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AI-guided discovery and structure-informed engineering of Staphylococcus aureus antigens using human nasal-colonisation immune signatures

Contribute to the Correlates of Immunity Program, pursuing a new leap in antibody and vaccine discovery, as a Graduate Ellison Scholar.

Key facts

Project description

Staphylococcus aureus is a major antimicrobial-resistance-associated pathogen causing skin and soft-tissue infection, pneumonia, bloodstream infection, device-associated disease and sepsis, yet no licensed vaccine exists despite decades of effort. This DPhil project will focus on novel protein antigen discovery and immunogen design, aligned to OVG's developing S. aureus nasal-colonisation CHIM/experimental medicine programme, including the ReSET study.

The student will integrate literature-supported and well-characterised S. aureus vaccine antigens as baselines with AI/ML-guided reverse vaccinology to identify up to 30 novel candidate protein antigens from S. aureus pangenome/proteome data. Candidate selection will consider conservation, predicted surface exposure or secretion, virulence biology, immune-evasion functions, strain diversity, developability and relevance to colonisation.

These candidates will be evaluated for association with human immune protection or colonisation outcomes using ReSET samples and systems serology, including IntelliFlex/Luminex-based multiplex measurement of antigen-specific IgG, IgA and IgM. Recombinant S. aureus proteins will also be used in ELISA to validate antigen-specific antibody responses, antibody avidity assays, Fc-receptor binding assays, complement deposition assays, opsonophagocytic killing assays, and inhibition-of-adhesion or toxin-neutralisation assays where relevant to antigen function. Cellular immunity will be assessed using peptide-pool stimulation, ELISpot, intracellular cytokine staining and flow-cytometry analysis to define CD4/CD8 T-cell responses and cytokine profiles. Additional analyses will explore B-cell and T-cell epitope features for correlates-of-protection discovery. Antigens showing promising human immune signatures and functional immune readouts will be produced as recombinant proteins using OVG protein-production capability and prioritised for structure-informed engineering.

The engineering component will aim to improve antigen stability, epitope presentation, expression and manufacturability, with particular attention to removing non-protective or immune-diverting regions. A multi-epitope immunogen approach may be developed for antigens or epitopes most strongly associated with protective immune signatures. The expected output is a ranked, experimentally supported panel of S. aureus protein antigens and engineered immunogen designs suitable for future preclinical vaccine evaluation. 

Skills Required

  • Strong interest in bacterial pathogenesis, immunology, vaccinology or infectious disease.
  • Previous wet-lab experience in molecular biology, microbiology, protein biology or immunology.
  • Quantitative aptitude and willingness to work with computational antigen-prioritisation outputs.
  • Ability to work across experimental immunology, protein-antigen design and data integration.
  • Strong organisational, communication and team-working skills. 

Skills to be Developed

  • AI/ML-guided reverse vaccinology and interpretation of antigen-prioritisation scorecards.
  • Systems serology using multiplex IntelliFlex/Luminex readouts for IgG, IgA and IgM.
  • Recombinant protein production, antigen QC and structure-informed immunogen engineering.
  • B-cell/T-cell epitope mapping and immune-correlate analysis using CHIM/experimental medicine samples.
  • Translational vaccine candidate down-selection and development of engineered multi-epitope immunogens. 

Relevant Background Reading

  1. Fowler VG et al. Effect of an investigational vaccine for preventing Staphylococcus aureus infections after cardiothoracic surgery: a randomized trial. JAMA. 2013.
  2. Zhang F et al. Protection against Staphylococcus aureus colonization and infection by B- and T-cell-mediated mechanisms. mBio. 2018.
  3. Giersing BK et al. Status of vaccine research and development of vaccines for Staphylococcus aureus. Vaccine. 2016.
  4. Ong et al. Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens. Bioinformatics. 2020.  
  5. Recent OVG/CoI-AI materials on human challenge, systems serology and immune correlates of protection. https://eit.org/projects/correlates-of-immunity---artificial-intelligence-coi-ai 

How to apply

Stage 1: Apply to Ellison Institute of Technology

Submit an application to Ellison Institute of Technology (EIT) by 5pm UK time on Monday 16 November 2026.

We encourage you to apply early, as applications will be reviewed as they are received.

Learn more and apply

Stage 2: Apply to the University of Oxford 

If selected by EIT, you will receive conditional funding and will be invited to apply to the University of Oxford by following the instructions on the course page.

Your application will then follow the standard graduate admissions process and will be assessed against the entry requirements shown on the course page.