What Makes a Good Vaccine Antigen Target? Defining Key Features and Predicting Candidates in the Staphylococcus aureus Proteome

Abstract

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.