Selection of target-binding proteins from the information of weakly enriched phage display libraries by deep sequencing and machine learning.

IF 5.6 2区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
mAbs Pub Date : 2023-01-01 DOI:10.1080/19420862.2023.2168470
Tomoyuki Ito, Thuy Duong Nguyen, Yutaka Saito, Yoichi Kurumida, Hikaru Nakazawa, Sakiya Kawada, Hafumi Nishi, Koji Tsuda, Tomoshi Kameda, Mitsuo Umetsu
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引用次数: 1

Abstract

Despite the advances in surface-display systems for directed evolution, variants with high affinity are not always enriched due to undesirable biases that increase target-unrelated variants during biopanning. Here, our goal was to design a library containing improved variants from the information of the "weakly enriched" library where functional variants were weakly enriched. Deep sequencing for the previous biopanning result, where no functional antibody mimetics were experimentally identified, revealed that weak enrichment was partly due to undesirable biases during phage infection and amplification steps. The clustering analysis of the deep sequencing data from appropriate steps revealed no distinct sequence patterns, but a Bayesian machine learning model trained with the selected deep sequencing data supplied nine clusters with distinct sequence patterns. Phage libraries were designed on the basis of the sequence patterns identified, and four improved variants with target-specific affinity (EC50 = 80-277 nM) were identified by biopanning. The selection and use of deep sequencing data without undesirable bias enabled us to extract the information on prospective variants. In summary, the use of appropriate deep sequencing data and machine learning with the sequence data has the possibility of finding sequence space where functional variants are enriched.

利用深度测序和机器学习技术从弱富集噬菌体展示文库信息中选择靶结合蛋白。
尽管在定向进化的表面显示系统方面取得了进展,但由于在生物筛选过程中不受欢迎的偏差增加了与靶标无关的变异,具有高亲和力的变异并不总是丰富的。在这里,我们的目标是设计一个库,包含来自“弱富集”库的信息的改进变体,其中功能变体是弱富集的。对先前的生物筛选结果进行深度测序,在实验中没有发现功能性抗体模拟物,结果显示弱富集部分是由于噬菌体感染和扩增步骤中不希望出现的偏差。从适当的步骤对深度测序数据进行聚类分析,没有发现明显的序列模式,但使用所选深度测序数据训练的贝叶斯机器学习模型提供了9个具有明显序列模式的聚类。根据鉴定的噬菌体序列模式设计噬菌体文库,通过生物筛选鉴定出4个具有特异性亲和力(EC50 = 80 ~ 277 nM)的改进变体。深度测序数据的选择和使用没有不希望的偏差,使我们能够提取有关潜在变异的信息。总之,使用适当的深度测序数据和机器学习序列数据有可能找到功能变体丰富的序列空间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
mAbs
mAbs 工程技术-仪器仪表
CiteScore
10.70
自引率
11.30%
发文量
77
审稿时长
6-12 weeks
期刊介绍: mAbs is a multi-disciplinary journal dedicated to the art and science of antibody research and development. The journal has a strong scientific and medical focus, but also strives to serve a broader readership. The articles are thus of interest to scientists, clinical researchers, and physicians, as well as the wider mAb community, including our readers involved in technology transfer, legal issues, investment, strategic planning and the regulation of therapeutics.
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