Vaccination is one of the most successful public health interventions in modern medicine. However, it is still challenging to develop effective vaccines against many infectious diseases such as tuberculosis, HIV, and malaria. There are challenges in integrating the high volume, variety, and variability of vaccine-related data and rationally designing effective and safe vaccines efficiently. In my thesis study, I systematically and comprehensively analyzed manually annotated protective vaccine antigens in the Protegen database and identified these protective antigens' enriched patterns. I then created Vaxign-ML, a novel machine learning-based reverse vaccinology method based on the curated Protegen data for rational vaccine design. Vaxign-ML was used to successfully predict vaccine antigens for tuberculosis and Coronavirus Disease 2019 (COVID-19). I also developed a new structural vaccinology design program that optimizes COVID-19 spike glycoprotein as a vaccine candidate for enhanced vaccine protection via T cell epitope engineering. The vaccine antigens selected and optimized by Reverse and Structural Vaccinology in this dissertation are subjected to future experimental verification. Furthermore, I created a community-based Ontology of Host-Pathogen Interactions (OHPI), which served as a platform to semantically represent the interactions between host and virulence factors that are also protective antigens. I developed the Vaccine Investigation Ontology (VIO) for standardized metadata representation for high throughput vaccine OMICS data analysis. Overall, my thesis research aims to uncover protective antigen patterns, create methods/tools to effectively develop vaccines against infectious diseases of public health significance, and strengthen our understanding of vaccine protection mechanisms. These works can be further expanded and integrated with other technologies such as epitope prediction, molecular epidemiology, and high-throughput sequencing to build the foundation of precision vaccinology.