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Updated: Jul 26, 2025

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries

Published on: January 17, 2015

候选抗体的机器学习优化产生了高度多样化的亚纳米相亲抗体库

Lin Li¹, Esther Gupta², John Spaeth²

¹Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA. Lin.Li@LL.MIT.EDU.

Nature communications

|June 12, 2023

概括

我们开发了一种贝叶斯语言模型,用于设计高亲和抗体碎片 (scFvs). 这种人工智能驱动的方法显著改善了治疗性抗体的发现,超过了传统的技术.

科学领域:

生物技术是生物技术.
计算生物学计算生物学
药物发现药物发现药物发现

背景情况:

治疗性抗体是一个关键的药物类别,但它们的设计是耗时和昂贵的.
开发高亲和度抗体片段,如单链可变片段 (scFvs),对于有效的治疗方法至关重要.

研究的目的:

提出一种新的,端到端的贝叶斯语言模型,用于设计大型和多样化的高亲和度scFvs.库.
将这种人工智能驱动方法的性能与传统的定向进化方法进行比较.

主要方法:

采用贝叶斯式,基于语言模型的方法进行scFv库的新设计.
经验测量了设计的scFvs. 的结合亲和力.
将人工智能设计的scFvs与通过定向进化产生的scFvs进行了比较.

主要成果:

通过人工智能方法设计的最好的scFv在结合方面表现出28.7倍的改进,与最好的定向进化相比.
在最成功的图书馆中,99%的设计的scFvs表现出比最初的候选者更好的绑定.
该方法成功地预测了图书馆的成功,并探索了多样性-亲和力的权衡.

结论:

机器学习模型,特别是语言模型,可以显著加速和增强scFv开发.
这种由人工智能驱动的方法为蛋白质工程和治疗抗体设计提供了强大的,广泛适用的工具.

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