Streptococcus pneumoniae remains a leading cause of pneumonia, meningitis and sepsis. Existing pneumococcal conjugate vaccines are highly effective but serotype-limited, costly and vulnerable to serotype replacement. Next-generation vaccines require conserved protein antigens capable of inducing serotype-independent protection, but antigen selection remains difficult because animal immunogenicity does not always predict human protection.

This DPhil project will use Oxford pneumococcal controlled human infection model (CHIM) datasets to guide AI-driven antigen discovery and vaccine design. Recent Oxford analyses suggest that immune responses to pneumococcal proteins including SP1069, PdB and SP0899 are associated with reduced colonisation risk. The student will build on these human protection signals by integrating pneumococcal pangenome/proteome data, AMR/strain diversity, antigen localisation, conservation, expression evidence and immune-response datasets into interpretable antigen-prioritisation pipelines.

The project will combine AI-guided reverse vaccinology with structure-informed antigen engineering. Literature-supported and well-characterised pneumococcal antigens will be used as baselines alongside up to 30 prioritised novel candidate proteins discovered by AI/ML guided antigen discovery. Candidates will be assessed for developability, epitope presentation, strain breadth and manufacturability. Selected antigens will be produced as recombinant proteins using OVG protein-production capacity. These proteins will be used for ELISA validation, IntelliFlex/Luminex systems serology, antibody avidity, complement deposition and other functional assays, and CHIM-linked immune-correlate analysis.

Antigens showing promising human immune signatures and functional readouts will be prioritised for structure-informed engineering to improve expression, stability, protective epitope presentation and manufacturability. The strongest candidates may progress to mouse immunogenicity studies and, through collaborators, pneumococcal colonisation or invasive disease models. The expected output is a ranked and experimentally validated panel of serotype-independent pneumococcal vaccine antigens and a reusable framework for human-informed bacterial vaccine discovery.