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有效的de novo仿真抗原受体的序列和结构决定因素

Arthur Chow¹, Hoyin Chu¹, Ruofan Li¹

¹Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY.

bioRxiv : the preprint server for biology

|December 19, 2025

概括

人工智能 (AI) 加快了用于癌症治疗的新型蛋白质结合剂的开发. 这项研究确定并克服了使用人工智能设计的蛋白质在仿真抗原受体 (CAR) T 细胞疗法中的关键挑战.

科学领域:

生物技术是生物技术.
免疫治疗是一种免疫疗法.
计算生物学计算生物学

背景情况:

使用人工智能的生成蛋白质设计快速创建瘤抗原的结合剂.
人工智能设计的蛋白质结合剂在治疗应用中,如CAR T细胞,存在有限的评估.

研究的目的:

合成人工智能驱动的蛋白质设计工作流程,用于选新型结合剂.
评估AI设计的蛋白质结合剂在CAR T细胞应用中针对BCMA,CD19和CD22的疗效.
识别和解决阻碍在CAR疗法中使用新型蛋白质结合剂的挑战.

主要方法:

使用人工智能生成的设计工作流程,选了1589种新型蛋白质结合剂.
利用可扩展的蛋白质结合和T细胞测试来评估疗效.
开发了计算和实验启发式来克服已识别的局限性.

主要成果:

确定了三个主要挑战:增强信号传递,封闭性表皮质参与和非目标活动.
开发了恢复目标CAR激活的方法,同时减轻负债.
成功选了父母结构的序列变异,以改善CAR的功能.

结论:

开发的框架加速了人工智能设计的蛋白质的临床前查.
这项工作通过改进人工智能设计的CAR T细胞蛋白质结合剂,使新一代细胞疗法成为可能.
克服 de novo 结合剂实用性的局限性对于在治疗开发中推进人工智能至关重要.

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