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Bioinformatics analysis of epitope-based vaccine design against the novel SARS-CoV-2.
Infect Dis Poverty. 2020 Jul 10; 9(1):88.ID

Abstract

BACKGROUND

An outbreak of infection caused by SARS-CoV-2 recently has brought a great challenge to public health. Rapid identification of immune epitopes would be an efficient way to screen the candidates for vaccine development at the time of pandemic. This study aimed to predict the protective epitopes with bioinformatics methods and resources for vaccine development.

METHODS

The genome sequence and protein sequences of SARS-CoV-2 were retrieved from the National Center for Biotechnology Information (NCBI) database. ABCpred and BepiPred servers were utilized for sequential B-cell epitope analysis. Discontinuous B-cell epitopes were predicted via DiscoTope 2.0 program. IEDB server was utilized for HLA-1 and HLA-2 binding peptides computation. Surface accessibility, antigenicity, and other important features of forecasted epitopes were characterized for immunogen potential evaluation.

RESULTS

A total of 63 sequential B-cell epitopes on spike protein were predicted and 4 peptides (Spike315-324, Spike333-338, Spike648-663, Spike1064-1079) exhibited high antigenicity score and good surface accessibility. Ten residues within spike protein (Gly496, Glu498, Pro499, Thr500, Leu1141, Gln1142, Pro1143, Glu1144, Leu1145, Asp1146) are forecasted as components of discontinuous B-cell epitopes. The bioinformatics analysis of HLA binding peptides within nucleocapsid protein produced 81 and 64 peptides being able to bind MHC class I and MHC class II molecules respectively. The peptides (Nucleocapsid66-75, Nucleocapsid104-112) were predicted to bind a wide spectrum of both HLA-1 and HLA-2 molecules.

CONCLUSIONS

B-cell epitopes on spike protein and T-cell epitopes within nucleocapsid protein were identified and recommended for developing a protective vaccine against SARS-CoV-2.

Authors+Show Affiliations

Department of Metabolism & Endocrinology, Metabolic Syndrome Research Center, Key Laboratory of Diabetes Immunology, Ministry of Education, National Clinical Research Center for Metabolic Disease, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China. chenhongzhi2013@csu.edu.cn.Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.Hunan Institute of Parasite Disease, WHO Collaborating Center For Research and Control on Schistosomiasis in Lake Region, Yueyang, 414000, Hunan, China.Hunan Institute of Parasite Disease, WHO Collaborating Center For Research and Control on Schistosomiasis in Lake Region, Yueyang, 414000, Hunan, China.Department of Forensic Medicine Science, Xiangya School of Basic Medicine, Central South University, Changsha, 410013, Hunan, China. changyunfeng880@163.com.Department of Parasitology, Xiangya School of Basic Medicine, Central South University, Changsha, 410013, Hunan, China. wxspring@126.com.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32741372

Citation

Chen, Hong-Zhi, et al. "Bioinformatics Analysis of Epitope-based Vaccine Design Against the Novel SARS-CoV-2." Infectious Diseases of Poverty, vol. 9, no. 1, 2020, p. 88.
Chen HZ, Tang LL, Yu XL, et al. Bioinformatics analysis of epitope-based vaccine design against the novel SARS-CoV-2. Infect Dis Poverty. 2020;9(1):88.
Chen, H. Z., Tang, L. L., Yu, X. L., Zhou, J., Chang, Y. F., & Wu, X. (2020). Bioinformatics analysis of epitope-based vaccine design against the novel SARS-CoV-2. Infectious Diseases of Poverty, 9(1), 88. https://doi.org/10.1186/s40249-020-00713-3
Chen HZ, et al. Bioinformatics Analysis of Epitope-based Vaccine Design Against the Novel SARS-CoV-2. Infect Dis Poverty. 2020 Jul 10;9(1):88. PubMed PMID: 32741372.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Bioinformatics analysis of epitope-based vaccine design against the novel SARS-CoV-2. AU - Chen,Hong-Zhi, AU - Tang,Ling-Li, AU - Yu,Xin-Ling, AU - Zhou,Jie, AU - Chang,Yun-Feng, AU - Wu,Xiang, Y1 - 2020/07/10/ PY - 2020/04/22/received PY - 2020/07/02/accepted PY - 2020/8/4/entrez PY - 2020/8/4/pubmed PY - 2020/8/14/medline KW - Bioinformatics KW - Epitope KW - SARS-CoV-2 KW - Vaccine SP - 88 EP - 88 JF - Infectious diseases of poverty JO - Infect Dis Poverty VL - 9 IS - 1 N2 - BACKGROUND: An outbreak of infection caused by SARS-CoV-2 recently has brought a great challenge to public health. Rapid identification of immune epitopes would be an efficient way to screen the candidates for vaccine development at the time of pandemic. This study aimed to predict the protective epitopes with bioinformatics methods and resources for vaccine development. METHODS: The genome sequence and protein sequences of SARS-CoV-2 were retrieved from the National Center for Biotechnology Information (NCBI) database. ABCpred and BepiPred servers were utilized for sequential B-cell epitope analysis. Discontinuous B-cell epitopes were predicted via DiscoTope 2.0 program. IEDB server was utilized for HLA-1 and HLA-2 binding peptides computation. Surface accessibility, antigenicity, and other important features of forecasted epitopes were characterized for immunogen potential evaluation. RESULTS: A total of 63 sequential B-cell epitopes on spike protein were predicted and 4 peptides (Spike315-324, Spike333-338, Spike648-663, Spike1064-1079) exhibited high antigenicity score and good surface accessibility. Ten residues within spike protein (Gly496, Glu498, Pro499, Thr500, Leu1141, Gln1142, Pro1143, Glu1144, Leu1145, Asp1146) are forecasted as components of discontinuous B-cell epitopes. The bioinformatics analysis of HLA binding peptides within nucleocapsid protein produced 81 and 64 peptides being able to bind MHC class I and MHC class II molecules respectively. The peptides (Nucleocapsid66-75, Nucleocapsid104-112) were predicted to bind a wide spectrum of both HLA-1 and HLA-2 molecules. CONCLUSIONS: B-cell epitopes on spike protein and T-cell epitopes within nucleocapsid protein were identified and recommended for developing a protective vaccine against SARS-CoV-2. SN - 2049-9957 UR - https://www.unboundmedicine.com/medline/citation/32741372/Bioinformatics_analysis_of_epitope_based_vaccine_design_against_the_novel_SARS_CoV_2_ L2 - https://idpjournal.biomedcentral.com/articles/10.1186/s40249-020-00713-3 DB - PRIME DP - Unbound Medicine ER -