ArticleBiomedicines2023
Design of a Novel and Potent Multi-Epitope Chimeric Vaccine against Human Papillomavirus (HPV): An Immunoinformatics Approach.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 15 citations in OpenAlex.
- Design and computational evaluation of a prophylactic and therapeutic multi-epitope vaccine candidate against cervical cancer.Virology journal · 2026Article
- In silico design of a multi-epitope vaccine targeting DENV-1 and DENV-3.Scientific reports · 2026Article
- Systems Vaccinology-Integrated Proteomics to Develop a Novel Prophylactic and Therapeutic Vaccine Against Human Papillomavirus 16.Bioinformatics and biology insights · 2026Article
- Design, synthesis and verification of HPV16 and HPV18 multiple antigens peptides.Journal of translational medicine · 2025Article
- Article
- Designing a Multi-Epitope Vaccine Against HPV 16, 18, 33, and 45 Targeting L1 and E7 Proteins: An Immunoinformatics Approach for Cervical Cancer Prevention and Therapy.Bioinformatics and biology insights · 2025Article
- In-silico development of a novel TLR2-mediating multi-epitope vaccine againstIn silico pharmacology · 2025Article
- Immunoinformatics approach for design novel multi-epitope prophylactic and therapeutic vaccine based on capsid proteins L1 and L2 and oncoproteins E6 and E7 of human papillomavirus 16 and human papillomavirus 18 against cervical cancer.Osong public health and research perspectives · 2024Article
- An immunoinformatics and structural vaccinology approach to design a novel and potent multi-epitope base vaccine targeting Zika virus.BMC chemistry · 2024Article
- Development of a Multiple-Epitope-Based Vaccine for Hepatitis C Virus Genotypes 1a and 1b: an in-silico reverse vaccinology approach.In silico pharmacology · 2024Article
- Multi-epitope Based Peptide Vaccine Candidate AgainstBioinformatics and biology insights · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the current era, our experience is full of pandemic infectious agents that no longer threaten the major local source but the whole globe. One such infectious agent is HPV, a sexually transmitted disease that can cause various clinical disorders, including benign lesions and cervical cancer. Since available vaccines still need further improvements in order to enhance efficacy, our goal was to design a chimeric vaccine against HPV using an immunoinformatics approach. For designing the vaccine, the sequence of HPV was retrieved, and then phylogenetic analysis was performed. Several CTL epitopes, HTL epitopes, and LBL epitopes were all predicted using bioinformatics tools. After checking the antigenicity, allergenicity, and toxicity scores, the best epitopes were selected for vaccine construction, and then physicochemical and immunological properties were analyzed. Subsequently, vaccine 3D structure prediction, refinement, and validation were performed. Molecular docking and dynamics simulation techniques were used to explore the interactions between the Toll-like receptor 2 and the modeled vaccine construct. To ensure the vaccine protein was expressed at a higher level, the construct was computationally cloned into the pET28a (+) plasmid. The molecular docking and simulation results showed that our designed vaccine is stable, of immunogenic quality, and has considerable solubility. Through in silico immune simulation, it was predicted that the designed polypeptide vaccine construct would trigger both humoral and cellular immune responses. The developed vaccine showed significant affinity for the TLR2 receptor molecule. However, additional laboratory research is required to evaluate its safety and efficacy.
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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.