Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2 : Immunoinformatics and in silico approaches
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
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| Enthalten in: |
PloS one - 15(2020), 12 vom: 02., Seite e0244176 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tahir Ul Qamar, Muhammad [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 Vaccines |
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| Anmerkungen: |
Date Completed 08.01.2021 Date Revised 04.11.2023 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pone.0244176 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM319163938 |
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| 245 | 1 | 0 | |a Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2 |b Immunoinformatics and in silico approaches |
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| 500 | |a Date Revised 04.11.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
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| 700 | 1 | |a Rehman, Abdur |e verfasserin |4 aut | |
| 700 | 1 | |a Tusleem, Kishver |e verfasserin |4 aut | |
| 700 | 1 | |a Ashfaq, Usman Ali |e verfasserin |4 aut | |
| 700 | 1 | |a Qasim, Muhammad |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Xitong |e verfasserin |4 aut | |
| 700 | 1 | |a Fatima, Israr |e verfasserin |4 aut | |
| 700 | 1 | |a Shahid, Farah |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Ling-Ling |e verfasserin |4 aut | |
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