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• W3109798411 abstract "COVID-19 is a new viral emergent disease caused by a novel strain of coronavirus. This virus has caused a huge problem in the world as millions of people are affected by this disease. We aimed at designing a peptide vaccine for COVID-19 particularly for the envelope protein using computational methods to predict epitopes inducing the immune system. The envelope protein sequence of SARS-CoV-2 has been retrieved from the NCBI database. The bioinformatics analysis was carried out by using the Immune Epitope Database (IEDB) to predict B- and T-cell epitopes. The predicted HTL and CTL epitopes were docked with HLA alleles and binding energies were evaluated. The allergenicity of predicted epitopes was analyzed, the conservancy analysis was performed, and the population coverage was determined throughout the world. Some overlapped CTL, HTL, and B-cell epitopes were suggested to become a universal candidate for peptide-based vaccine against COVID-19. This vaccine peptide could simultaneously elicit humoral and cell-mediated immune responses. We hope to confirm our findings by adding complementary steps of both in vitro and in vivo studies to support this new universal predicted candidate." @default.
• W3109798411 created "2020-12-07" @default.
• W3109798411 creator A5014205561 @default.
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• W3109798411 date "2020-11-25" @default.
• W3109798411 modified "2023-09-25" @default.
• W3109798411 title "An Immunoinformatics Study to Predict Epitopes in the Envelope Protein of SARS-CoV-2" @default.
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• W3109798411 doi "https://doi.org/10.1155/2020/7079356" @default.
• W3109798411 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7686850" @default.
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