Aptamer-based approaches for sensitive detection and epitope mapping of SARS-CoV-2 spike protein
Bibliographic record
Abstract
The SARS-CoV-2 spike (S) protein, crucial for viral entry, remains a key target for diagnostics and therapeutics amid evolving variants. Here, we describe the selection and characterization of novel DNA aptamers targeting the S1 subunit, including the Omicron strain, via systematic evolution of ligands by exponential enrichment (SELEX) and biolayer interferometry (BLI). Three aptamers-AptS1-tSP4, AptS1-tSP10, and AptS1-tSP11-exhibited nanomolar dissociation constants (14-59 nM), with AptS1-tSP10 demonstrating good selectivity over MERS-CoV and robust binding in human saliva and pseudovirus samples. Integration with proximity ligation assay and qPCR (PLA-qPCR) achieved a detection limit of 3 pM, surpassing many antibody-based methods. Mass spectrometry-based epitope mapping identified the receptor-binding domain (RBD) peptide VGGNYNYLYR as the primary binding site for AptS1-tSP10. Molecular dynamics and quantum mechanics simulations revealed stable interactions through hydrogen bonding and π-π stacking with neutral residues in both open and closed spike conformations, independent of variant mutations. These multifunctional aptamers offer a versatile platform for ultrasensitive, epitope-specific SARS-CoV-2 detection and pave the way for nucleic acid-based therapeutics to combat viral infections.
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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) | 0.000 | 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 | 0.001 | 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".