The outbreak of COVID-19 was originated from China, responsible for Several Acute Respiratory Syndrome (SARS). Scientists are forced to develop vaccine and effective drugs to control COVID-19 infection. To develop effective vaccine for SARS – COVID 19, immunoinformatics and computational approaches could helps to design successful vaccine against this biggest danger for humanity. Here we used various in – silico approaches to designed vaccine against COVID-19. To develop vaccine, we target S- protein, expressed on the virus surface plays important role in COVID-19 infection. We identified 12 B-cell, 9 T-helper and 20 Cytotoxic T-cell epitope based on criteria of selection. The predicted epitopes were link simultaneously with GPGPG & AAY linkers. The β-defensin was used as adjuvant, linked with selected epitope by using EAAAK linker. For vaccine construct justification we analysed its immunogenicity, allergenicity and physiochemical properties. Our study revealed that vaccine was non toxic, immunogenic and antigenic in nature and covers 98.6% of world population, important for vaccine effectively . In- silico cloning was used to analyse its expression in vector. Molecular docking was performed to study the interaction of construct with TLR (TLR3, TLR4, and TLR9) molecules. The immune simulation was conducted and conformed that our vaccine constructs can induces both acquired and humoral immunity effectively against COVID-19 at very low concentration, but along with bioinformatics study we need to conduct experiment in laboratory to validate its safety and effectiveness.
The present study provides the first multiepitope vaccine construct using the 3CL hydrolase protein of SARS‐CoV‐2. The coronavirus 3CL hydrolase (Mpro) enzyme is essential for proteolytic maturation of the virus. This study was based on immunoinformatics and structural vaccinology strategies. The design of the multiepitope vaccine was built using helper T‐cell and cytotoxic T‐cell epitopes from the 3CL hydrolase protein along with an adjuvant to enhance immune response; these are joined to each other by short peptide linkers. The vaccine also carries potential B‐cell linear epitope regions, B‐cell discontinuous epitopes, and interferon‐γ‐inducing epitopes. Epitopes of the constructed multiepitope vaccine were found to be antigenic, nonallergic, nontoxic, and covering large human populations worldwide. The vaccine construct was modeled, validated, and refined by different programs to achieve a high‐quality three‐dimensional structure. The resulting high‐quality model was applied for conformational B‐cell epitope selection and docking analyses with toll‐like receptor‐3 for understanding the capability of the vaccine to elicit an immune response. In silico cloning and codon adaptation were also performed with the pET‐19b plasmid vector. The designed multiepitope peptide vaccine may prompt the development of a vaccine to control SARS‐CoV‐2 infection.
COVID-19 is a new viral emergent disease caused by a novel strain of coronavirus. This virus has caused a huge problem in the world as millions of people are affected by this disease. We aimed at designing a peptide vaccine for COVID-19 particularly for the envelope protein using computational methods to predict epitopes inducing the immune system. The envelope protein sequence of SARS-CoV-2 has been retrieved from the NCBI database. The bioinformatics analysis was carried out by using the Immune Epitope Database (IEDB) to predict B- and T-cell epitopes. The predicted HTL and CTL epitopes were docked with HLA alleles and binding energies were evaluated. The allergenicity of predicted epitopes was analyzed, the conservancy analysis was performed, and the population coverage was determined throughout the world. Some overlapped CTL, HTL, and B-cell epitopes were suggested to become a universal candidate for peptide-based vaccine against COVID-19. This vaccine peptide could simultaneously elicit humoral and cell-mediated immune responses. We hope to confirm our findings by adding complementary steps of both in vitro and in vivo studies to support this new universal predicted candidate.
Analysis of the Amino-peptidase N (APN) protein from Anopheles culicifacies as a vector based Transmission Blocking Vaccines (TBV) target has been considered for malaria vaccine development. Short peptides as potential epitopes for B cells and cytotoxic T cells and/or helper T cells were identified using prediction models provided by NetCTL and IEDB servers. Antigenicity determination, allergenicity, immunogenicity, epitope conservancy analysis, atomic interaction with HLA allele specific structure models and population coverage were investigated in this study. The analysis of the target protein helped to identify conserved regions as potential epitopes of APN in various Anopheles species. The T cell epitopes like peptides were further analyzed by using molecular docking to check interactions against the allele specific HLA models. Thus, we report the predicted B cell (VDERYRL) and T cell (RRYLATTQF for HLA class I and LKATFTVSI for HLA class II) epitopes like peptides from APN protein of Anopheles culicifacies (Diptera: Culicidae) for further consideration as vaccine candidates subsequent to in vitro and in vivo analysis.
COVID-19 is a new viral emergent human disease caused by a novel strain of Coronavirus.This virus has caused a huge problem in the world as millions of the people are affected with this disease in the entire world. We aimed to design a peptide vaccine for COVID-19 particularly for the envelope protein using computational methods to predict epitopes inducing the immune system and can be used later to create a new peptide vaccine that could replace conventional vaccines. A total of available 370 sequences of SARS-CoV-2 were retrieved from NCBI for bioinformatics analysis using Immune Epitope Data Base (IEDB) to predict B and T cells epitopes. Then we docked the best predicted CTL epitopes with HLA alleles. CTL cell epitopes namely interacted with MHC class I alleles and we suggested them to become universal peptides based vaccine against COVID-19. Potentially continuous B cell epitopes were predicted using tools from IEDB. The Allergenicity of predicted epitopes was analyzed by AllerTOP tool and the coverage was determined throughout the worlds. We found these CTL epitopes to be T helper epitopes also. The B cell epitope, SRVKNL and T cell epitope, FLAFVVFLL were suggested to become a universal candidate for peptide-based vaccine against COVID-19. We hope to confirm our findings by adding complementary steps of both in vitro and in vivo studies to support this new universal predicted candidate.
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