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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. [ABSTRACT FROM AUTHOR]

The neuraminidase (NA) epitope from the Mexican AH1N1 influenza virus was identified by using sequences registered at the GenBank during the peak of a pandemic (from April 2009 to October 2010). First, NA protein sequences were submitted for multiple alignment analysis, and their three-dimensional models (3-D) were then built by using homology modeling. The most common sequence (denominated wild-type) and its mutants were submitted to linear and nonlinear epitope predictors, which included the major histocompatibility complex type II (MHC II) and B-cell peptides. The epitope prediction was in accordance with evolutionary behavior and some protein structural properties. The latter included a low NA mutation rate, NA 3-D surface exposure, and the presence of high hindrance side chain residues. After selecting the epitope, docking studies and molecular dynamics (MD) simulations were used to explore interactions between the epitope and MHC II. Afterward, several experimental assays were performed to validate the theoretical study by using antibodies from humans (infected by pandemic H1N1) and rabbits (epitope vaccination). The results show 119 complete sequences that were grouped into 28 protein sequences according to their identity (one wild-type and 27 representative mutants (1-5 mutations)). The predictors yielded several epitopes, with the best fit being the one located in the C-terminal region. Theoretical methods demonstrated that the selected epitope reached the P4, P6, P7, and P9 pockets of MHC II, whereas the experimental evidence indicates that the epitope is recognized by human antibodies and also by rabbit antibodies immunized with the peptide.