ArticleThe Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale2020
An Immunoinformatics Study to Predict Epitopes in the Envelope Protein of SARS-CoV-2.
Article in The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The trial behind it
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Who cites it
14 citing papers in PubMed, 24 citations in OpenAlex.
- C-terminus-outward orientation of SARS-CoV-2 envelope proteins on viral capsid enables a novel virus-cell interaction pathway.Frontiers in cellular and infection microbiology · 2026Article
- Human Alveolar Macrophages Detect SARS-CoV-2 Envelope Protein Through TLR2 and TLR4 and Secrete Cytokines in Response.Immunology · 2025Article
- Subtractive proteomics and reverse-vaccinology approaches for novel drug targets and designing a chimeric vaccine againstFrontiers in immunology · 2025Article
- Evolutionary dynamics of canine kobuvirus in Vietnam and Thailand reveal the evidence of viral ability to evade host immunity.Scientific reports · 2024Article
- Molecular cloning, expression, and purification, along withFrontiers in fungal biology · 2024Article
- Mapping Potential Vaccine Candidates Predicted by VaxiJen for Different Viral Pathogens between 2017-2021-A Scoping Review.Vaccines · 2022Article
- Immunology to Immunotherapeutics of SARS-CoV-2: Identification of Immunogenic Epitopes for Vaccine Development.Current microbiology · 2022Review
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- Evaluation of the whole proteome to design a novel mRNA-based vaccine against multidrug-resistantFrontiers in microbiology · 2022Article
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- Epitope-based Multi-variant SARS-Cov-2 Vaccine Design: Shared Epitopes Among the Natural SARS-Cov-2 Spike Glycoprotein and 5 of its Variants (D614G, α, β, γ, δ) with HighJournal of immunological sciences · 2021Article
- Current and prospective computational approaches and challenges for developing COVID-19 vaccines.Advanced drug delivery reviews · 2021Review
- Lessons Learned from Cutting-Edge Immunoinformatics on Next-Generation COVID-19 Vaccine Research.International journal of peptide research and therapeutics · 2021Article
- Immunoinformatics Identification of B- and T-Cell Epitopes in the RNA-Dependent RNA Polymerase of SARS-CoV-2.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.