Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-CoV-2.
Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-CoV-2.
复制标题
DOI:
10.1016/j.isci.2021.102311
复制
发表时间:
2021-04-23
期刊:
影响因子:
5.8
通讯作者:
Julg B
中科院分区:
文献类型:
--
作者:
Gao A;Chen Z;Amitai A;Doelger J;Mallajosyula V;Sundquist E;Pereyra Segal F;Carrington M;Davis MM;Streeck H;Chakraborty AK;Julg B
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 patients with COVID-19. 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 before SARS-CoV-2 infection. A physics-based learning model to predict CTL epitope immunogenicity across viruses Trained on relative CTL epitope immunodominance in HIV and applied to SARS-CoV-2 Only a fraction of SARS-CoV-2 peptides that bind to HLA molecules is immunogenic Immunogenic SARS-CoV-2 epitopes identical to seasonal coronaviruses were identified Immunology; Immune Respons; In Silico Biology; Artificial Intelligence
登录
查看更多内容
DOI:
10.4049/jimmunol.1700893
发表时间:
2017-11-01
期刊:
Journal of immunology (Baltimore, Md. : 1950)
影响因子:
--
作者:
Jurtz V;Paul S;Andreatta M;Marcatili P;Peters B;Nielsen M
通讯作者:
Nielsen M
影响因子:
4.3
作者:
Calis JJ;Maybeno M;Greenbaum JA;Weiskopf D;De Silva AD;Sette A;Keşmir C;Peters B
通讯作者:
Peters B
影响因子:
32.4
作者:
Ferguson AL;Mann JK;Omarjee S;Ndung'u T;Walker BD;Chakraborty AK
通讯作者:
Chakraborty AK
影响因子:
64.8
作者:
Daniels, Mark A.;Teixeiro, Emma;Palmer, Ed
通讯作者:
Palmer, Ed
影响因子:
3.7
作者:
Hayton EJ;Rose A;Ibrahimsa U;Del Sorbo M;Capone S;Crook A;Black AP;Dorrell L;Hanke T
通讯作者:
Hanke T