Microsecond molecular dynamics simulations revealed the inhibitory potency of amiloride analogs against SARS-CoV-2 E viroporin
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) encodes small envelope protein (E) that plays a major role in viral assembly, release, pathogenesis, and host inflammation. Previous studies demonstrated that pyrazine ring containing amiloride analogs inhibit this protein in different types of coronavirus including SARS-CoV-1 small envelope protein E (SARS-CoV-1 E). SARS-CoV-1 E has 93.42% sequence identity with SARS-CoV-2 E and shared a conserved domain NS3/small envelope protein (NS3_envE). Amiloride analog hexamethylene amiloride (HMA) can inhibit SARS-CoV-1 E. Therefore, we performed molecular docking and dynamics simulations to explore whether amiloride analogs are effective in inhibiting SARS-CoV-2 E. To do so, SARS-CoV-1 E and SARS-CoV-2 E proteins were taken as receptors while HMA and 3-amino-5-(azepan-1-yl)-N-(diaminomethylidene)-6-pyrimidin-5-ylpyrazine-2-carboxamide (3A5NP2C) were selected as ligands. Molecular docking simulation showed higher binding affinity scores of HMA and 3A5NP2C for SARS-CoV-2 E than SARS-CoV-1 E. Moreover, HMA and 3A5NP2C engaged more amino acids in SARS-CoV-2 E. Molecular dynamics simulation for 1 μs (1,000 ns) revealed that these ligands could alter the native structure of the proteins and their flexibility. Our study suggests that suitable amiloride analogs might yield a prospective drug against coronavirus disease 2019.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".