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bioRxiv · 10.1101/2025.08.03.668363

Reengineering the antigen optimization process for superior neoantigen vaccine design

Zhang, G.·Fu, Y.·Buratto, D.·Chan, K. C.·Wang, H.·Huang, Y.·Huang, L.·Zhou, R.
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Abstract

Identifying effective neoantigen sequences is essential for enhancing anti-tumor immunity. However, the vast sequence space (>109 possible peptides) and limited accuracy of existing immunogenicity predictors hinder efficient vaccine design for patient-specific human leukocyte antigens (HLAs). We present AlphaVacc, a deep reinforcement learning framework that integrates Monte Carlo Tree Search with a Transformer-based network to optimize antigenic peptides. AlphaVacc outperforms previous generative models in binding-affinity prediction. Experimental validation of 12 AlphaVacc-generated variants of the BING-4 peptide confirmed that 11 showed increased HLA-A*02:01 binding and 7 elicited significant T cell responses. Further testing of 16 single-mutation peptides confirmed computational predictions for 15 candidates, exhibiting a remarkable success rate. AlphaVacc thus provides a powerful tool for designing neoantigen-based cancer vaccines and may accelerate personalized immunotherapies.

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Zhang, G., Fu, Y., Buratto, D., Chan, K. C., Wang, H., Huang, Y., Huang, L., Zhou, R.. 2025-08-04. Reengineering the antigen optimization process for superior neoantigen vaccine design. https://doi.org/10.1101/2025.08.03.668363

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