探索基于结构的深度学习方法对T细胞受体设计的潜力
Helder V Ribeiro-Filho1, Gabriel E Jara1, João V S Guerra1,2
1Brazilian Biosciences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas 13083-100, Brazil.
bioRxiv : the preprint server for biology
|May 7, 2024
概括
深度学习模型在设计T细胞受体 (TCRs) 用于癌症免疫治疗方面表现有前途. 这些计算方法比传统的工程 TCR 针对特定的方法有优势.
科学领域:
- 蛋白质工程是一种蛋白质工程.
- 计算生物学是一种计算生物学.
- 免疫治疗是一种免疫疗法.
背景情况:
- 深度学习已经推进了蛋白质的建模和设计,使新型蛋白质的创建和优化为特定的功能.
- 设计用于免疫治疗的T细胞受体 (TCRs),特别是用于癌症治疗,是有前途的,但由于自然界面的复杂性而具有挑战性的应用.
- 目前基于物理学的计算方法在与TCR--MHC相互作用的低亲和力和交叉反应性特征作斗争.
结论:
- 基于结构的深度学习方法显示了设计具有特定约束能力的TCR的潜力.
- 这些计算方法在免疫治疗中比传统的TCR设计方法具有优势.
- 该研究确定了未来应用的基于深度学习的TCR设计的改进领域.
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