ImmuneAgent 系统从人 B 细胞库中发现广谱中和抗体
Multimodal reasoning for broadly neutralizing antibody discovery from label-free human B cell repertoires across virus families
一个把 AI 推理和湿实验闭环起来的抗体发现系统，110 个候选里 60 个真中和，还顺带找到了 bnAb 的细胞来源，免疫学背景的读者别错过。
ImmuneAgent 是一个闭环 AI 系统，结合多模态推理、持续元学习与湿实验反馈，用于从无标记的人 BCR 库中筛选广谱中和抗体。在 110 个克隆候选中实现约 55% 的中和抗体发现率（60/110）和约 11% 的 bnAb 产率（12/110），超过同克隆预算下的序列中和预测器与 cofolding 模型。其中 5 个抗体在小鼠流感致死攻击模型中提供 100% 保护，效果与临床阶段疗法 MEDI8852 相当。系统还发现 FCRL5+CD27+ 非典型记忆 B 细胞是 bnAb 的保守来源，并将方法泛化到 hMPV 与 HPV，无需抗原特异性分选即可发现中和抗体。
Multimodal reasoning for broadly neutralizing antibody discovery from label-free human B cell repertoires across virus families
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.
