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Cmai:从大规模测序数据中预测抗原-抗体相互作用

Bing Song, Kaiwen Wang, Saiyang Na

bioRxiv : the preprint server for biology

|July 15, 2024

概括

我们开发了Cmai,这是一种用于高通量抗体-抗原结合预测的AI工具. 该工具有助于识别免疫检查点抑制剂 (ICI) 治疗反应的生物标志物,并了解与免疫相关的不良事件 (irAEs).

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.
人工智能的人工智能

背景情况:

抗体-抗原相互作用对于幽默免疫非常重要.
目前用于分析这些相互作用的方法受到成本,时间和吞吐量的限制.
现有的生物信息学工具专注于抗体优化,而不是绑定预测.

研究的目的:

开发一种可扩展,高通量的人工智能工具,用于预测抗体-抗原结合.
使用人工智能预测的绑定数据创建生物标记指标,用于翻译应用.
为了研究与免疫相关的不良事件 (irAEs) 和癌症免疫治疗中的幽默性免疫反应.

主要方法:

开发一种名为Cmai的人工智能工具,用于抗体-抗原结合预测.
将Cmai应用于高通量B细胞受体 (BCR) 测序数据.
根据Cmai的输出设计了一个生物标记指标,用于分析免疫反应.

主要成果:

在验证过程中,Cmai 获得了 0.91 的 AUROC.
在irAEs期间,幽默免疫优先准细胞内抗原.
细胞外瘤抗原驱动B细胞透和与瘤细胞的同定位.
丰富的瘤抗原向抗体预测了对免疫检查点抑制剂 (ICI) 治疗的反应.

结论:

Cmai为高通量抗体-抗原结合预测提供了一个可扩展的解决方案.
开发的生物标志物方法为IRE和ICI治疗疗效提供了洞察力.
这项工作解决了当前抗体信息学和蛋白质复合体预测工具的局限性.

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