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[3H6-OS-10d-04] Development of Vaccine Antigen Design Using In Silico Tools
Keywords:vaccine development, epitope prediction, influenza virus
In recent years, advancements in bioinformatics have significantly improved epitope prediction. Notably, the IEDB database enables the prediction of both T-cell epitopes and linear B-cell epitopes with a certain level of accuracy using only antigen sequence data. Furthermore, tools for evaluating the toxicity and allergenicity of these epitopes have also been well-developed.
Leveraging these tools, multi-epitope vaccines, which align multiple epitopes in sequence, have been developed for various antigens and have demonstrated effectiveness. In the case of influenza, numerous in silico vaccine designs have been published, and some studies have shown superior efficacy compared to the quadrivalent influenza vaccine (QIV) in mouse infection experiments. This suggests that multi-epitope design is a viable approach for effective antigen design in influenza.
In this study, we focused on H5N1 influenza and performed antigen design using the in silico tools.
Leveraging these tools, multi-epitope vaccines, which align multiple epitopes in sequence, have been developed for various antigens and have demonstrated effectiveness. In the case of influenza, numerous in silico vaccine designs have been published, and some studies have shown superior efficacy compared to the quadrivalent influenza vaccine (QIV) in mouse infection experiments. This suggests that multi-epitope design is a viable approach for effective antigen design in influenza.
In this study, we focused on H5N1 influenza and performed antigen design using the in silico tools.
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