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预先训练的抗体语言模型的监督微调可以改善抗原特异性预测

Meng Wang¹, Jonathan Patsenker², Henry Li²

¹Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America.

PLoS computational biology

|March 31, 2025

概括

监督微调增强了用于预测抗体-抗原特异性的抗体语言模型. 这种方法提高了SARS-CoV-2和流感抗原的准确性,并揭示了分子识别见解.

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:

抗体是适应性免疫的关键,其特异性决定功能.
预测抗体-抗原相互作用对于疫苗设计和治疗至关重要.
目前的方法可能无法充分利用预先训练的抗体语言模型进行特异性预测.

研究的目的:

通过对抗体语言模型的监督微调来改善抗体-抗原特异性预测.
与预先训练的嵌入相比,评估微调模型的增强预测准确性.
探索抗原识别的分子基础,跟踪疫苗接种后免疫谱系的变化.

主要方法:

监督微调四个预训练的抗体语言模型.
培训分类器来预测SARS-CoV-2尖端蛋白和流感血蛋白的特异性.
对模型注意力激活的分析和对BCR目录数据的应用.

主要成果:

微调的抗体语言模型分类器显示了对抗原特异性的增强预测准确性.
监督微调改进了仅在预先训练的嵌入式上训练的分类器.
模型捕捉了因流感和SARS-CoV-2疫苗接种而发生的剧目变化.

结论:

监督微调是一种有效的策略,用于增强预先训练的抗体语言模型,用于抗原特异性预测.
这些微调的模型为抗体-抗原识别机制提供了宝贵的见解.
该方法在分析免疫对疫苗接种的反应方面具有实用性.

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