One Solution for All: Searching for Universal Aptamers for Constantly Mutating Spike Proteins of SARS‐CoV‐2
Bibliographic record
Abstract
Aptamers that can recognize the spike (S) protein of SARS-CoV-2 with high affinity and specificity are useful molecules towards the development of diagnostics and therapeutics to fight COVID-19. However, this S protein is constantly mutating, producing variants of concern (VoCs) that can significantly weaken the binding by aptamers initially engineered to recognize the S protein of the wildtype virus or a specific VoC. One strategy to overcome this problem is to develop universal aptamers that are insensitive to all or most of the naturally emerging mutations in the protein. We have recently demonstrated this concept by subjecting a pool of S protein-binding DNA aptamers for one-round parallel-SELEX experiments targeting 5 different S protein variants for binding-based sequence enrichment, followed by bioinformatic analysis of the enriched pools. This effort has led to the identification of a universal aptamer that recognizes 8 different variants of the spike protein with equally excellent affinity.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".