A continuous peptide epitope reacting with pandemic influenza AH1N1 predicted by bioinformatic approaches.

Computational identification of potential epitopes with an immunogenic capacity challenges immunological research. Several methods show considerable success, and together with experimental studies, the efficiency of the algorithms to identify potential peptides with biological activity has improved. Herein, an epitope was designed by combining bioinformatics, docking, and molecular dynamics simulations. The hemagglutinin protein of the H1N1 influenza pandemic strain served as a template, owing to the interest of obtaining a scheme of immunization. Afterward, we performed enzyme-linked immunosorbent assay (ELISA) using the epitope to analyze if any antibodies in human sera before and after the influenza outbreak in 2009 recognize this peptide. Also, a plaque reduction neutralization test induced by virus-neutralizing antibodies and the IgG determination showed the biological activity of this computationally designed peptide. The results of the ELISAs demonstrated that the serum of both prepandemic and pandemic recognized the epitope. Moreover, the plaque reduction neutralization test evidenced the capacity of the designed peptide to neutralize influenza virus in Madin-Darby canine cells.