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通过实验采样机器学习预测的非有害的CDR突变来增强抗体亲和力

Thomas Clark¹, Vidya Subramanian¹, Akila Jayaraman¹

¹Altus Enterprises, 900 Middlesex Turnpike, Billerica, MA, USA.

Communications chemistry

|November 9, 2023

概括

机器学习模型现在可以指导抗体亲和力成熟. 一个新的分类器准确地预测了有益的突变,导致SARS-CoV-2抗体具有1000倍的结合亲和力.

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:

机器学习 (ML) 越来越多地用于抗体优化.
公开的抗体-抗原数据集很小并且有偏见,这阻碍了从突变中准确预测结合亲和力变化 (ΔΔG).
预测有害与非有害突变是抗体亲和力成熟的一个可行的替代方案.

研究的目的:

开发和验证用于分类抗体突变的ML模型.
将该模型集成到抗体亲和力成熟工作流程中.
评估ML在增强抗体结合方面的实际效用.

主要方法:

使用专家指导的功能开发了一个随机森林分类器 (AbRFC).
将 AbRFC 集成到一个计算-实验工作流中.
在内部数据集上验证了AbRFC,避免了公共数据的偏见.

主要成果:

在无偏差的验证集上,AbRFC准确地预测了非有害的突变.
对<10^2个设计的实验选发现了增强亲和力的突变.
对两个SARS-CoV-2抗体的结合亲和力增加了多达1000倍.

结论:

非有害突变的ML驱动分类是抗体亲和力成熟的强大策略.
这种方法克服了传统的ML模型的局限性,这些模型依赖于大型,公正的绑定亲和关系数据集.
开发的 AbRFC 工作流程有效地增强了对 SARS-CoV-2 RBD 的抗体结合.

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