EAGER: Computationally Predicting and Characterizing the Immune Response to Viral Infections

严重急性呼吸综合征冠状病毒2(SARS-CoV-2)等病原体对不同人的影响不同。一个人是否会做出强烈的反应，至少部分取决于他们的基因。特定的基因编码细胞表面的蛋白质，将病毒蛋白质片段呈现给免疫系统。杀伤T细胞识别这些片段并杀死受感染的细胞。对SARS-CoV-2的免疫反应取决于病毒蛋白片段是否结合到这些细胞表面蛋白的凹槽中--就像钥匙打开锁一样。分子生物学是很好理解的。蛋白质片段是否结合是一个3D结构和简单的原子力计算的问题。与SARS-CoV-2相关的全套蛋白质已于3月份发布，因此可以获得必要的数据。这个项目将通过纯粹的计算手段预测，是否所有病毒蛋白片段、细胞表面蛋白的所有常见变体都会发生这种结合--所有打开所有类型锁的钥匙也是如此。如果成功，这种计算能力将对科学理解大流行产生革命性影响，未来可以为其他大流行--由病毒或细菌引起的大流行--部署相同的计算基础设施。总的来说，它也可能在描述人类免疫系统及其对病原体的反应方面产生变革性的作用。用技术术语来说，该项目的目标是通过计算手段预测来自SARS-CoV-2的哪些多肽将与美国人口中常见的MHC-I分子的每个等位基因变体结合。人类白细胞抗原(HL A)分型可用于建立个体MHC-I分子的等位基因变异。通过全人群分型，该项目开发的工具将预测人群中的哪些人最有可能对病毒产生强大的抗病毒免疫反应，因为他们的MHC-I等位基因。将首先使用机器学习算法，对MHC-I分子的所有常见等位基因变体进行免疫肽组分析。下一步，将使用定制开发的原子级模拟软件，部署在图形处理单元上，进行免疫肽组分析。该项目将提供该工具集的公共实现。研究结果将在明尼苏达大学主办的网站上迅速传播。前端将利用现代软件基础设施进行数据分析和可视化。后端将由MySQL数据库组成，直接链接到计算引擎，在分布式平台上运行。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Pathogens such as the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) affect different people differently. Whether an individual mounts a strong response or not depends, at least in part, on their genes. Specific genes code for the proteins on the surface of cells that present viral protein fragments to the immune system. Killer T cells recognize these fragments and kill the infected cells. The immune response to SARS-CoV-2 hinges on whether the viral protein fragments bind into a groove in these cell-surface proteins -- like a key into a lock. The molecular biology is well understood. Whether a protein fragment binds or not is a question of 3D structure and simple atomic force calculations. The full set of proteins associated with SARS-CoV-2 was published in March, so the requisite data is available. This project will predict, through purely computational means, whether such binding happens for all viral protein fragments, for all common variants of the cell surface proteins -- so for all keys into all types of locks. This computational ability will be transformative for a scientific understanding of the pandemic If successful, the same computational infrastructure could be deployed in the future for other pandemics -- those caused by viruses or by bacteria. It could also be transformative in characterizing the human immune system, in general, and its response to pathogens. In technical terms, the goal of the project is to predict, through computational means, which peptides derived from SARS- CoV-2 will bind to each allelic variant of MHC-I molecule commonly found in the U.S. population. Human leukocyte antigen (HLA) typing can be performed to establish the allelic variants of MHC-I molecules of individuals. With population-wide typing, the tools developed by this project will predict which individuals in a population are most likely to mount a strong antiviral immune response to the virus, given their MHC-I alleles. Immunopeptidome profiling will be performed of all common allelic variants of MHC-I molecules, first using machine-learning algorithms. Next immunopeptidome profiling will be performed using custom-developed atomic-level simulation software, deployed on graphical processing units.The project will provide a public implementation of the tool set. The results of the research will be promptly disseminated on a website hosted by the University of Minnesota. The front-end will exploit modern software infrastructure for data analytics and visualization. The back-end will consist of a MySQL database, directly linked to the computational engine, running on a distributed platform.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.