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http://hdl.handle.net/11375/30281
Title: | A DUAL RANDOM DOMAIN LIBRARY STRATEGY FOR APTAMER SELECTIONS |
Other Titles: | A DUAL RANDOM DOMAIN LIBRARY STRATEGY FOR APTAMER SELECTIONS: TARGETING SARS-COV-2 SPIKE PROTEIN AS A MODEL FOR PANDEMIC PREVENTION |
Authors: | Amini, Ryan |
Advisor: | Li, Yingfu |
Department: | Biochemistry and Biomedical Sciences |
Keywords: | aptamers;DNA;molecular recognition;diagnostics |
Publication Date: | 2024 |
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 strategy that directly selects for multimeric aptamers is highly desirable. Here, we report on an in vitro selection strategy 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, our library contains two random regions separated by a flexible poly-thymidine (poly-T) 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 found with our library displayed Kd values as low as 0.15 nM, which is consistent with other reported dimeric aptamers. As confirmed via dot blot analysis, each random region functions as a distinct binding moiety, but the regions work together to recognize the spike protein. Our library strategy provides an accelerated method to obtain high-binding dimeric aptamers, which may prove useful in future aptamer diagnostic and therapeutic applications. |
URI: | http://hdl.handle.net/11375/30281 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Amini_Ryan_202409_MSc.pdf | 2.41 MB | Adobe PDF | View/Open |
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