S2ALM:序列结构预训练的大型语言模型用于全面的抗体表示学习
Mingze Yin1,2, Hanjing Zhou3, Jialu Wu4
1College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
Research (Washington, D.C.)
|August 21, 2025
概括
新的序列结构多级预训练抗体语言模型 (S2ALM) 整合了抗体序列和结构,以改善治疗开发. 这种先进的模型提高了对各种疾病的抗体的理解和设计.
科学领域:
- 生物技术
- 计算生物学
- 免疫学
背景情况:
- 抗体对于健康和疾病治疗至关重要, 生物医学语言模型显示出希望.
- 目前的模型缺乏对抗体结构信息的明确考虑,限制了它们的预测能力.
- 一维序列和三维结构都为抗体功能提供了补充的见解.
研究的目的:
- 提出一个统一的抗体语言模型,即S2ALM,它既集成了序列信息,也集成了结构信息.
- 开发一个层次化的预训练范式,以定制针对综合抗体表示的目标.
- 证明S2ALM在各种下游任务中的实用性,包括结合亲和预测和治疗抗体设计.
主要方法:
- 开发了序列结构多级预训练抗体语言模型 (S2ALM).
- 采用分层的预培训模式,有两个定制的多级培训目标.
- 在7500万个序列和1170万个结构的大数据集上进行了预训练.
主要成果:
- S2ALM的表示空间揭示了功能结合机制,进化性质和结构相互作用模式.
- 在预测抗原-抗体结合亲缘关系和B细胞成熟阶段方面取得了最先进的性能.
- 在识别关键抗体结合部位和设计新型冠状病毒结合抗体方面取得了成功.
结论:
- 通过整合序列和结构,S2ALM有效地建模了综合和通用的抗体表征.
- 该模型显示了促进治疗抗体开发和解决未满足需求的巨大潜力.
- 在各种抗体理解和生成任务中,S2ALM的表现优于现有的基线.
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