Abstract
The avian influenza is an important infectious disease of birds. The genome of influenza A virus was segmented, single-stranded, negative-sense RNA molecules, which encodes many proteins. The significant surface proteins are hemagglutinin and neuraminidase for the pathogenicity of birds to humans. The prediction of epitopes in protein provides a suitable primary immunodiagnostic antigen for the detection of the influenza A virus H5N1. It was further used in the development and approval of epitopes, which were used as antigens, and the peptides can be used for vaccines in the potential control of an emerging pandemic of this pathogen. The conserved epitopes may be useful for the diagnosis of animals infected with the influenza virus. These might be helpful to prevent the spreading of influenza in animal to animal and also in the prevention and monitoring of its spread in the newer region. The epitopes provide the support for serodiagnosis or as a protective immunogen in novel vaccines. In this study, the preliminary data from the in silico analysis of hemagglutinin and neuraminidase was done to find potential T-cell epitopes. The determined peptides were beneficial for vaccine development, as they can reduce time by minimizing the number of required tests to find the possible selected epitopes, which target for vaccine development. T-cell recognition of the peptide-major histocompatible complex (MHC) is a prerequisite for cellular immunity. This work examines existing computational strategies for the study of peptide-MHC interactions. We have also provided guidelines for predicting antigenic peptides based on the availability of existing experimental data.
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