ALLM-Ab:使用微调蛋白语言模型进行主动学习驱动的抗体优化
1Department of Computer Science, School of Computing, Institute of Science Tokyo, Yokohama 226-8501, Japan.
Journal of chemical information and modeling
|October 22, 2025
概括
使用蛋白质语言模型的积极学习框架ALLM-Ab加速了抗体序列优化. 它平衡了结合亲和力和可开发性,在发现高亲和力变体方面超过了其他方法.
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:
- 抗体工程面临的挑战是优化结合亲和力,同时保持可开发性.
- 蛋白质语言模型为预测抗体序列适应性提供了潜力.
研究的目的:
- 介绍ALLM-Ab,这是一个用于加速抗体序列优化的主动学习框架.
- 为了利用精细调整的蛋白质语言模型来有效地生成候选序列.
- 将抗体开发能力指标整合到优化过程中.
主要方法:
- 利用了蛋白质语言模型的参数有效微调 (低级调整).
- 用一种学习到等级的策略来评估突变体适应性.
- 整合了一个多目标优化方案,包括可开发性指标.
- 使用深度突变扫描数据和在线积极学习试验进行验证.
主要成果:
- ALLM-Ab准确地评估突变体适应性,并有效地生成候选序列.
- 该框架成功地平衡了改善的结合亲和力与治疗性抗体样性质.
- 与基线方法相比,证明了高亲和度抗体变体的加快发现.
- 在优化过程中保留了关键的抗体开发能力指标.
结论:
- ALLM-Ab提供了一种高效可靠的抗体设计策略.
- 该框架有可能显著降低治疗开发成本.
- 这种方法推进了抗体工程和药物发现领域.
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