COVID-19 coronavirus vaccine T cell epitope prediction analysis based on distributions of HLA class I loci (HLA-A, -B, -C) across global populations

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 (KWPWYIWLGF_1211-1220) 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:

• SARS-CoV-2
• Human Leukocyte Antigen (HLA)
• T cell epitopes
• population coverage
• vaccine

Author contributions

Conceived and designed the experiments: Li Shi and Yufeng Yao. Performed the HLA data analysis: Sun Ming, Shuying Dai, Le Sun. Performed the immunoinformatic analysis: Yina Cun, Chuanyin Li, Lei Shi. Wrote the paper: Li Shi and Yufeng Yao.

Additional information

Funding

This work was supported by grant from Special Funds for high-level health talents of Yunnan Province (D-201669 and L-201615) and the Yunnan Provincial Science and Technology Department (2019HC0060). The funders had no role in the design of the study, data collection and analysis, decision to publish, or preparation of the manuscript.