Molecular Interactions of Tannic Acid with Proteins Associated with SARS-CoV-2 Infectivity
Bibliographic record
Abstract
The overall impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on our society is unprecedented. The identification of small natural ligands that could prevent the entry and/or replication of the coronavirus remains a pertinent approach to fight the coronavirus disease (COVID-19) pandemic. Previously, we showed that the phenolic compounds corilagin and 1,3,6-tri-O-galloyl-β-D-glucose (TGG) inhibit the interaction between the SARS-CoV-2 spike protein receptor binding domain (RBD) and angiotensin-converting enzyme 2 (ACE2), the SARS-CoV-2 target receptor on the cell membrane of the host organism. Building on these promising results, we now assess the effects of these phenolic ligands on two other crucial targets involved in SARS-CoV-2 cell entry and replication, respectively: transmembrane protease serine 2 (TMPRSS2) and 3-chymotrypsin like protease (3CLpro) inhibitors. Since corilagin, TGG, and tannic acid (TA) share many physicochemical and structural properties, we investigate the binding of TA to these targets. In this work, a combination of experimental methods (biochemical inhibition assays, surface plasmon resonance, and quartz crystal microbalance with dissipation monitoring) confirms the potential role of TA in the prevention of SARS-CoV-2 infectivity through the inhibition of extracellular RBD/ACE2 interactions and TMPRSS2 and 3CLpro activity. Moreover, molecular docking prediction followed by dynamic simulation and molecular mechanics Poisson-Boltzmann surface area (MMPBSA) free energy calculation also shows that TA binds to RBD, TMPRSS2, and 3CLpro with higher affinities than TGG and corilagin. Overall, these results suggest that naturally occurring TA is a promising candidate to prevent and inhibit the infectivity of SARS-CoV-2.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".