10.6084/M9.FIGSHARE.14684216.V1
Vikrant Singh Rajput
Vikrant Singh
Rajput
Ritika Sharma
Ritika
Sharma
Technical University of Munich
Anchala Kumari
Anchala
Kumari
Nidhi Vyas
Nidhi
Vyas
Vijay Prajapati
Vijay
Prajapati
Abhinav Grover
Abhinav
Grover
Engineering a multi epitope vaccine against SARS-CoV-2 by exploiting its non structural and structural proteins
<p>SARS-CoV-2, the causative agent behind the ongoing pandemic exhibits an enhanced potential for infection when compared to its related family members- the SARS-CoV and MERS-CoV; which have caused similar disease outbreaks in the past. The severity of the global health burden, increasing mortality rate and the emergent economic crisis urgently demands the development of next generation vaccines. Amongst such emergent next generation vaccines are the multi-epitope subunit vaccines, which hold promise in combating deadly pathogens. In this study we have exploited immunoinformatics applications to delineate a vaccine candidate possessing multiple B and T cells epitopes by utilizing the SARS-CoV-2 non structural and structural proteins. The antigenicity potential, safety, structural stability and the production feasibility of the designed construct was evaluated computationally. Furthermore, due to the known role of human TLR-3 immune receptor in viral sensing, which facilitates host cells activation for an immune response, the vaccine construct was examined for its binding efficiency using molecular docking and molecular dynamics simulation studies, which resulted in strong and stable interactions. Finally, the immune simulation studies suggested an effective immune response on vaccine administration. Overall, the immunoinformatics analysis advocates that the proposed vaccine candidate is safe and immunogenic and therefore can be pushed as a lead for <i>in vitro</i> and <i>in vivo</i> investigations.</p> <p>Communicated by Ramaswamy H. Sarma</p>
Medicine
Biotechnology
Immunology
69999 Biological Sciences not elsewhere classified
80699 Information Systems not elsewhere classified
110309 Infectious Diseases
Computational Biology
Taylor & Francis
2021
2021-05-27
2022-12-28
Journal contribution
1939317 Bytes
10.1080/07391102.2021.1924265
10.6084/m9.figshare.14684216
CC BY 4.0