An immune world model with multi-agent orchestration for peptide vaccines

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.