What Makes a Good Vaccine Antigen Target? Defining Key Features and Predicting Candidates in the Staphylococcus aureus Proteome
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The rapid advancement of computational methods is transforming vaccine development by enabling faster, data-driven identification of promising antigens. In this study, we applied an in-silico pipeline to assess a broad set of sequence, structure, localisation, and immunology-derived features and determine which most effectively discriminate antigens from non-antigens in bacteria. Using these insights, we identified bacterial proteins with high potential as vaccine antigens. Applied to Staphylococcus aureus , this approach prioritized 304 candidate antigens, highlighting SSLs, nutrient acquisition factors, and cell wall-associated enzymes. While these findings demonstrate the potential of bioinformatics-guided antigen discovery, experimental validation remains essential. This work underscores the growing role of integrated computational and machine-learning approaches in accelerating next-generation vaccine design.
