ArticleArchives of virology2020
Bioinformatics design and experimental validation of influenza A virus multi-epitopes that induce neutralizing antibodies.
Article in Archives of virology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- Consensus-Guided Construction of H5N1-Specific and Universal Influenza a Multiepitope Vaccines.Biology · 2025Article
- Construction of an aerolysin-based multi-epitope vaccine againstFrontiers in immunology · 2024Article
- Article
- Identification of peptide epitopes of the gp120 protein of HIV-1 capable of inducing cellular and humoral immunity.RSC advances · 2023Article
- The Advantage of Using Immunoinformatic Tools on Vaccine Design and Development for Coronavirus.Vaccines · 2022Review
- Databases, Knowledgebases, and Software Tools for Virus Informatics.Advances in experimental medicine and biology · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
Pandemics caused by influenza A virus (IAV) are responsible for the deaths of millions of humans around the world. One of these pandemics occurred in Mexico in 2009. Despite the impact of IAV on human health, there is no effective vaccine. Gene mutations and translocation of genome segments of different IAV subtypes infecting a single host cell make the development of a universal vaccine difficult. The design of immunogenic peptides using bioinformatics tools could be an interesting strategy to increase the success of vaccines. In this work, we used the predicted amino acid sequences of the neuraminidase (NA) and hemagglutinin (HA) proteins of different IAV subtypes to perform multiple alignments, epitope predictions, molecular dynamics simulations, and experimental validation. Peptide selection was based on the following criteria: promiscuity, protein surface exposure, and the degree of conservation among different medically relevant IAV strains. These peptides were tested using immunological assays to test their ability to induce production of antibodies against IAV. We immunized rabbits and mice and measured the levels of IgG and IgA antibodies in serum samples and nasal washes. Rabbit antibodies against the peptides P11 and P14 (both of which are hybrids of NA and HA) recognized HA from both group 1 (H1, H2, and H5) and group 2 (H3 and H7) IAV and also recognized the purified NA protein from the viral stock (influenza A Puerto Rico/916/34). IgG antibodies from rabbits immunized with P11 and P14 were capable of recognizing viral particles and inhibited virus hemagglutination. Additionally, intranasal immunization of mice with P11 and P14 induced specific IgG and IgA antibodies in serum and nasal mucosa, respectively. Interestingly, the IgG antibodies were found to have neutralizing capability. In conclusion, the peptides designed through in silico studies were validated in experimental assays.
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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.