核酸上下文模型在预测抗体亲和力成熟时的表现优于蛋白质语言模型
Mackenzie M Johnson1, Kevin Sung1, Hugh K Haddox1
1Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109-1024, USA.
bioRxiv : the preprint server for biology
|July 16, 2025
概括
使用核酸背景准确建模体变异 (SHM) 显著改善了对抗体亲和力成熟的预测. 基于核酸的模型在预测B细胞受体演变方面表现优于先进的蛋白质语言模型.
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,在B细胞上发展为B细胞受体 (BCR).
- BCRs经历亲和力成熟,这是一种涉及体质突变 (SHM) 和选择的过程,以增强抗原结合.
- 亲和力成熟的计算模型是从分子进化和语言建模的角度出现的.
研究的目的:
- 为了比较分子进化和语言建模方法对抗体亲和力成熟的预测能力.
- 通过使用BCR目录数据评估SHM,选择性SHM和蛋白质语言模型.
- 引入EPAM,一个用于基准测试和推进抗体进化模型的框架.
主要方法:
- 将基于核酸的分子进化模型与蛋白质语言模型进行比较.
- 利用了大量人类BCR数据集和一种抗原特异性小鼠实验.
- 开发并应用基于核酸的卷积神经网络用于SHM建模.
主要成果:
- 精确的SHM建模,结合核酸背景,显著提高了亲和力成熟的预测.
- 一个基于核酸的卷积神经网络建模SHM超越了最先进的蛋白质语言模型.
- 纳入选择估计只为预测能力提供了适度的改进.
结论:
- 核酸上下文对于准确建模抗体亲和力成熟过程中的体质突变至关重要.
- 与目前的蛋白质语言模型相比,基于核酸的模型为BCR进化提供了更好的预测能力.
- EPAM框架促进了对抗体进化和预测建模的综合研究.
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