Dual Inhibition of HIV-1 and Cathepsin L Proteases by Sarcandra glabra
Bibliographic record
Abstract
The COVID-19 pandemic continues to impose a huge threat on human health due to rapid viral mutations. Thus, it is imperative to develop more potent antivirals with both prophylactic and treatment functions. In this study, we screened for potential antiviral compounds from Sarcandra glabra (SG) against Cathepsin L and HIV-1 proteases. A FRET assay was applied to investigate the inhibitory effects and UPLC-HRMS was employed to identify and quantify the bioactive components. Furthermore, molecular docking was carried out to get a glimpse of the binding of active compounds to the proteases. Our results showed that the SG extracts (SGW, SG30, SG60, and SG85) inhibited HIV-1 protease with an IC50 of 0.003~0.07 mg/mL and Cathepsin L protease with an IC50 of 0.11~0.26 mg/mL. Fourteen compounds were identified along with eight quantified from the SG extracts. Chlorogenic acid, which presented in high content in the extracts (12.7~15.76 µg/mg), possessed the most potent inhibitory activity against HIV-1 protease (IC50 = 0.026 mg/mL) and Cathepsin L protease (inhibition: 40.8% at 0.01 mg/mL). Thus, SG extracts and the active ingredients could potentially be used to prevent/treat viral infections, including SARS-CoV-2, due to their dual-inhibition functions against viral proteases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".