Reverse vaccinology approach is an in silico methodology in order to identify antigens that are good vaccine candidates in a simple, safety and inexpensive way, with reduced time and effort. This strategy is based on bioinformatics tools, which predict important protein structural features, such as: integral transmembrane β-barrel arrangements; transmembrane alpha-helices; signal peptides; and secreted proteins. In this context, specific tools have been developed for the prediction of critical structural features, however despite significant progress, challenges persist due to the lack of integration in existing methods and the limited robustness and generalization of deep learning models with sparse data. To address these gaps, we introduce ReVarcine, a transformer-based deep neural network designed to automate the identification of signal peptides, subcellular localization, β-barrels, and alpha helices in bacteria, while prioritizing vaccine targets, advancing immunoinformatics and next-generation vaccine development. ReVarcine integrates predictions of signal peptides, subcellular localization, and structural features into a single automated workflow, generating detailed reports and prioritizing vaccine targets. Benchmarks against SignalP, PSORT, and PSIPRED demonstrate its superior predictive capabilities across diverse proteomes. By addressing key limitations in immunoinformatics, ReVarcine sets a new standard for computational tools in immunology and vaccinology, with potential for significant contributions through ongoing refinement and experimental validation.