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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".