一个协同的生成排名框架,用于定制设计治疗单域抗体
Yu Kong1, Jiale Shi1, Fandi Wu2
1Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Department of Pulmonary and Critical Care Medicine, Department of Liver Surgery and Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China.
Cell discovery
|October 30, 2025
概括
由人工智能驱动的TFDesign-sdAb工程通过优化框架和CDR区域来增强单域抗体 (sdAbs). 这个框架成功地赋予了蛋白质A的结合,提高了治疗应用的可制造性和功能多功能性.
科学领域:
- 生物技术是生物技术.
- 免疫学 免疫学 免疫学
- 人工智能在医学中的应用
背景情况:
- 单域抗体 (sdAbs) 提供治疗优势,如小尺寸和组织透.
- 然而,sdAbs缺乏Fc域,限制了其功能和制造流程.
- 现有的工程方法通常只关注互补性决定区域 (CDR).
研究的目的:
- 开发一个人工智能驱动的框架,TFDesign-sdAb,用于SDAbs的理性工程.
- 为了使sdAbs能够获得新的功能性质,例如与蛋白A结合,同时保持抗原特异性.
- 为了克服sdAb功能多功能性和可制造性的局限性.
主要方法:
- 利用了深度学习框架,集成了一个结构意识的扩散模型 (IgGM) 用于候选生成和一个微调的分类器 (A2binder) 用于优先排序.
- 优化了 sdAbs. 的互补决定区域 (CDR) 和框架区域 (FR).
- 通过蛋白质A结合试验,亲和度测量和高分辨率结构特征,验证了工程 sdAbs.
主要成果:
- 成功地赋予了与人类VHs和缺乏这种特性的纳米体的蛋白质A结合.
- 通过蛋白A亲和染色学实现了高表达率,强大的结合亲和力和净化.
- 结构分析证实了保留的FR介导结合基因,模仿自然的Fc-蛋白A相互作用.
结论:
- TFDesign-sdAb提供了一个可扩展和通用化的解决方案,用于增强sdAb的功能.
- 人工智能驱动的设计可以克服抗体工程的局限性,提高治疗潜力.
- 该框架在产生功能性蛋白A结合sdAbs的过程中实现了100%的成功率,而不会影响抗原亲和力.
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