Dimeric DNA Aptamers for the Spike Protein of SARS-CoV-2 Derived from a Structured Library with Dual Random Domains.

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Ryan Amini, Jian Ma, Zijie Zhang, Qing Wang, Jimmy Gu, Leyla Soleymani, Yingfu Li
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引用次数: 0

Abstract

Multimeric aptamer strategies are often adopted to improve the binding affinity of an aptamer toward its target molecules. In most cases, multimeric aptamers are constructed by connecting pre-identified monomeric aptamers derived from in vitro selection. Although multimerization provides an added benefit of enhanced binding avidity, the characterization of different aptamer pairings adds more steps to an already lengthy procedure. Therefore, an aptamer engineering strategy that directly selects for multimeric aptamers is highly desirable. Here, an in vitro selection strategy is reported on using a pre-structured DNA library that forms dimeric aptamers. Rather than using a library containing a single random region, which is nearly ubiquitous in existing aptamer selections, the library contains two random regions separated by a flexible poly-thymidine linker. Following sixteen rounds of selection against the SARS-CoV-2 spike protein, a relevant model target protein due to the COVID-19 pandemic, the top aptamers displayed superb affinity with KD values as low as 150 pM. Further analysis reveals that each random region functions as a distinct binding moiety and works together to achieve higher affinity. The demonstrated strategy provides an accelerated method to obtain high-affinity aptamers, which may prove useful in future aptamer diagnostic and therapeutic applications.

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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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