蛋白质语言模型可以预测单特异性,双特异性和仅重链抗体的多活性
Xin Yu, Kostika Vangjeli1, Anusha Prakash1
1Biotherapeutics and Genetic Medicine, AbbVie Bioresearch Center, Worcester, MA 01605, United States.
Antibody therapeutics
|July 22, 2024
概括
计算模型预测了抗体多活性,这是药物开发的关键因素. 这项研究引入了使用蛋白质语言模型的深度学习模型,以预测抗体与baculovirus颗粒和牛血清白蛋白结合,有助于早期风险评估.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 免疫学 免疫学 免疫学
背景情况:
- 早期评估抗体非标结合对于减轻可开发性风险至关重要,例如降低疗效和免疫性.
- 细菌病毒颗粒 (BVP) 结合试验是评估抗体多活性性的标准.
- 对抗体多活性的计算预测模型是需要在体发现查.
研究的目的:
- 利用深度学习和蛋白质语言模型开发用于预测抗体多活性的计算模型.
- 为更全面地评估抗体结合性质,创建补充性测试.
主要方法:
- 开发了一组使用基础蛋白质语言模型 (ESM2,ProtT5) 和抗体特异模型 (Antiberty) 的三种深度学习模型.
- 在大量抗体序列和实验条件的数据集上使用转移学习训练模型.
- 使用BVP和牛血清白蛋白结合试验进行培训和验证.
主要成果:
- 开发的模型在各种抗体格式中表现出强大的性能,包括正规的mAbs,双特异的Abs和VHH-Fcs.
- 蛋白质语言模型的表现优于基于来自AlphaFold 2结构的分子描述符的模型.
- 来自抗体特异性和基础蛋白质语言模型的嵌入产生了相似的性能.
结论:
- 这项工作首次应用了蛋白质语言模型来预测对双特异抗体和VHH-Fcs.的测试数据.
- 开发的模型为抗体的反查提供了有价值的工具,改善了早期开发能力的评估.
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