Computational and Enzymatic Studies of Sartans in SARS-CoV-2 Spike RBD-ACE2 Binding: The Role of Tetrazole and Perspectives as Antihypertensive and COVID-19 Therapeutics
Bibliographic record
Abstract
This study is an extension of current research into a novel class of synthetic antihypertensive drugs referred to as “bisartans”, which are bis-alkylated imidazole derivatives bearing two symmetric anionic biphenyltetrazoles. Research to date indicates that bisartans are superior to commercially available hypertension drugs, since the former undergo stronger docking to angiotensin-converting enzyme 2 (ACE2). ACE2 is the key receptor involved in SARS-CoV-2 entry, thus initiating COVID-19 infection and in regulating levels of vasoactive peptides such as angiotensin II and beneficial heptapeptides A(1-7) and Alamandine in the renin–angiotensin system (RAS). In previous studies using in vivo rabbit-iliac arterial models, we showed that Na+ or K+ salts of selected Bisartans initiate a potent dose–response inhibition of vasoconstriction. Furthermore, computational studies revealed that bisartans undergo stable binding to the vital interfacial region between ACE2 and the SARS-CoV-2 “receptor binding domain” (i.e., the viral RBD). Thus, bisartan homologs are expected to interfere with SARS-CoV-2 infection and/or suppress disease expression in humans. The primary goal of this study was to investigate the role of tetrazole in binding and the network of amino acids of SARS-CoV-2 Spike RBD-ACE2 complex involved in interactions with sartans. This study would, furthermore, allow the expansion of the synthetic space to create a diverse suite of new bisartans in conjunction with detailed computational and in vitro antiviral studies. A critical role for tetrazole was uncovered in this study, shedding light on the vital importance of this group in the binding of sartans and bisartans to the ACE2/Spike complex. The in silico data predicting an interaction of tetrazole-containing sartans with ACE2 were experimentally validated by the results of surface plasmon resonance (SPR) analyses performed with a recombinant human ACE2 protein.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".