Investigations on the mechanisms of interactions between matrix metalloproteinase 9 and its flavonoid inhibitors using a combination of molecular docking, hybrid quantum mechanical/molecular mechanical calculations, and molecular dynamics simulations
Bibliographic record
Abstract
Many experimental studies have found that flavonoids can inhibit the activities of matrix metalloproteinases (MMPs), but the relevant mechanisms are still unclear. In this paper, the interaction mechanisms of MMP-9 with its five flavonoid inhibitors are investigated using a combination of molecular docking, hybrid quantum mechanical and molecular mechanical (QM/MM) calculations, and molecular dynamics simulations. The molecular dynamics simulation results show a good linear correlation between the calculated binding free energies of QM/MM−Poisson–Boltzmann surface area (PBSA) and the experimental −log(EC50) regarding the studied five flavonoids on MMP-9 inhibition in explicit solvent. It is found that compared with the MM−PBSA method, the QM/MM−PBSA method can obviously improve the accuracy for the calculated binding free energies. The predicted binding modes of the five flavonoid−MMP-9 complexes reveal that the different hydrogen bond networks can form besides producing the Zn−O coordination bonds, which can reasonably explain previous experimental results. The agreement between our calculated results and the previous experimental facts indicates that the force field parameters used here are effective and reliable for investigating the systems of flavonoid−MMP-9 interactions, and thus, these simulations and analyses could be reproduced for the other related systems involving protein−ligand interactions. This paper may be helpful for designing the new MMP-9 inhibitors having higher biological activities by carrying out the structural modifications of flavonoid molecules.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".