用于亲和增强生物检测的生物启发DNA框架编程异交。

IF 9.1 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Shuai Yang, Chenghao Xi, Xiaolei Zuo, Min Li
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引用次数: 0

摘要

天然多反应性抗体通过异质配体结合获得增强的亲和性。然而,精确控制识别基序方向和距离的工程合成杂交种系统仍然具有挑战性。本文提出了一种基于DNA框架的策略,通过空间组织靶向血小板衍生生长因子- bb (PDGF-BB)的二价适体来编程异型结合。通过系统地调节异型识别基序之间的距离,证明了结合亲和力的可编程调制。所得到的杂种杂种具有快速结合动力学、高特异性和对PDGF-BB的超灵敏检测,检测限达到0.5 pM。这种方法为设计多价相互作用提供了一个通用的平台,在疾病诊断和癌症、病毒感染和过敏性疾病的免疫治疗中具有潜在的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bioinspired DNA Framework-Programmed Heteroligation for Affinity-Enhanced Biodetection.

Natural polyreactive antibodies achieve enhanced avidity through heterogeneous ligand binding. However, engineering synthetic heteroligation systems with precise control over recognition motif orientation and distance remains challenging. Here, a DNA framework-based strategy is presented to program heterotypic binding by spatially organizing bivalent aptamers targeting platelet-derived growth factor-BB (PDGF-BB). By systematically tuning the distance between heterotypic recognition motifs, programmable modulation of binding affinity is demonstrated. The resulting heteroligation exhibits rapid binding kinetics, high specificity, and ultrasensitive detection of PDGF-BB, with a limit of detection reaching 0.5 pM. This approach provides a versatile platform for designing multivalent interactions, with potential applications in disease diagnostics and immunotherapy for cancer, viral infections, and allergic disorders.

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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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