机器学习使纳米体的效率和有效的亲和力成熟成为可能
bioRxiv : the preprint server for biology
|February 6, 2026
概括
机器学习通过从有限的酵母显示数据中预测高亲和度结合剂来加速抗体发现. 线性模型有效地识别有益的突变,使得纳米体能够快速优化用于RXFP1.1.等治疗点的纳米体.
科学领域:
- 蛋白质工程是一种蛋白质工程.
- 计算生物学是一种计算生物学.
- 免疫学 免疫学 免疫学
背景情况:
- 由于相互作用表面大,抗体与高 afinity 的目标结合.
- 在体外抗体选择往往产生适度的亲和力,需要亲和力成熟.
- 亲和力成熟是艰苦的,涉及多个轮和策略.
研究的目的:
- 为了加速优化抗体结合剂的发现.
- 研究用于抗体工程的酵母显示数据的机器学习应用.
- 从单回合选择数据中预测高亲和度抗体序列.
主要方法:
- 酵母显示亲和力成熟活动.
- 机器学习分析单个排序轮数据的序列数据.
- 线性模型,深度神经网络和半监督方法的比较.
- 使用预测模型设计和选择纳米体结合剂.
主要成果:
- 从一种类型的稀疏测序数据可以预测多轮测序后丰富的序列.
- 线性模型在排名亲和力增强替代品方面优于深度神经网络和半监督方法.
- 线性模型为工程的残留偏好提供了可解释的见解.
- 开发了针对放松素家族受体1 (RXFP1) 的优化纳米体,包括亚纳米分子结合剂.
结论:
- 机器学习,特别是线性模型,可以显著加速抗体亲和力成熟.
- 基于有限数据的预测建模为传统的多轮优化提供了有效的替代方案.
- 这种方法可以快速设计用于治疗标的高亲和度结合剂.
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