Molecular modeling of the methylene blue interaction with the SARS-CoV-2 coronavirus viroporin
Bibliographic record
Abstract
Viroporins are small membrane proteins of enveloped viruses. They play an important role both in the life cycle of the virus and in the development of disease pathogenesis. In this regard, inhibition of viroporins is considered a promising strategy in the treatment of many diseases caused by enveloped viruses, such as coronavirus, herpes virus, human immunodeficiency virus, Ebola virus and many others. An important step in the search for highly effective inhibitors of such channels is the study of the interaction of potential antiviral drugs with the amino acid residues of the viroporin channel. In turn, methylene blue is a well-known effective antiviral agent and is widely used in medical practice. In this work, we carried out molecular dynamic calculations of the interaction of methylene blue with the viroporin channel of the SARS-CoV-2 coronavirus using the umbrella sampling method. Analysis of the contacts formed between the methylene blue molecule and the amino acid residues of viroporin showed that the key role in binding is played by non-covalent stacking interactions between the system of aromatic rings of methylene blue and the phenylalanine residues located in the center of the viroporin channel. The results obtained bring us closer to understanding the mechanisms of the antiviral action of methylene blue. Conducting such computational experiments seems to be an effective approach in the search for viroporin inhibitors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".