Simplifying complex antibody engineering using machine learning.

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Emily K Makowski, Hsin-Ting Chen, Peter M Tessier
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引用次数: 0

Abstract

Machine learning is transforming antibody engineering by enabling the generation of drug-like monoclonal antibodies with unprecedented efficiency. Unsupervised algorithms trained on massive and diverse protein sequence datasets facilitate the prediction of panels of antibody variants with native-like intrinsic properties (e.g., high stability), greatly reducing the amount of subsequent experimentation needed to identify specific candidates that also possess desired extrinsic properties (e.g., high affinity). Additionally, supervised algorithms, which are trained on deep sequencing datasets obtained after enrichment of in vitro antibody libraries for one or more specific extrinsic properties, enable the prediction of antibody variants with desired combinations of extrinsic properties without the need for additional screening. Here we review recent advances using both machine learning approaches and how they are impacting the field of antibody engineering as well as key outstanding challenges and opportunities for these paradigm-changing methods.

Abstract Image

使用机器学习简化复杂的抗体工程。
机器学习正在改变抗体工程,以前所未有的效率产生类似药物的单克隆抗体。在大量和多样化的蛋白质序列数据集上训练的无监督算法有助于预测具有天然样固有特性(例如,高稳定性)的抗体变体组,大大减少了识别具有所需外部特性(例如,高亲和力)的特定候选物所需的后续实验量。此外,在体外抗体库富集一种或多种特定外在特性后获得的深度测序数据集上训练的监督算法,能够预测具有所需外在特性组合的抗体变体,而无需额外的筛选。在这里,我们回顾了使用机器学习方法的最新进展,以及它们如何影响抗体工程领域,以及这些改变范式的方法面临的关键挑战和机遇。
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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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