Designing of Epitope-Based Vaccine from the Conserved Region of the Spike Glycoprotein of SARS-CoV-2
Agarwal, V.; Tiwari, A.; Varadwaj, P. K.
Show abstract
The emergence of COVID-19 as a pandemic with a high morbidity rate is posing serious global concern. There is an urgent need to design a suitable therapy or vaccine that could fight against SARS-CoV-2 infection. As spike glycoprotein of SARS-CoV-2 plays a crucial role in receptor binding and membrane fusion inside the host, it could be a suitable target for designing of an epitope-based vaccine. SARS-CoV-2 is an RNA virus and thus has a property to mutate. So, a conserved peptide region of spike glycoprotein was used for predicting suitable B cell and T cell epitopes. 4 T cell epitopes were selected based on stability, antigenicity, allergenicity and toxicity. Further, MHC-I were found from the immune database that could best interact with the selected epitopes. Population coverage analysis was also done to check the presence of identified MHC-I, in the human population of the affected countries. The T cell epitope that binds with the respective MHC-I with highest affinity was chosen. Molecular dynamic simulation results show that the epitope is well selected. This is an in-silico based study that predicts a novel T cell epitope from the conserved spike glycoprotein that could act as a target for designing of the epitope-based vaccine. Further, B cell epitopes have also been found but the main work focuses on T cell epitope as the immunity generated by it is long lasting as compared to B cell epitope.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Contriving a chimeric polyvalent vaccine to prevent infections caused by Herpes Simplex Virus (Type-1 and Type-2): an exploratory immunoinformatic approach 97%
- A program to automate the discovery of drugs for West Nile and Dengue virus -- programmatic screening of over a billion compounds on PubChem, generation of drug leads and automated In Silico modelling 96%
- In silico design of Multi-epitope-based peptide vaccine against SARS-CoV-2 using its spike protein 96%
Similar papers in this journal
- Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches 99%
- Immuno-informatics Design of a Multimeric Epitope Peptide Based Vaccine Targeting SARS-CoV-2 Spike Glycoprotein 98%
- iBRAB: in silico based-designed Broad-spectrum Fab against H1N1 Influenza A Virus 97%
Similar papers in this journal
- A Multiple Peptides Vaccine against nCOVID-19 Designed from the Nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the Immunoinformatics Approach 98%
- Epitope-Based Peptide Vaccine against Bombali Ebolavirus Viral Protein 40: An Immunoinformatics Combined with Molecular Docking Studies 98%
- Zika viral proteome analysis reveals an epitope cluster within NS3 helicase as a potential vaccine candidate: an in silico study 97%
Similar papers in this journal
- Design of Epitope Based Peptide Vaccine Against Pseudomonas Aeruginosa Fructose Bisphosphate Aldolase Protein using Immunoinformatics 99%
- Attenuated Subcomponent Vaccine Design Targeting the SARS-CoV-2 Nucleocapsid Phosphoprotein RNA Binding Domain: In silico analysis 98%
- Epitope-based peptide vaccine against glycoprotein G of Nipah henipavirus using immunoinformatics approaches 98%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.