细菌线意识深度学习模型和基准用于预测抗体VH-VL配对.
Sara Joubbi1,2, Enrico D'Arco1, Giuseppe Maccari2
1Department of Computer Science, University of Pisa, Pisa, Italy.
mAbs
|October 17, 2025
概括
通过计算预测兼容的抗体重光链对对于抗体工程至关重要. 这项研究引入了一个新的数据集和深度学习模型,在识别天然抗体组合时达到90%以上的准确性.
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:
- 抗体变量重 (VH) 和变量轻 (VL) 链的配对决定了抗体的功能.
- 有效的VH-VL对的实验性识别是资源密集的.
- 需要计算预测方法来加速抗体工程.
研究的目的:
- 开发一个全面的框架来预测兼容的VH-VL对.
- 创建一个基准数据集与自然和合成负对.
- 用不同的负采样策略训练的深度学习模型进行评估.
主要方法:
- 开发一个新的基准数据集,包括天然和合成的VH-VL对.
- 训练和评估三个深度学习模型,使用不同的负采样策略 (随机,V基因不匹配,V(D) J生殖系不匹配).
- 实施基于BERT的模型来区分天然和合成对.
主要成果:
- 基于BERT的模型在区分自然VH-VL对和合成VH-VL对方面取得了90%以上的准确性.
- V(D) J生殖系知情的负采样显著提高了模型的概括性.
- 开发的框架为计算抗体工程提供了可重现的基线.
结论:
- 已经建立了一个强大的计算框架和VH-VL配对预测的基准数据集.
- V(D) J生殖系信息对于提高预测模型的生物解释性和准确性至关重要.
- 这项工作促进了用于抗体工程的高效计算工具的开发.
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