Aptamer Development for SARS-CoV-2 and Omicron Variants Using the Spike Protein Receptor Binding Domain as a Potential Diagnostic Tool and Therapeutic Agent
Bibliographic record
Abstract
Despite various methods for detecting and treating SARS-CoV-2, affordable and easily applicable solutions are still needed. Aptamers can potentially fill this gap. Here, we establish a workflow to identify aptamers that bind to the spike proteins of SARS-CoV-2, a process applicable to other targets as well. The spike protein is crucial for the virus's entry into host cells. The aptamer development process for the spike protein's receptor binding domain (RBD) begins with splitting the SARS-CoV-2's genome into 40 nucleotide-long sequences, predicting their two-dimensional structure, and sorting based on the free energy. Selected oligomers undergo three-dimensional structure prediction and docking onto the viral spike protein's RBD. Six RNA oligomers were identified as top candidates based on the RNA docking with the SARS-CoV-2 wild-type (WT) (Wuhan-Hu-1 strain) and Omicron variant BA.1 RBD and molecular dynamics simulations. Three oligomers also demonstrated strong predicted binding affinity with other SARS-CoV-2 variants, including BA.2, XBB.1.5, and EG.5, based on the protein-aptamer docking followed by stability evaluation using the MD simulations. The aptamer with the best fit for the spike protein RBD was later validated using biolayer interferometry. The process has resulted in identifying a single aptamer from a library of 29,000 RNA oligomers, which exhibited affinity in the submicromolar range and the potential to develop into a viral screen or therapeutic.
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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".