SemVac: A Semantic Vaccinology Paradigm Powered by LLMs for Antigen Discovery
Zhao, Y.; Shu, Y.; Shu, L.; Lv, P.; Chi, X.; Li, D.; Zhang, J.; Huang, Z.; Ren, H.; Xu, J.; Zai, X.; Chen, W.
Show abstract
Reverse vaccinology has enabled sequence-based antigen discovery, but it over-looks the rich semantic knowledge embedded in the biomedical literature. Here we establish Semantic Vaccinology (SemVac), a paradigm that leverages large language models (LLMs) to predict protective antigens directly from scientific text. Benchmarking 14 state-of-the-art LLMs on a curated antigen dataset shows that text-reasoning-based approaches match or exceed specialized deep learning models in precision, while offering superior robustness on functionally ambiguous proteins. Intriguingly, explicit reasoning modes (e.g., chain-of-thought) increase recall but consistently reduce precision, revealing an over-reasoning pitfall in biological discovery tasks. Applied to the complete proteome of Mpox virus, SemVac recapitulates known protective antigens and identifies previously unrecognized candidates such as B20R, which our semantic analysis links to immune evasion and structural exposure. This work establishes literature-driven semantic reasoning as a powerful complement to conventional vaccinology, with broad implications for AI-aided scientific discovery.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Paraplume: A fast and accurate paratope prediction method provides insights into repertoire-scale binding dynamics 95%
- Supervised fine-tuning of pre-trained antibody language models improves antigen specificity prediction 95%
- THLANet: A Deep Learning Framework for Predicting TCR-pHLA Binding in Immunotherapy Applications 94%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.