T细胞受体特异性景观透露通过新的体设计
Gian Marco Visani1, Michael N Pun2, Anastasia A Minervina3
1Paul G. Allen School of Computer Science and Engineering, University of Washington, 85 E Stevens Way NE, Seattle, WA 98195, USA.
bioRxiv : the preprint server for biology
|March 17, 2025
概括
我们开发了一种计算方法来预测T细胞受体 (TCR) 与-MHC复合物的相互作用,并设计新的免疫性. 这种方法准确地预测了TCR结合和T细胞活性,有助于针对病毒和癌症的疫苗开发.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- T细胞对于适应性免疫至关重要,识别由MHC分子呈现的病原体.
- 由于功能数据有限,预测T细胞受体 (TCR) - -MHC (pMHC) 相互作用是具有挑战性的.
- 准确的预测对于理解免疫反应和开发向疗法至关重要.
研究的目的:
- 开发一种计算方法来预测TCR-pMHC相互作用,并设计新的免疫原性.
- 利用物理引导的机器学习模型 (HERMES) 来预测TCR结合亲和力和T细胞活动.
- 为治疗应用验证免疫原的新设计.
主要方法:
- 利用 HERMES,一种基于结构的,以物理为导向的机器学习模型,以蛋白质宇宙为训练.
- 在没有对TCR-pMHC数据的直接培训的情况下,预测的TCR-pMHC结合亲缘关系和T细胞活动.
- 开发了一种用于免疫性的新设计的计算协议,经过实验验证.
主要成果:
- 与TCR-pMHC结合和T细胞活动预测的实验数据达到了高达72%的相关性.
- 成功设计了多达五种替代的新型免疫性,在50%的病例中激活T细胞.
- 量化了各种TCR-MHC复合体的体识别格局,为T细胞特异性提供了洞察力.
结论:
- 计算方法准确地预测了TCR-pMHC相互作用,并使免疫性的新设计成为可能.
- 这个平台提供了一个新的计算策略,用于开发针对病毒感染和癌症的T细胞疫苗.
- 这些发现有助于我们更好地了解T细胞特异性,并对个性化免疫疗法产生影响.
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