An immune world model with multi-agent orchestration for peptide vaccines
- 1Shanghai Jiao Tong University
Abstract
Peptide-vaccine design requires a connection between vaccine-induced priming and recognition of the antigen naturally displayed by a tumor. We present an immune world model that preserves this connection through separate vaccine and tumor branches, endpoint-specific evidence and multi-agent orchestration. Native-recognition constraints held an altered peptide despite its higher presentation score, and targeted perturbations removed antigen-dependent benefit when required tumor-recognition conditions were absent. Public dendritic-cell data distinguished donor from antigen-source transfer: a context ranker achieved Recall@20 of 0.483 and 0.333 under the respective holdouts. Structure-informed receptor prediction added candidate-ranking evidence while preserving functional constraints and matched scenario trajectories. An assay observation model further concentrated a cross-presentation coefficient around its known value in a synthetic conditioning experiment. Together, these results establish a computational framework for linking molecular predictions, native-target requirements and conditional immune dynamics. The framework provides a reproducible basis for choosing peptide-vaccine candidates and the experiments needed to resolve their biological dependencies.
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BibTeX
@misc{chen2026immune,
title = {{An immune world model with multi-agent orchestration for peptide vaccines}},
author = {L. Chen and H. Luan and G. Yu},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.19.760096},
url = {https://www.biorxiv.org/content/10.64898/2026.10.19.760096v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - An immune world model with multi-agent orchestration for peptide vaccines
AU - L. Chen
AU - H. Luan
AU - G. Yu
PY - 2026
DA - 2026/10/19
PB - bioRxiv
DO - 10.64898/2026.10.19.760096
UR - https://www.biorxiv.org/content/10.64898/2026.10.19.760096v1
AB - Peptide-vaccine design requires a connection between vaccine-induced priming and recognition of the antigen naturally displayed by a tumor. We present an immune world model that preserves this connection through separate vaccine and tumor branches, endpoint-specific evidence and multi-agent orchestration. Native-recognition constraints held an altered peptide despite its higher presentation score, and targeted perturbations removed antigen-dependent benefit when required tumor-recognition conditions were absent. Public dendritic-cell data distinguished donor from antigen-source transfer: a context ranker achieved Recall@20 of 0.483 and 0.333 under the respective holdouts. Structure-informed receptor prediction added candidate-ranking evidence while preserving functional constraints and matched scenario trajectories. An assay observation model further concentrated a cross-presentation coefficient around its known value in a synthetic conditioning experiment. Together, these results establish a computational framework for linking molecular predictions, native-target requirements and conditional immune dynamics. The framework provides a reproducible basis for choosing peptide-vaccine candidates and the experiments needed to resolve their biological dependencies.
ER -