Using in-silica Analysis and Reverse Vaccinology Approach for COVID-19 Vaccine Development
Background: The recent pandemic of COVID19 that has struck the world is yet to be battled by a potential cure. Countless lives have been claimed due to the existing pandemic and the societal normalcy has been damaged permanently. As a result, it becomes crucial for academic researchers in the field of bioinformatics to combat the existing pandemic. Materials and Methods: The study involved collecting the virulent strain sequence of SARS-nCoV19 for the country USA against human host through publically available bioinformatics databases. Using in-silica analysis, reverse vaccinology, and 3-D modelling, two leader proteins were identified to be potential vaccine candidates for development of a multi-epitope drug. Results: It was revealed that the two leader proteins ORF1ab MT326102 and MT326715 had the highest extinction coefficient and the lowest score on the GRAVY. Along with the given parameters, these leader proteins were highly stable and were also antigenic in nature. The two selected epitopes were then docked against their respective alleles to obtain the global energy scores, which was the lowest of all possible pairs. Conclusion: The epitopes which displayed the lowest global energy score on docking with the alleles were selected and proposed as successful and potential vaccine candidates for multi-epitope vaccine development.
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