深度生成模型捕获了人类抗体中成熟度依赖的配对模式
Lea Brönnimann1,2, Thomas Lemmin1, Chiara Rodella1,2
1Institute of Biochemistry and Molecular Medicine (IBMM), University of Bern, 3012 Bern, Switzerland.
iScience
|January 21, 2026
概括
我们开发了一个深度学习框架,可以从重链中预测抗体轻链. 这种方法产生了可信的抗体对,有助于免疫谱系分析和治疗抗体设计.
科学领域:
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 抗体重光链配对对免疫系统功能和治疗性抗体的发展至关重要.
- 现有的序列数据库往往缺乏必要的对抗体链信息.
- 准确的配对数据对于理解免疫谱系架构至关重要.
研究的目的:
- 开发一个计算框架,以基于重链序列来预测抗体轻链.
- 为了弥补序列数据库内对抗体链数据中的差距.
- 为了生成可信的抗体对,以改善谱系分析和治疗设计.
主要方法:
- 采用了两阶段的深度学习框架.
- 基于变压器的语言模型在未配对的抗体序列上进行了预训练.
- 使用序列对序列模型,从重链输入生成轻链.
主要成果:
- 生成的抗体序列显示出高生殖系相同性和改善的结构质量.
- 该模型实现了本地轻链的适度恢复.
- 分析显示,来自记忆B细胞的重链中存在成熟度依赖的选择.
- 生成的kappa光链显示了三模态相似分布,表明了不同的配对模式.
结论:
- 序列对序列建模可以有效地揭示抗体中的链际依赖性.
- 开发的框架可以生成可信的抗体对,增强计算谱分析.
- 这种方法为设计具有特定配对特征的新型治疗抗体提供了基础.
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