Abstract

Background

Coronavirus disease 19 (COVID-19) first appeared in the city of Wuhan, in the Hubei province of China. Since its emergence, the COVID-19-causing virus, SARS-CoV-2, has been rapidly transmitted around the globe, overwhelming the medical care systems in many countries and leading to more than 3.3 million deaths. Identification of immunological epitopes on the virus would be highly useful for the development of diagnostic tools and vaccines that will be critical to limiting further spread of COVID-19.

Methods

To find disease-specific B-cell epitopes that correspond to or mimic natural epitopes, we used phage display technology to determine the targets of specific antibodies present in the sera of immune-responsive COVID-19 patients. Enzyme-linked immunosorbent assays were further applied to assess competitive antibody binding and serological detection. VaxiJen, BepiPred-2.0 and DiscoTope 2.0 were utilized for B-cell epitope prediction. PyMOL was used for protein structural analysis.

Results

36 enriched peptides were identified by biopanning with antibodies from two COVID-19 patients; the peptides 4 motifs with consensus residues corresponding to two potential B-cell epitopes on SARS-CoV-2 viral proteins. The putative epitopes and hit peptides were then synthesized for validation by competitive antibody binding and serological detection.

Conclusions

The identified B-cell epitopes on SARS-CoV-2 may aid investigations into COVID-19 pathogenesis and facilitate the development of epitope-based serological diagnostics and vaccines.

Details

Title
Identification of COVID-19 B-cell epitopes with phage-displayed peptide library
Author
Jing-You, Guo; I-Ju, Liu; Lin, Hsiu-Ting; Mei-Jung, Wang; Yu-Ling, Chang; Shin-Chang, Lin; Mei-Ying Liao; Wei-Chia, Hsu; Yi-Ling, Lin; Liao, James C; Han-Chung, Wu  VIAFID ORCID Logo 
Pages
1-13
Section
Research
Publication year
2021
Publication date
2021
Publisher
BioMed Central
ISSN
10217770
e-ISSN
14230127
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2543503708
Copyright
© 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.