COVID-19 coronavirus vaccine T cell epitope prediction analysis based on distributions of HLA class I loci (HLA-A, -B, -C) across global populations
Authors:
Open Access
- 11 November 2020
- journal article
- research article
- Published by Taylor & Francis in Human Vaccines & Immunotherapeutics
- Vol. 17 (4) , 1097-1108
Abstract
T cell immunity, such as CD4 and/or CD8 T cell responses, plays a vital role in controlling the virus infection and pathological damage. Several studies have reported SARS-CoV-2 proteins could serve as ideal vaccine candidates against SARS-CoV-2 infection by activating the T cell responses. In the current study, based on the SARS-CoV-2 sequence and distribution of host human leukocyte antigen (HLA), we predicted the possible epitopes for the vaccine against SARS-CoV-2 infections. Firstly, the current study retrieved the SARS-CoV-2 S and N protein sequences from the NCBI Database. Then, using the Immune Epitope Database Analysis Resource, we predicted the CTL epitopes of the SARS-CoV-2 S and N proteins according to worldwide frequency distributions of HLA-A, -B, and -C alleles (>1%). Our results predicted 90 and 106 epitopes of N and S proteins, respectively. Epitope cluster analysis showed 16 and 34 respective clusters of SARS-CoV-2 N and S proteins, which covered 95.91% and 96.14% of the global population, respectively. After epitope conservancy analysis, 8 N protein epitopes and 6 S protein epitopes showed conservancy within two SARS-CoV-2 types. Of these 14 epitopes, 13 could cover SARS coronavirus and Bat SARS-like coronavirus. The remaining epitope (KWPWYIWLGF1211-1220) could cover MERS coronavirus. Finally, the 14-epitope combination could vaccinate 89.60% of all individuals worldwide. Our results propose single or combined CTL epitopes predicted in the current study as candidates for vaccines to effectively control SARS-CoV-2 infection and development.Keywords
Funding Information
- high-level health talents of Yunnan Province (D-201669 and L-201615)
- Yunnan Provincial Science and Technology Department (2019HC0060)
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