Fighting Mutations with Mutations: Evolutionarily Adapting a DNA Aptamer for High‐Affinity Recognition of Mutated Spike Proteins of SARS‐CoV‐2
Bibliographic record
Abstract
Abstract An on‐going challenge with COVID‐19, which has huge implications for future pandemics, is the rapid emergence of viral variants that makes diagnostic tools less accurate, calling for rapid identification of recognition elements for detecting new variants caused by mutations. We hypothesize that we can fight mutations of the viruses with mutations of existing recognition elements. We demonstrate this concept via rapidly evolving an existing DNA aptamer originally selected for the spike protein (S‐protein) of wildtype SARS‐CoV‐2 to enhance the interaction with the same protein of the Omicron variants. The new aptamer, MBA5SA1, has acquired 22 mutations within its 40‐nucleotide core sequence and improved its binding affinity for the S‐proteins of diverse Omicron subvariants by >100‐fold compared to its parental aptamer (improved from nanomolar to picomolar affinity). Deep sequencing analysis reveals dynamic competitions among several MBA5SA1 variants in response to increasing selection pressure imposed during in vitro selection, with MBA5SA1 being the final winner of the competition. Additionally, MBA5SA1 was implemented into an enzyme‐linked aptamer binding assay (ELABA), which was applied for detecting Omicron variants in the saliva of infected patients. The assay produced a sensitivity of 86.5 % and a specificity of 100 %, which were established with 83 clinical samples.
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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) | 0.000 | 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) | 0.001 | 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 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".