RAPID: Immunogenicity of SARS-CoV2 to Human T Cells

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Pandemics caused by infectious pathogens have plagued humanity since antiquity. The Coronavirus Disease 2019 (COVID-19) caused by the SARS-CoV-2 virus is currently spreading across the world rapidly, including in the United States, with major adverse impact on health and the economy. The SARSCoV-2 outbreak has led to several urgent efforts to develop vaccines that may offer protection against this virus. It is unknown as to whether the current approaches being pursued will elicit protective immune responses in humans. While vaccines have been very effective against many pathogens, the empirical methods for vaccine development pioneered by Pasteur and Jenner over two centuries ago have failed to produce effective vaccines against Human Immune Deficiency Virus, Malaria, Tuberculosis, and many other pathogens. Therefore, rational design of vaccines based on a mechanistic understanding of the pertinent virology and immunology is being pursued, and these efforts include work that is rooted in statistical physics. SARSCoV-2 is phylogenetically most similar to SARS-CoV. This project will use a machine learning approach to understand how the SARS-CoV-2 virus interacts with the immune T cells. This work will directly impact the design of SARS-CoV-2 vaccines and vaccines against future endemic-causing pathogens.Analyses of patients who have recovered from SARS-CoV shows that antibody responses are not prevalent a few years later, but memory T cell responses are durable and may offer long-term protection. The main questions addressed by this project are 1. Will the SARS-CoV peptides targeted by human T cells that are mutated in SARS-CoV-2 still elicit human T cell responses - i.e. are they immunogenic? 2: Are the 102 peptides identified by host major histocompatibility molecules binding assays alone that are common between SARS-CoV and SARS-CoV-2 immunogenic in humans? If not, they are irrelevant from vaccine design perspective. The goal of the work proposed here is to take a physics-based machine learning approach to determine the immunogenicity of SARS-CoV-2 proteins to human T cell responses.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.