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|a Gao, Ang
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|a Massachusetts Institute of Technology. Department of Mechanical Engineering
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|a Massachusetts Institute of Technology. Department of Chemical Engineering
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|a Massachusetts Institute of Technology. Department of Physics
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|a Massachusetts Institute of Technology. Department of Chemistry
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|a Massachusetts Institute of Technology. Department of Biological Engineering
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|a Amitai, Assaf
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|a Doelger, Julia
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|a Chakraborty, Arup K
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|a Julg, Boris D.
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|a Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-COV-2
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|b Elsevier BV,
|c 2021-04-01T17:22:23Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/130336
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|a We describe a physics-based learning model for predicting the immunogenicity of Cytotoxic-T-Lymphocyte (CTL) epitopes derived from diverse pathogens including SARS-CoV-2. The model was trained and optimized on the relative immunodominance of CTL epitopes in Human Immunodeficiency Virus infection. Its accuracy was tested against experimental data from COVID-19 patients. Our model predicts that only some SARS-CoV-2 epitopes predicted to bind to HLA molecules are immunogenic. The immunogenic CTL epitopes across all SARS-CoV-2 proteins are predicted to provide broad population coverage, but those from the SARS-CoV-2 spike protein alone are unlikely to do so. Our model also predicts that several immunogenic SARS-CoV-2 CTL epitopes are identical to seasonal coronaviruses circulating in the population and such cross-reactive CD8+ T cells can indeed be detected in prepandemic blood donors, suggesting that some level of CTL immunity against COVID-19 may be present in some individuals prior to SARS-CoV-2 infection.
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|a National Science Foundation (U.S.) (Grant PHY-2026995)
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|a Frederick National Laboratory for Cancer Research (Contract HHSN261200800001E)
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|a National Institutes of Health (U.S.) (Grant AI138790)
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|a en
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|a Article
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|t 10.1016/j.isci.2021.102311
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|t iScience
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