有条件生成真实抗原特异性T细胞受体序列
Dhuvarakesh Karthikeyan1,2,3,4, Sarah N Bennett1,5,6, Amy G Reynolds5
1Personalized Immunotherapy Research Lab, University of North Carolina at Chapel Hill, Chapel Hill, NC USA.
Nature machine intelligence
|September 29, 2025
概括
我们开发了TCR-TRANSLATE,这是一个新的AI框架,用于设计T细胞受体 (TCR) 序列. 我们的模型成功设计了一种针对癌症抗原的功能性TCR,推进了个性化免疫疗法.
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:
- 由于复杂的交叉反应规则和有限的数据,为特定目标设计功能性T细胞受体 (TCR) 很困难.
- 现有的方法难以产生针对任意抗原的新型TCR序列.
研究的目的:
- 开发一个计算框架,TCR-TRANSLATE,用于生成抗原特异性TCR序列.
- 评估和优化TCR设计的序列对序列模型.
- 实验验证一个计算设计的TCR与一个治疗相关的目标.
主要方法:
- 使用一个序列对序列框架 (TCR-TRANSLATE) 适应机器翻译技术.
- 根据BART和T5架构评估了12个模型变体.
- 实验验证了一种针对威尔姆斯瘤抗原的设计TCR.
主要成果:
- 旗舰型号TCRT5在计算基准上表现出卓越的性能,功能序列的排名更高.
- 一个计算设计的TCR成功地与威尔姆斯瘤抗原 (非病毒性表位) 进行了验证.
- 设计的TCR表现出与病原体衍生的交叉反应,表明特异性限制.
结论:
- TCR-TRANSLATE为计算式 TCR 设计提供了基础,使得针对新型表征物生成功能性 TCR 成为可能.
- 这项工作突出了通过减少治疗点的搜索空间来加速个性化免疫疗法的潜力.
- 需要进一步的研究来解决TCR的特异性,并改善数据可用性,以提高计算设计.
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