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The multi-epitope vaccine prediction to combat Pandemic SARS-CoV-2, an immunoinformatic approach
| Content Provider | Research Square |
|---|---|
| Author | Kibria, K. M. Kaderi Islam, Md Shaid Bin Ullah, Hedayet Miah, Mojnu |
| Abstract | Novel coronavirus (SARS-CoV-2) leads to coronavirus disease 19 (COVID-19) recently declared as a pandemic for its outbreak within almost 190 countries worldwide. No effective drugs and/or vaccines authenticated against this rapidly spreading virus till now. This study aims to establish an efficient multi-epitope vaccine that could elicit both T-cell and B-cell responses sufficient to recognize confirmed surface proteins of the virus. The sequences of the viral surface proteins, e.g. envelope protein (E), membrane glycoprotein (M), and S1 and S2 domain of spike surface glycoprotein (S) collected from the NCBI database. We adopted an immunoinformatic strategy to identify the immunogenic region of the proteins and assessed their affinity with MHC class-I and MHC class-II by various bioinformatics tools. Top epitopes have been selected and assessed for population coverage and conservancy among 180 SARS-CoV-2 genomes. Along with the above analyses, and results of Antigenicity, Allergenicity, and transmembrane location prediction, we selected top epitopes from these four proteins. The epitopes were assembled by the AAY linker to form a multi-epitope vaccine is 70 aa long, can be synthesized commercially. This should be processed by Antigen-presenting cells; consequently, the surface proteins might be recognized by the helper and cytotoxic T-cells as well as by B-cells. We also assessed the structural and various physicochemical properties of the novel chimeric peptide for its suitability as a multi-epitope vaccine. This in-silico study leads to a rationally designed potential vaccine candidate that could be assessed by wet-lab experiments driving towards efficient combat of the novel coronavirus outbreak.Novel coronavirus (SARS-CoV-2) leads to coronavirus disease 19 (COVID-19) recently declared as a pandemic for its outbreak within almost 190 countries worldwide. No effective drugs and/or vaccines authenticated against this rapidly spreading virus till now. This study aims to establish an efficient multi-epitope vaccine that could elicit both T-cell and B-cell responses sufficient to recognize confirmed surface proteins of the virus. The sequences of the viral surface proteins, e.g. envelope protein (E), membrane glycoprotein (M), and S1 and S2 domain of spike surface glycoprotein (S) collected from the NCBI database. We adopted an immunoinformatic strategy to identify the immunogenic region of the proteins and assessed their affinity with MHC class-I and MHC class-II by various bioinformatics tools. Top epitopes have been selected and assessed for population coverage and conservancy among 180 SARS-CoV-2 genomes. Along with the above analyses, and results of Antigenicity, Allergenicity, and transmembrane location prediction, we selected top epitopes from these four proteins. The epitopes were assembled by the AAY linker to form a multi-epitope vaccine is 70 aa long, can be synthesized commercially. This should be processed by Antigen-presenting cells; consequently, the surface proteins might be recognized by the helper and cytotoxic T-cells as well as by B-cells. We also assessed the structural and various physicochemical properties of the novel chimeric peptide for its suitability as a multi-epitope vaccine. This in-silico study leads to a rationally designed potential vaccine candidate that could be assessed by wet-lab experiments driving towards efficient combat of the novel coronavirus outbreak. |
| Related Links | https://assets.researchsquare.com/files/rs-21853/v1/manuscript.pdf |
| File Format | HTM / HTML |
| DOI | 10.21203/rs.3.rs-21853/v1 |
| Journal | Nature Research |
| Language | English |
| Publisher Date | 2020-04-07 |
| Access Restriction | Open |
| Subject Keyword | COVID-19 SARS-CoV-2 Epitope Envelope protein Spike surface glycoprotein Vaccine Membrane glycoprotein Immunoinformatics |
| Content Type | Text |
| Resource Type | Article Preprint |
