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Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches.
PLoS One. 2020; 15(12):e0244176.Plos

Abstract

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-COV-2) is a significant threat to global health security. Till date, no completely effective drug or vaccine is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven highly antigenic proteins of SARS-COV-2 were selected as targets and different epitopes (B-cell and T-cell) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.93% coverage of the world's population. Hence, 505 amino acids long MEV was designed by connecting 16 MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. MEV construct was non-allergenic, antigenic, stable and flexible. Furthermore, molecular docking followed by molecular dynamics (MD) simulation analyses, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Finally, MEV codons were optimized for its in silico cloning into Escherichia coli K-12 system, to ensure its increased expression. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated.

Authors+Show Affiliations

College of Life Science and Technology, Guangxi University, Nanning, P. R. China.Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan.Fatima Jinnah Medical University, Lahore, Pakistan.Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan.Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan.Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan.Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan.College of Life Science and Technology, Guangxi University, Nanning, P. R. China. Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P. R. China.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

33351863

Citation

Tahir Ul Qamar, Muhammad, et al. "Designing of a Next Generation Multiepitope Based Vaccine (MEV) Against SARS-COV-2: Immunoinformatics and in Silico Approaches." PloS One, vol. 15, no. 12, 2020, pp. e0244176.
Tahir Ul Qamar M, Rehman A, Tusleem K, et al. Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches. PLoS One. 2020;15(12):e0244176.
Tahir Ul Qamar, M., Rehman, A., Tusleem, K., Ashfaq, U. A., Qasim, M., Zhu, X., Fatima, I., Shahid, F., & Chen, L. L. (2020). Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches. PloS One, 15(12), e0244176. https://doi.org/10.1371/journal.pone.0244176
Tahir Ul Qamar M, et al. Designing of a Next Generation Multiepitope Based Vaccine (MEV) Against SARS-COV-2: Immunoinformatics and in Silico Approaches. PLoS One. 2020;15(12):e0244176. PubMed PMID: 33351863.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches. AU - Tahir Ul Qamar,Muhammad, AU - Rehman,Abdur, AU - Tusleem,Kishver, AU - Ashfaq,Usman Ali, AU - Qasim,Muhammad, AU - Zhu,Xitong, AU - Fatima,Israr, AU - Shahid,Farah, AU - Chen,Ling-Ling, Y1 - 2020/12/22/ PY - 2020/10/04/received PY - 2020/12/04/accepted PY - 2020/12/22/entrez PY - 2020/12/23/pubmed PY - 2021/1/9/medline SP - e0244176 EP - e0244176 JF - PloS one JO - PLoS One VL - 15 IS - 12 N2 - Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-COV-2) is a significant threat to global health security. Till date, no completely effective drug or vaccine is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven highly antigenic proteins of SARS-COV-2 were selected as targets and different epitopes (B-cell and T-cell) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.93% coverage of the world's population. Hence, 505 amino acids long MEV was designed by connecting 16 MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. MEV construct was non-allergenic, antigenic, stable and flexible. Furthermore, molecular docking followed by molecular dynamics (MD) simulation analyses, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Finally, MEV codons were optimized for its in silico cloning into Escherichia coli K-12 system, to ensure its increased expression. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/33351863/Designing_of_a_next_generation_multiepitope_based_vaccine__MEV__against_SARS_COV_2:_Immunoinformatics_and_in_silico_approaches_ L2 - https://dx.plos.org/10.1371/journal.pone.0244176 DB - PRIME DP - Unbound Medicine ER -