Spike protein disulfide disruption as a potential treatment for SARS-CoV-2
Bibliographic record
Abstract
Abstract The coronaviral pandemic is exerting a tremendously detrimental impact on global health, quality of life and the world economy, emphasizing the need for effective medications for current and future coronaviral outbreaks as a complementary approach to vaccines. The Spike protein, responsible for cell receptor binding and viral internalization, possesses multiple disulfide bonds raising the possibility that disulfide-reducing agents might disrupt Spike function, prevent viral entry and serve as effective drugs against SARS-CoV-2. Here we show the first experimental evidence that reagents capable of reducing disulfide bonds can inhibit viral infection in cell-based assays. Molecular dynamics simulations of the Spike receptor-binding domain (RBD) predict increased domain flexibility when the four disulfide bonds of the domain are reduced. This flexibility is particularly prominent for the surface loop, comprised of residues 456-490, which interacts with the Spike cell receptor ACE2. Consistent with this finding, the addition of exogenous disulfide bond reducing agents affects the RBD secondary structure, lowers its melting temperature from 52 to 36-39°C and decreases its binding affinity to ACE2 by two orders of magnitude at 37°C. Finally, the reducing agents dithiothreitol (DTT) and tris (2-carboxyethyl)phosphine (TCEP) inhibit viral replication at high µM – low mM levels with a negligible effect on cell viability at these concentrations. The antiviral effect of monothiol-based reductants N-Acetyl-L-cysteine (NAC) and reduced glutathione (GSH) was not observed due to decreases in cell viability. Our research demonstrates the clear potential for medications that disrupt Spike disulfides as broad-spectrum anticoronaviral agents and as a first-line defense against current and future outbreaks.
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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".