In Silico and In Vitro Analyses of Multiple Terpenes Predict Cryptotanshinone as a Potent Inhibitor of the Omicron Variant of SARS-CoV-2
Bibliographic record
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant (B.1.1.529) underwent a substantial number of alterations, and the accompanying structural mutations in the spike protein prompted questions about the virus’s propensity to evade the antibody neutralization produced by prior infection or vaccination. New mutations in SARS-CoV-2 have raised serious concerns regarding the effectiveness of drugs and vaccines against the virus; thus, identifying and developing potent antiviral medications is crucial to combat viral infections. In the present study, we conducted a detailed in silico investigation that involves molecular docking, density functional (DFT) analysis, molecular dynamics (MD) simulations, and pharmacological analysis followed by an in vitro study with the spike protein. Among fifty terpenes screened, cryptotanshinone and saikosaponin B2 were found to be potent S1-RBD spike protein inhibitors, displaying considerable hydrogen bond interactions with key binding site residues, significant binding affinity, and high reactivity attributed to band gap energy. In addition, 100 ns molecular dynamics (MD) simulations further substantiated these findings, showcasing the stability of the compounds within a biological environment. With favorable pharmacokinetic properties and a low half inhibitory concentration (IC50) of 86.06 ± 1.56 μM, cryptotanshinone inhibited S1-RBD of the SARS-CoV-2 Omicron variant. Our findings account for in-depth research on cryptotanshinone as a SARS-CoV-2 inhibitor.
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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 | 0.000 | 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 | 0.000 |
| 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".