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微调的蛋白质语言模型识别了来自免疫谱的抗原特异性B细胞受体

Karen Paco¹, Mariana Paco Mendivil¹, Zihao Zhang¹

¹Henry E. Riggs School of Applied Life Sciences, Keck Graduate Institute, Claremont CA, USA.

bioRxiv : the preprint server for biology

|November 24, 2025

概括

蛋白质语言模型可以从抗体序列直接预测抗原特异性. 这种可扩展的方法识别了潜在的抗体治疗方法,包括SARS-CoV-2纳米体,推动了抗体的发现.

科学领域:

生物医学工程生物医学工程
免疫学免疫学免疫学
计算生物学计算生物学

背景情况:

从免疫谱中识别抗原特异性抗体至关重要,但具有挑战性.
目前用于抗体发现的方法在可扩展性和效率上可能受到限制.

研究的目的:

开发和验证一种可扩展的方法,使用蛋白质语言模型 (PLM) 预测抗体抗原特异性.
评估开发的模型的性能,抗原特异性预测器 (ASPred),对各种抗原,包括SARS-CoV-2,流感和艾滋病毒-艾滋病.

主要方法:

微调抗体重链序列上的PLM,以预测抗原特异性.
评估ASPred对免疫谱的性能 V(D) J从免疫小鼠的序列.
通过实验和分子动力学模拟验证预测的抗体序列.
基准测试 ASPred 预测与条形码启用抗原映射 (BEAM) 数据进行比较.

主要成果:

ASPred准确地预测了从未选择的免疫谱中获得的抗原特异性.
该模型在免疫小鼠中确定了SARS-CoV-2受体结合域 (RBD) 特定的抗体序列.
实验验证证了候选序列,包括一个高亲和度重链单纳米体.
ASPred展示了超出记忆序列的概括性和与BEAM数据的显著重叠,表明了可扩展性.

结论:

抗体重链序列含有足够的信息,使PLM可以推断抗原特异性.
ASPred为抗体发现提供了一个可扩展的框架,具有广泛的生物医学应用.
这种方法加速了针对各种病原体的治疗抗体的识别.

关键词:

发现抗体发现抗体多样性抗体多样性抗体异质性的异质性抗体异质性的异质性抗原的特异性抗原的特异性计算抗体发现的发现免疫学曲目免疫学曲目蛋白质语言模型

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