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学习抗体序列的约束从等位基包含.
Milind Jagota1, Chloe Hsu1, Thomas Mazumder2
1Computer Science Division, UC Berkeley, Berkeley, CA USA.
bioRxiv : the preprint server for biology
|November 1, 2024
概括
在纯粹的B细胞中基的包含揭示了新的抗体序列约束. 在这些罕见细胞上训练的机器学习模型比现有方法更准确地预测抗体特性.
科学领域:
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- B细胞产生抗体和B细胞受体 (BCRs),包括重链和轻链.
- 基排斥通常确保每个B细胞单一的重链和轻链对.
- 在B细胞表达两个生产性光链的情况下,等位体的包含是一种罕见但有信息的现象.
研究的目的:
- 调查基包含在原始B细胞中的潜力,以学习抗体序列约束.
- 开发机器学习模型,从等位体包含数据中识别异常抗体序列.
- 为了比较训练在等位基包含数据上的模型的预测能力与现有的抗体建模方法.
主要方法:
- 对从人类原始B细胞中大规模单细胞测序数据的分析.
- 训练机器学习模型识别异常的B细胞中异常光链序列,这些B细胞表现出基含有.
- 评估模型与抗体属性的相关性,如多活性,表面表达和亲和力成熟的使用情况.
- 研究小鼠的重链多样性和选择力,考虑替代轻链配对.
主要成果:
- 数以千计的人类纯粹B细胞与轻链等位素含有被确定,显著扩大了可用的数据集.
- 经过训练的机器学习模型显示出与在训练中未使用的抗体特性有很强的相关性.
- 这些相关性超过了当前抗体建模技术所取得的相关性,突出显示了等位基包含数据的价值.
- 发现小鼠的重链多样性通过与替代轻链配对而显著受到限制.
结论:
- 在原始B细胞中基的包含为了解抗体序列约束提供了有价值的信息来源.
- 机器学习模型利用等位基包含数据提供了对抗体特征的改进预测.
- 这些发现表明,等位基含有比以前认为的更为普遍的现象,并具有抗体工程和研究的重大潜力.
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