ABSTRACT

The extensive blowout of COVID-19, an infectious disease caused by SARS-CoV-2, has resulted in millions of demises and has wreaked havoc on social, economic, and political systems all over the biosphere. Vaccines are despairingly needed to prevent the spread of this disease in view of the fact that there is currently no viable medical treatment. In this article, we offer an in silico deep learning approach for possibility of multi-epitope vaccines prediction and design. The virus is comparable to SARS-CoV and MERS-CoV, according to genome sequence research. Despite this, antiviral medications used to treat previous SARS-CoV and MERS- CoV infections have been found to be ineffective in controlling SARS-CoV-2. The need to discover a vaccine solution grows as the rate of infection and mortality from COVID-19 rises. The immunogenicity, stability, safety, and vaccination potential of many epitope targets of SARSCoV-2's spike (S) protein are being explored. The vaccine designs described in this article show promise as vaccine candidates, but more in vitro and in vivo testing is required. Keywords: SARS-CoV, MERS-CoV, Immunogenicity, Epitope targets, In-silico
Publication date: 01/03/2023

https://ijbpas.com/pdf/2023/March/MS_IJBPAS_2023_6910.pdf

Download PDF
https://doi.org/10.31032/IJBPAS/2023/12.3.6910