Unknown

Dataset Information

0

Reverse vaccinology assisted designing of multiepitope-based subunit vaccine against SARS-CoV-2.


ABSTRACT:

Background

Coronavirus disease 2019 (COVID-19) linked with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause severe illness and life-threatening pneumonia in humans. The current COVID-19 pandemic demands an effective vaccine to acquire protection against the infection. Therefore, the present study was aimed to design a multiepitope-based subunit vaccine (MESV) against COVID-19.

Methods

Structural proteins (Surface glycoprotein, Envelope protein, and Membrane glycoprotein) of SARS-CoV-2 are responsible for its prime functions. Sequences of proteins were downloaded from GenBank and several immunoinformatics coupled with computational approaches were employed to forecast B- and T- cell epitopes from the SARS-CoV-2 highly antigenic structural proteins to design an effective MESV.

Results

Predicted epitopes suggested high antigenicity, conserveness, substantial interactions with the human leukocyte antigen (HLA) binding alleles, and collective global population coverage of 88.40%. Taken together, 276 amino acids long MESV was designed by connecting 3 cytotoxic T lymphocytes (CTL), 6 helper T lymphocyte (HTL) and 4 B-cell epitopes with suitable adjuvant and linkers. The MESV construct was non-allergenic, stable, and highly antigenic. Molecular docking showed a stable and high binding affinity of MESV with human pathogenic toll-like receptors-3 (TLR3). Furthermore, in silico immune simulation revealed significant immunogenic response of MESV. Finally, MEV codons were optimized for its in silico cloning into the Escherichia coli K-12 system, to ensure its increased expression.

Conclusion

The MESV developed in this study is capable of generating immune response against COVID-19. Therefore, if designed MESV further investigated experimentally, it would be an effective vaccine candidate against SARS-CoV-2 to control and prevent COVID-19.

SUBMITTER: Tahir Ul Qamar M 

PROVIDER: S-EPMC7492789 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC8450176 | biostudies-literature
| S-EPMC8009247 | biostudies-literature
| S-EPMC7350008 | biostudies-literature
| S-EPMC8159972 | biostudies-literature
| S-EPMC7755200 | biostudies-literature
| S-EPMC9005162 | biostudies-literature
| S-EPMC7785481 | biostudies-literature
| S-EPMC9302570 | biostudies-literature
| S-EPMC9166176 | biostudies-literature
| S-EPMC9111088 | biostudies-literature