对分子模拟和深度学习的评估 预测抗体对TRBC1和TRBC2的识别
Xincheng Zeng1, Tianqun Wang2, Yue Kang2
1Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China.
Antibodies (Basel, Switzerland)
|September 27, 2023
概括
这项研究量化了使用AI和分子模拟对T细胞受体β链常数 (TRBC) 向TRBC1和TRBC2的抗体亲和力差异. 这些发现有助于开发精确的癌症疗法,可以选择性地消除恶性T细胞.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 结构生物学 结构生物学
背景情况:
- T细胞受体β链常数 (TRBC) 蛋白,TRBC1和TRBC2,是同源的癌症标.
- 向抗体疗法旨在消除恶性T细胞,同时保留正常的T细胞.
研究的目的:
- 量化对TRBC1和TRBC2的突变抗体的亲和力差异.
- 使用先进的计算方法探索抗体-抗原识别机制.
主要方法:
- 利用分子模拟和人工智能 (AI) 来评估抗体亲和力.
- 员工自由能量扰动 (FEP) 计算用于亲和力验证.
- 进行长期分子动力学模拟以分析抗原识别动力学.
主要成果:
- 对TRBC1和TRBC2具有特定突变的抗体的量化亲和力变化.
- 使用FEP计算验证了AI预测的亲和关系.
- 揭示了TRBC抗体与抗原相互作用的动态机制.
结论:
- 人工智能和分子模拟有效量化了对TRBC点的抗体亲和力.
- 了解动态识别机制可以指导改进的TRBC向抗体治疗癌症的设计.
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