The interaction of the severe acute respiratory syndrome coronavirus 2 spike protein with drug-inhibited angiotensin converting enzyme 2 studied by molecular dynamics simulation
Bibliographic record
Abstract
BACKGROUND: Hypertension has been identified as the most common comorbidity in coronavirus disease 2019 (COVID-19) patients, and has been suggested as a risk factor for COVID-19 disease outcomes. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters host human cells via binding to host cell angiotensin-converting enzyme 2 (ACE2) receptors. Inhibition of ACE2 has been proposed as a potential therapeutic approach to block SARS-CoV-2 contagion. However, some experts suggest that ACE2 inhibition could worsen the infection. Here, we aimed to study the effect of ACE2 inhibition on the SARS-CoV-2 spike protein binding to ACE2. METHOD: Crystallographic structures of the SARS-CoV-2 spike protein, the spike receptor-binding domain, native ACE2, and the ACE2 complexed with MLN-4760 were used as the study model structures. The spike proteins were docked to the ACE2 structures and the dynamics of the complexes, ligand-protein, and protein-protein interactions were studied by molecular dynamics simulation for 100 ns. RESULTS: Our result showed that inhibition of ACE2 by MLN-4760 increased the affinity of the SARS-CoV-2 spike protein binding to ACE2. Results also revealed that spike protein binding to the ACE2 inhibited by MLN-4760 restored the enzymatic active conformation of the ACE2 from closed/inactive to open/active conformation by removing MLN-4760 binding from the ligand-binding pocket of ACE2. CONCLUSION: We conclude that using ACE2 inhibitors can increase the risk of SARS-CoV-2 infection and worsen COVID-19 disease outcome. We also found that the SARS-CoV-2 can abrogate the function of ACE2 inhibitors and rescue the enzymatic activity of ACE2. Therefore, ACE2 inhibition is not a useful treatment against COVID-19 infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".