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Published on: June 5, 2021
Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep
Bilal Ahmed Abbasi1, Devansh Saraf1, Trapti Sharma1
1Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, India.
Abstract:
An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September 2021. This study is focused on finding potential vaccine candidates and designing an in-silico subunit multi-epitope vaccine candidates using a unique computational pipeline, integrating reverse vaccinology, molecular docking and simulation methods. A protein named spike protein of SARS-CoV-2 with the GenBank ID QHD43416.1 was shortlisted as a potential vaccine candidate and was examined for presence of B-cell and T-cell epitopes. We also investigated antigenicity and interaction with distinct polymorphic alleles of the epitopes. High ranking epitopes such as DLCFTNVY (B cell epitope), KIADYNKL (MHC Class-I) and VKNKCVNFN (MHC class-II) were shortlisted for subsequent analysis. Digestion analysis verified the safety and stability of the shortlisted peptides. Docking study reported a strong binding of proposed peptides with HLA-A*02 and HLA-B7 alleles. We used standard methods to construct vaccine model and this construct was evaluated further for its antigenicity, physicochemical properties, 2D and 3D structure prediction and validation. Further, molecular docking followed by molecular dynamics simulation was performed to evaluate the binding affinity and stability of TLR-4 and vaccine complex. Finally, the vaccine construct was reverse transcribed and adapted for E. coli strain K 12 prior to the insertion within the pET-28-a (+) vector for determining translational and microbial expression followed by conservancy analysis. Also, six multi-epitope subunit vaccines were constructed using different strategies containing immunogenic epitopes, appropriate adjuvants and linker sequences. We propose that our vaccine constructs can be used for downstream investigations using in-vitro and in-vivo studies to design effective and safe vaccine against different strains of COVID-19.
Insights
This study designed novel multi-epitope subunit vaccines against COVID-19 using computational methods. The proposed vaccine constructs show promise for further in-vitro and in-vivo studies to combat SARS-CoV-2 strains.
Area of Science:
- Computational vaccinology
- Immunoinformatics
- Molecular modeling
Background:
- COVID-19, caused by SARS-CoV-2, has led to a global health crisis with millions of deaths.
- Development of effective vaccines is crucial to control the pandemic and prevent future outbreaks.
Purpose of the Study:
- To design and computationally evaluate novel subunit multi-epitope vaccine candidates against SARS-CoV-2.
- To identify potential B-cell and T-cell epitopes from the spike protein for vaccine development.
Main Methods:
- Integrated reverse vaccinology, molecular docking, and molecular dynamics simulations.
- Identified and analyzed B-cell and T-cell epitopes, assessed antigenicity, and predicted binding affinities.
- Constructed and validated multi-epitope vaccine models, including expression analysis in E. coli.
Main Results:
- Shortlisted high-ranking epitopes (e.g., DLCFTNVY, KIADYNKL, VKNKCVNFN) with validated safety and stability.
- Demonstrated strong binding of proposed peptides with specific HLA alleles (HLA-A*02, HLA-B7).
- Successfully constructed six multi-epitope subunit vaccine candidates with favorable predicted properties.
Conclusions:
- The computationally designed vaccine constructs are promising candidates for further experimental validation.
- These multi-epitope vaccines offer a potential strategy for developing effective and safe COVID-19 vaccines against various strains.
