Secondary structure determination of conserved SARS-CoV-2 RNA elements by NMR spectroscopy
Bibliographic record
Abstract
The current pandemic situation caused by the Betacoronavirus SARS-CoV-2 (SCoV2) highlights the need for coordinated research to combat COVID-19. A particularly important aspect is the development of medication. In addition to viral proteins, structured RNA elements represent a potent alternative as drug targets. The search for drugs that target RNA requires their high-resolution structural characterization. Using nuclear magnetic resonance (NMR) spectroscopy, a worldwide consortium of NMR researchers aims to characterize potential RNA drug targets of SCoV2. Here, we report the characterization of 15 conserved RNA elements located at the 5' end, the ribosomal frameshift segment and the 3'-untranslated region (3'-UTR) of the SCoV2 genome, their large-scale production and NMR-based secondary structure determination. The NMR data are corroborated with secondary structure probing by DMS footprinting experiments. The close agreement of NMR secondary structure determination of isolated RNA elements with DMS footprinting and NMR performed on larger RNA regions shows that the secondary structure elements fold independently. The NMR data reported here provide the basis for NMR investigations of RNA function, RNA interactions with viral and host proteins and screening campaigns to identify potential RNA binders for pharmaceutical intervention.
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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".