针对具有设计T细胞受体和抗体的化物-MHC复合体
Amir Motmaen1,2,3, Kevin M Jude4,5, Nan Wang4,5
1Institute for Protein Design, University of Washington, Seattle, WA, USA.
bioRxiv : the preprint server for biology
|December 3, 2025
概括
研究人员开发了ADAPT,这是一个深度学习框架,用于设计T细胞受体 (TCR) 和抗体. 这种方法精确地针对新型癌症免疫疗法和自身免疫性疾病治疗的-MHC (pMHC) 复合体.
科学领域:
- 免疫学 免疫学 免疫学
- 结构生物学 结构生物学
- 计算生物学 计算生物学
背景情况:
- 第I类主要基因相容性复合体 (MHCs) 向T细胞呈现细胞内,用于免疫监测.
- 对-MHC (pMHC) 复合物的T细胞受体 (TCR) 识别对于识别病原体和瘤至关重要.
- 设计针对特定pMHC的治疗受体是具有挑战性的,因为自我抗原的约束.
研究的目的:
- 引入ADAPT,一个基于结构的深度学习框架,用于设计T细胞受体 (TCR) 和抗体.
- 为了能够设计针对特定-MHC (pMHC) 综合体的新型受体.
- 探索癌症免疫疗法和自身免疫性疾病中的治疗应用.
主要方法:
- 开发了一个深度学习框架 (ADAPT),集成用于抗原受体设计的结构信息.
- 设计和特征TCRs和抗体针对一个多样化的pMHC目标组.
- 使用冷电子显微镜 (cryo-EM) 来验证设计抗体的原子级准确性.
主要成果:
- 证明了 ADAPT 设计高特异性的 TCR 和抗体的能力,以对 pMHC 目标进行高特异性设计.
- 冷EM结构证实了在pMHC识别接口上设计的抗体的原子级准确性.
- 验证了基于结构的设计方法的稳定性和潜力.
结论:
- ADAPT提供了一个强大的平台,用于设计针对pMHC复合体的治疗受体.
- 设计的受体对推进癌症免疫疗法和治疗自身免疫性疾病充满希望.
- 这项工作增强了对TCR特异性的预测理解,影响了基础免疫学和诊断.
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