In Silico and In Vitro Approach for Validating the Inhibition of Matrix Metalloproteinase-9 by Quercetin
Bibliographic record
Abstract
PURPOSE: To validate the mechanism and inhibitory activity of quercetin against matrix metalloproteinase-9 (MMP-9) using a hybrid in silico and in vitro approach. METHODS: The structure of MMP-9 was obtained from the Protein Data Bank, and the active site was identified using previous annotations from the Universal Protein Resource. The structure of quercetin was obtained from ZINC15. Molecular docking was performed to quantify the binding affinity of quercetin to the active site of MMP-9. The inhibitory effect of various concentrations of quercetin (0.0025, 0.025, 0.25, 1.0, and 1.5 mM) on MMP-9 was quantified using a commercially available fluorometric assay. The cytotoxicity of quercetin to immortalized human corneal epithelial cells (HCECs) was quantified by obtaining the metabolic activities of the cells exposed to various concentrations of quercetin for 24 hr. RESULTS: Quercetin interacts with MMP-9 by binding within the active site pocket and interacting with residues LEU 188, ALA 189, GLU 227, and MET 247. The binding affinity predicted by molecular docking was -9.9 kcal/mol. All concentrations of quercetin demonstrated significant inhibition of MMP-9 enzyme activity (all P <0.03). There was little to no reduction of HCEC metabolic activity after a 24-hr exposure to all concentrations of quercetin ( P >0.99). CONCLUSIONS: Quercetin inhibited MMP-9 in a dose-dependent manner and was well-tolerated by HCECs, suggesting a potential role in therapy for diseases with upregulated MMP-9 as part of its pathogenesis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".