Engineering intramolecular adjuvants for next-generation vaccines
Open accessAbstract
Vaccines play a pivotal role in reducing the global burden of infectious diseases. Currently, vaccine development is transitioning from empirical approaches toward precision engineering. The intramolecular adjuvant strategy, which fuses adjuvants and antigens into single molecules via genetic engineering, emerges as a promising strategy with the potential to play a key role in this transition. Through precise control of co-delivery and synergistic signaling, intramolecular adjuvants can modulate the magnitude, duration, and type of immune response, thereby advancing vaccine design toward a more programmable paradigm, in which immune activation can be rationally tuned at the molecular level, ultimately enabling “programmable immunity”. This review systematically summarizes the current toolbox of genetically encoded intramolecular adjuvants, including derivatives of bacterial toxins, pattern recognition receptor (PRR) ligands, cytokines, and the complement fragments. By integrating structural biology, synthetic biology, and immunology, we delineate their mechanisms in receptor-targeted delivery and synergistic immune pathway activation. Notably, artificial intelligence (AI) might accelerate the rational development of intramolecular adjuvants via sequence mining, structural optimization and de novo design. Collectively, this review aims to establish a systematic framework for intramolecular adjuvant selection and design, advancing a data-driven paradigm to counter emerging pathogens.
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
De novo design of protein structure and function with RFdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · Nature · doi:10.1038/s41586-023-06415-8 · Cited by 2,325 · see citing works
Robbie, G. J., Criste, R., Dall'Acqua, W. F., Jensen, K., Patel, N. K., Losonsky, G. A., & Griffin, M. P. (2013) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.01285-13 · Cited by 366 · see citing works
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- Authors
- Wang, Y., Liao, W., Zhang, Z., Wang, T., Zhang, J., Zai, X., & Xu, J.
- Affiliations
- Academy of Military Medical Sciences (CN)
- Corresponding author
- Zai, Xiaodong
- Xu, Junjie
- Venue
- The Innovation Medicine
- Publisher
- Innovation Press Co., Limited
- Published
- 2026
- Volume
- 4
- Issue
- 4
- Pages
- 100258
- ISSN
- 2959-8745 (ISSN-L)
- OpenAlex
- W7213779565
- Cited by
- 0
- References
- 3
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- The Innovation Medicine (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Immunology and Microbiology › Immunology › Immune responses and vaccinations
- Keywords
- vaccine design, bacterial toxins, cytokines, complement fragments, synthetic biology, structural biology
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