ImmuneAgent：多模态推理 AI 系统从人 B 细胞库跨病毒家族发现广谱中和抗体
Multimodal reasoning for broadly neutralizing antibody discovery from label-free human B cell repertoires across virus families
研究者提出闭环 AI 系统 ImmuneAgent，结合多模态推理、持续元学习与湿实验反馈，从无标签人 BCR 库中筛选广谱中和抗体。在 110 个克隆候选中实现约 55% 中和抗体发现率和约 11% bnAb 产出率，优于同等克隆预算下的序列预测器和共折叠模型；5 个抗体在小鼠致死性流感攻击中提供 100% 保护，效果与临床阶段药物 MEDI8852 相当。
Authors:Hantao Lou, Jianqing Zheng, Can Yue, Meihan Zhang, Yuanchao Bao, Yu Chen, Mengting Huang, Yupeng Yang, Qianyu Pan, Nana Fu, Yansong Shi, Hongli Li, Yangyang Chai, Ruyi Chen, Wansheng Li, Zhu Liang, Rongmei Yao, Yuanhan Mo, Lei Wang, Chunmei Wang, Yun Quan, Qiong Zhang, Xiangxi Wang, Xuetao Cao
Abstract:Discovering broadly neutralizing antibodies (bnAbs) from human natural immune repertoires remains a fundamental challenge in immunology, hindered by: the extreme rarity of bnAb, incomplete understanding of their cellular origins across pathogens, and the inability of existing computational tools to generalize across emerging viral threats. Here we present ImmuneAgent, a closed-loop AI system that integrates multimodal reasoning with continual meta-learning and wet-lab feedback to overcome these barriers. Applied to screen the natural BCR repertoires from vaccinated or infected cohorts, the system achieves a ~55% neutralization antibody discovery rate (60 of 110 cloned candidates) and a ~11% bnAb yield (12 of 110), substantially outperforming a state-of-the-art sequence-based neutralization predictor or cofolding models evaluated at the same cloning budget. Five ImmuneAgent-discovered antibodies conferred 100% in vivo protection against lethal influenza challenge, comparable to the clinical-stage therapeutic MEDI8852. The system recovered the cellular and structural determinants of bnAb activity and identified FCRL5+CD27+ atypical memory B cells as a conserved bnAb reservoir and hydrophobic interface enrichment as a cross-viral structural signature, which generalized to unseen antigens, discovering human metapneumovirus (hMPV) cross-neutralizing and human papillomavirus (HPV)-neutralizing antibodies without antigen-specific sorting. These results validate that ImmuneAgent is a generalizable framework for rapid therapeutic antibody discovery against emerging viral threats.
| Subjects: | Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Cell Behavior (q-bio.CB) |
| Cite as: | arXiv:2610.03160 [q-bio.QM] |
| (or arXiv:2610.03160v1 [q-bio.QM] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03160 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jian-Qing Zheng [view email]
[v1]
Fri, 2 Oct 2026 11:35:34 UTC (3,970 KB)
来源：arXiv：cs.AI · arxiv.org