In Silico Evaluation of Hexamethylene Amiloride Derivatives as Potential Luminal Inhibitors of SARS-CoV-2 E Protein
Bibliographic record
Abstract
The coronavirus E proteins are small membrane proteins found in the virus envelope of alpha and beta coronaviruses that have a high degree of overlap in their biochemical and functional properties despite minor sequence variations. The SARS-CoV-2 E is a 75-amino acid transmembrane protein capable of acting as an ion channel when assembled in a pentameric fashion. Various studies have found that hexamethylene amiloride (HMA) can inhibit the ion channel activity of the E protein in bilayers and also inhibit viral replication in cultured cells. Here, we use the available structural data in conjunction with homology modelling to build a comprehensive model of the E protein to assess potential binding sites and molecular interactions of HMA derivatives. Furthermore, we employed an iterative cycle of molecular modelling, extensive docking simulations, molecular dynamics and leveraging steered molecular dynamics to better understand the pore characteristics and quantify the affinity of the bound ligands. Results from this work highlight the potential of acylguanidines as blockers of the E protein and guide the development of subsequent small molecule 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".