Dimeric DNA Aptamers for the Spike Protein of SARS‐CoV‐2 Derived from a Structured Library with Dual Random Domains
Why this work is in the frame
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Bibliographic record
Abstract
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 K D 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it