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通过全面的突变扫描数据库,T细胞受体交叉活性预测得到了改进

Amitava Banerjee¹, David J Pattinson², Cornelia L Wincek³

¹Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.

|July 27, 2025

概括

绘制T细胞受体 (TCR) 目标对TCR疗法至关重要. 我们开发了 BATCAVE 数据库和 BATMAN 模型,以准确预测 TCR-相互作用,并有效地绘制 TCR 目标,提高突变抗原的预测准确度.

科学领域:

免疫学免疫学免疫学
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

在TCR疗法中,绘制T细胞受体 (TCR) 标对于预测病原体逃逸和非标效应至关重要.
目前面临的挑战包括缺乏公正的基准测试数据集和针对小突变的敏感计算方法.

研究的目的:

创建一个全面的数据库,并开发一个计算模型,用于准确的TCR目标映射.
为了能够有效地识别新型TCR的TCR目标.

主要方法:

策划了T细胞激活的基准与交叉反应激发皮细胞 (BATCAVE) 数据库,包含来自单氨基酸突变试验的超过22,000个TCR-对.
开发了由突变抗原激活TCR的贝叶斯推理 (BATMAN),这是一个在BATCAVE上训练的可解释的贝叶斯模型.
实施了积极学习扩展,以有效地绘制新型TCR的目标.

主要成果:

蝙蝠侠在预测TCR-相互作用方面优于现有的方法.
该模型揭示了TCR-结合的关键结构和生化预测因素.
蝙蝠侠精确预测多克隆T细胞反应,并识别具有高序列不相似性的TCR目标.

结论:

BATCAVE数据库和BATMAN模型为TCR目标映射提供了一个强大的框架.
这种方法提高了TCR-相互作用的预测,并促进了更安全,更有效的TCR疗法的开发.

关键词:

T细胞受体的交叉反应性.机器学习是机器学习.突变扫描数据库数据库.多克隆的T细胞反应.

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