Deep sequencing combined with high-throughput screening enables efficient development of a pH-dependent high-affinity binding domain targeting HER3.

IF 5.2 3区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Protein Science Pub Date : 2025-08-01 DOI:10.1002/pro.70247
Marit Möller, Malin Jönsson, Magnus Lundqvist, Johan Rockberg, John Löfblom, Hanna Tegel, Sophia Hober
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引用次数: 0

Abstract

In vitro methods for developing binding domains have been well-established for many years, owing to the cost-efficient synthesis of DNA and high-throughput selection and screening technologies. However, generating high-affinity binding domains often requires the development of focused maturation libraries for a second selection, which typically demands a detailed understanding of the binding surfaces from the initial selection, a process that can be time-consuming. In this study, we accelerated this process by using deep sequencing data from the first selection to guide the design of the maturation library. Additionally, we employed a high-throughput screening system using flow cytometry based on Escherichia coli display to identify conditional binding domains from the selection output. This approach enabled the development of a high-affinity binder targeting the cancer biomarker HER3, with a binding affinity of 3.3 nM at extracellular pH 7.4, 100 times higher than the first-generation binding domain. Notably, the binding domain features a pH-dependent release mechanism, enabling rapid release in slightly acidic environments (pH ≈6), which resemble endosomal conditions. When conjugated to the cytotoxin mertansine (DM1), the binding domain demonstrated specific cytotoxic activity against HER3-expressing cell lines, with an IC50 of 2-5 nM. The presented approach enables the efficient development of conditional binding domains which hold promise for therapeutic applications.

深度测序结合高通量筛选能够有效开发针对HER3的ph依赖性高亲和力结合域。
由于具有成本效益的DNA合成和高通量的选择和筛选技术,开发结合域的体外方法已经建立了多年。然而,产生高亲和力结合域通常需要为第二次选择开发重点成熟库,这通常需要从初始选择中详细了解结合表面,这一过程可能很耗时。在本研究中,我们利用首次筛选的深度测序数据来指导成熟文库的设计,从而加速了这一过程。此外,我们采用了基于大肠杆菌显示的流式细胞术的高通量筛选系统,从选择输出中识别条件结合域。这种方法能够开发出针对癌症生物标志物HER3的高亲和力结合物,在细胞外pH 7.4下的结合亲和力为3.3 nM,比第一代结合域高100倍。值得注意的是,结合域具有pH依赖的释放机制,能够在类似于内体条件的微酸性环境(pH≈6)中快速释放。当与细胞毒素mertansine (DM1)结合时,该结合域对表达her3的细胞系显示出特异性的细胞毒活性,IC50为2-5 nM。所提出的方法使条件结合域的有效开发有望用于治疗应用。
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来源期刊
Protein Science
Protein Science 生物-生化与分子生物学
CiteScore
12.40
自引率
1.20%
发文量
246
审稿时长
1 months
期刊介绍: Protein Science, the flagship journal of The Protein Society, is a publication that focuses on advancing fundamental knowledge in the field of protein molecules. The journal welcomes original reports and review articles that contribute to our understanding of protein function, structure, folding, design, and evolution. Additionally, Protein Science encourages papers that explore the applications of protein science in various areas such as therapeutics, protein-based biomaterials, bionanotechnology, synthetic biology, and bioelectronics. The journal accepts manuscript submissions in any suitable format for review, with the requirement of converting the manuscript to journal-style format only upon acceptance for publication. Protein Science is indexed and abstracted in numerous databases, including the Agricultural & Environmental Science Database (ProQuest), Biological Science Database (ProQuest), CAS: Chemical Abstracts Service (ACS), Embase (Elsevier), Health & Medical Collection (ProQuest), Health Research Premium Collection (ProQuest), Materials Science & Engineering Database (ProQuest), MEDLINE/PubMed (NLM), Natural Science Collection (ProQuest), and SciTech Premium Collection (ProQuest).
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