Diverse high-affinity DNA aptamers for wild-type and B.1.1.7 SARS-CoV-2 spike proteins from a pre-structured DNA library
Bibliographic record
Abstract
We performed in vitro selection experiments to identify DNA aptamers for the S1 subunit of the SARS-CoV-2 spike protein (S1 protein). Using a pool of pre-structured random DNA sequences, we obtained over 100 candidate aptamers after 13 cycles of enrichment under progressively more stringent selection pressure. The top 10 sequences all exhibited strong binding to the S1 protein. Two aptamers, named MSA1 (Kd = 1.8 nM) and MSA5 (Kd = 2.7 nM), were assessed for binding to the heat-treated S1 protein, untreated S1 protein spiked into 50% human saliva and the trimeric spike protein of both the wildtype and the B.1.1.7 variant, demonstrating comparable affinities in all cases. MSA1 and MSA5 also recognized the pseudotyped lentivirus of SARS-CoV-2 with respective Kd values of 22.7 pM and 11.8 pM. Secondary structure prediction and sequence truncation experiments revealed that both MSA1 and MSA5 adopted a hairpin structure, which was the motif pre-designed into the original library. A colorimetric sandwich assay was developed using MSA1 as both the recognition element and detection element, which was capable of detecting the pseudotyped lentivirus in 50% saliva with a limit of detection of 400 fM, confirming the potential of these aptamers as diagnostic tools for COVID-19 detection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".