那里 一直在进行大量的研究工作，旨在确定这些过程的结果 病原体使表位驱动的疫苗设计成为可能。在交叉口也引起了极大的兴趣 免疫学和个性化医学在识别主体(宿主)特定表位方面有很大的优势 在治疗过敏症和癌症方面有希望，在这些疾病中，自我和非我之间的区别变成了 模糊的。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，这会引发强大的免疫反应。这是一项非常具有挑战性的任务， 由于不同物种之间的遗传差异造成的不确定性的存在，进一步加剧了这一点 病原体菌株，以及从个体到个体。按照这一逻辑，使用动物也很明显 在评估由表位引起的免疫反应时，模型的预测价值往往有限，因为 模式物种和人类之间的序列差异可能导致显著不同的结果 抗原处理过程中形成的多肽和免疫细胞受体识别的表位的术语。 因此，对能够预测抗原结果的计算工具的需求尚未得到满足 以依赖于宿主的方式进行处理和表位识别，其中模型将两个抗原作为输入 和特定于宿主的基因数据。我们建议在三个相关领域开发计算工具，以 满足这些需求：i)预测通过抗原处理形成的多肽；ii)预测表位 MHC分子和T细胞受体的识别；以及iii)最有可能 引发一种免疫反应。在拟议的工作中，将使用分子建模和机器学习来 开发抗原处理和抗原表位与MHC分子和T细胞受体结合的准确模型。 分子模型将首先使我们能够确定抗原和免疫系统之间的关键相互作用 蛋白质，当结合统计数据时，可以让我们了解突变将如何影响这些 互动。突变影响的统计分析将应用于可公开获得的大型 充分捕捉突变对抗原处理和表位识别的影响的数据集，并将 最终被纳入到机器学习模型中。拟议的概率模型将应用于 用于捕获表位生成和识别中的不确定性的场景驱动方法。我们将提供样品。 基于已知突变流行率分布的潜在抗原和人类序列来衡量 确定的表位将被产生并引发强大的免疫反应的可能性。建议数 如果计算工具成功，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识别。

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.