抗体变量域的多维成熟与机器学习辅助
Tomoyuki Ito1, Sakiya Kawada1, Hikaru Nakazawa1
1Department of Biomolecular Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan.
mAbs
|January 7, 2026
概括
机器学习与分子进化相结合,增强了抗体VHH域,以改善亲和力和表达力. 这种方法优化了抗体特性,克服了传统突变发生的局限性.
科学领域:
- 生物技术是生物技术.
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
背景情况:
- 使用突变发生的传统抗体开发可能会对生物物理性质产生负面影响.
- 驼类重链抗体可变域 (VHHs) 对于治疗性抗体的开发至关重要.
研究的目的:
- 同时增强VHH域的抗体亲和力和表达水平.
- 应用机器学习辅助的分子进化来实现多维抗体优化.
主要方法:
- 菌体显示和深度测序用于对抗SARS-CoV-2 VHH的亲和力成熟.
- 机器学习模型被训练在实验测量117个变体的表达水平和亲和力.
- 高级变体被预测并通过计算验证.
主要成果:
- 选择的VHH变体在皮科莫拉范围中显示出50-70倍增加的亲和力.
- 与野生类型相比,工程VHHs表现出4-5倍高的表达水平.
- 一个变体显示了9.5°C的热稳定性改善.
结论:
- 机器学习辅助的分子进化对于多维抗体特性优化是有效的.
- 这一策略成功地提高了VHH域的亲和力和表达水平.
- 开发的VHH变种显示出治疗应用的前景.
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