蛋白质语言模型伪相似性捕捉了体内B细胞选择和进化的特征
Daphne van Ginneken1, Anamay Samant2, Karlis Daga-Krumins1
1Center for Translational Immunology, University Medical Center Utrecht, Lundlaan 6, Utrecht 3584EA, The Netherlands.
Briefings in bioinformatics
|August 18, 2025
概括
蛋白语言模型 (PLMs) 可以预测体内B细胞选择特征,包括体质突变和同型使用. 这些模型通过分析抗体序列数据,为抗体发现和工程提供了潜力.
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.
- 计算生物学 计算生物学
背景情况:
- B细胞的选择和进化对于有效的免疫反应至关重要.
- 测序和深度学习方面的进步提供了大量的抗体谱数据.
- 蛋白质语言模型 (PLMs) 学习复杂的抗体序列表示,用于各种应用.
研究的目的:
- 调查PLM是否能够捕获和预测体内B细胞选择特征.
- 分析PLM生成的序列伪相似性 (SPs) 和B细胞选择标记物之间的关系.
- 探索PLM在理解抗体进化和工程方面的实用性.
主要方法:
- 利用通用和抗体特定的PLM生成序列伪相似性 (SPs).
- 与体内B细胞选择特征相关的SPs:在单细胞分辨率下扩张,同型使用和体质突变 (SHM).
- 从人类和小鼠B细胞谱中构建进化血统树.
主要成果:
- PLM类型和抗体序列区域显著影响了SPs.
- 观察到SP和结合亲和力之间的负相关性,与体外发现形成对比.
- 发现SPS与诸如SHM和同型使用之类的曲目特征之间存在很强的相关性.
- 确定SHM是PLM建议的可能突变,保留的残留物具有更高的概率.
结论:
- PLM可以预测体内B细胞选择的关键特征,包括SHM和同型使用.
- SPs与特定的B细胞进化模式相关,与结合亲和力预测不同.
- 通过模拟B细胞进化,PLM显示出有助于抗体发现和工程的前景.
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