Three on Three: Universal and High-Affinity Molecular Recognition of the Symmetric Homotrimeric Spike Protein of SARS-CoV-2 with a Symmetric Homotrimeric Aptamer
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Our previously discovered monomeric aptamer for SARS-CoV-2 (MSA52) possesses a universal affinity for COVID-19 spike protein variants but is ultimately limited by its ability to bind only one subunit of the spike protein. The symmetrical shape of the homotrimeric SARS-CoV-2 spike protein presents the opportunity to create a matching homotrimeric molecular recognition element that is perfectly complementary to its structural scaffold, causing enhanced binding affinity. Here, we describe a branched homotrimeric aptamer with three-fold rotational symmetry, named TMSA52, that not only possesses excellent binding affinity but is also capable of binding several SARS-CoV-2 spike protein variants with picomolar affinity, as well as pseudotyped lentiviruses expressing SARS-CoV-2 spike protein variants with femtomolar affinity. Using Pd–Ir nanocubes as nanozymes in an enzyme-linked aptamer binding assay (ELABA), TMSA52 was capable of sensitively detecting diverse pseudotyped lentiviruses in pooled human saliva with a limit of detection as low as 6.3 × 10 3 copies/mL. The ELABA was also used to test 50 SARS-CoV-2-positive and 60 SARS-CoV-2-negative patient saliva samples, providing sensitivity and specificity values of 84.0 and 98.3%, respectively, thus highlighting the potential of TMSA52 for the development of future rapid tests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".