基于深度学习的设计和实验验证对类似药物的人类抗体库进行实验验证
Nandhini Rajagopal1, Udit Choudhary2, Kenny Tsang1
1Biotherapeutics Molecule Discovery, Boehringer Ingelheim Pharmaceutical Inc., 900 Ridgebury Road, Ridgefield, CT 06877, United States.
Briefings in bioinformatics
|January 24, 2025
概括
研究人员开发了一种深度学习模型,以计算生成新的人类抗体序列. 这种方法加速了以抗体为基础的生物治疗药物的发现,具有理想的可开发性属性,扩大了治疗点.
科学领域:
- 计算生物学是一种计算生物学.
- 免疫学 免疫学 免疫学
- 生物技术是生物技术.
背景情况:
- 传统的抗体生成方法 (动物免疫,体外显示) 是耗时的.
- 抗体数据和深度学习的进步为计算抗体生成提供了新的可能性.
研究的目的:
- 描述一种深度学习模型,用于生成具有理想可开发性属性的人类抗体变量区域.
- 创建计算机生成的抗体库,类似于市场上的生物治疗药物 ("药物相似性").
主要方法:
- 使用深度学习模型,对31416个人类抗体进行训练,并使用计算可开发性标准.
- 产生了10万个抗原不可知的人类抗体可变区域序列.
- 通过实验性生产和生物物理属性评估验证了基生成的抗体.
主要成果:
- 在基生成的抗体表现出有利的内在序列,结构和物理化学性质.
- 实验生产的抗体表现出高表达率,单体含量和热稳定性.
- 生成的抗体显示出低水性,自我关联性和非特异性结合性.
结论:
- 可开发的人类抗体库的计算生成是可行的.
- 这种方法可以加速基于抗体的生物疗法在体内发现.
- 扩大了可用药物的目标范围,包括那些对传统方法耐火的目标.
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