Corilagin and 1,3,6-Tri-<i>O</i>-galloy-β-<scp>d</scp>-glucose: potential inhibitors of SARS-CoV-2 variants
Bibliographic record
Abstract
The COVID-19 disease caused by the virus SARS-CoV-2, first detected in December 2019, is still emerging through virus mutations. Although almost under control in some countries due to effective vaccines that are mitigating the worldwide pandemic, the urgency to develop additional vaccines and therapeutic treatments is imperative. In this work, the natural polyphenols corilagin and 1,3,6-tri-O-galloy-β-d-glucose (TGG) are investigated to determine the structural basis of inhibitor interactions as potential candidates to inhibit SARS-CoV-2 viral entry into target cells. First, the therapeutic potential of the ligands are assessed on the ACE2/wild-type RBD. We first use molecular docking followed by molecular dynamics, to take into account the conformational flexibility that plays a significant role in ligand binding and that cannot be captured using only docking, and then analyze more precisely the affinity of these ligands using MMPBSA binding free energy. We show that both ligands bind to the ACE2/wild-type RBD interface with good affinities which might prevent the ACE2/RBD association. Second, we confirm the potency of these ligands to block the ACE2/RBD association using a combination of surface plasmon resonance and biochemical inhibition assays. These experiments confirm that TGG and, to a lesser extent, corilagin, inhibit the binding of RBD to ACE2. Both experiments and simulations show that the ligands interact preferentially with RBD, while weak binding is observed with ACE2, hence, avoiding potential physiological side-effects induced by the inhibition of ACE2. In addition to the wild-type RBD, we also study numerically three RBD mutations (E484K, N501Y and E484K/N501Y) found in the main SARS-CoV-2 variants of concerns. We find that corilagin could be as effective for RBD/E484K but less effective for the RBD/N501Y and RBD/E484K-N501Y mutants, while TGG strongly binds at relevant locations to all three mutants, demonstrating the significant interest of these molecules as potential inhibitors for variants of SARS-CoV-2.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".