Yukti Sabikhi and Anshika Singh
Influenza is an extremely contagious respiratory illness that remains a significant public health issue because of its swift antigenic drift and shift, resulting in the appearance of new strains. Seasonal vaccines necessitate regular updates and frequently exhibit lower effectiveness, emphasizing the demand for universal and more potent vaccine options. This research utilized computational methods to evaluate the Hemagglutinin (HA) Chain A protein of the influenza virus as a possible vaccine target, due to its essential function in viral attachment, membrane fusion, and immune recognition. The HA protein sequence was sourced from the NCBI database and examined using ProtParam to assess physicochemical attributes, PSIPRED for predicting secondary structures, and DiANNA for evaluating disulfide bonds. VaxiJen was utilized to assess antigenicity, and AllerTOP was employed to confirm non-allergenic potential. 3D structural modeling with high confidence was conducted using trROSETTA to confirm structural integrity and find possible epitopic areas. Findings suggested that the HA protein has advantageous stability characteristics, an elevated antigenicity score, and is expected to be non-allergenic, exhibiting a strong tertiary structure reinforced by disulfide bonds. The trROSETTA model reached a TM-score of 0.908, validating precise folding and epitope availability. These results illustrate the promise of HA Chain A as a viable vaccine candidate and highlight the importance of combined in silico analyses for speeding up vaccine development. This computational pipeline provides a systematic approach for the logical creation of next-generation influenza vaccines, facilitating experimental validation and subsequent clinical application.