Computational studies on structural modifications for the inhibition of matrix metalloproteinase activities by luteolin
Bibliographic record
Abstract
Many experimental studies have previously found that flavonoids including luteolin can inhibit the activities of matrix metalloproteinases (MMPs), but the related theoretical studies are rather lacking. In this paper, based on our recently obtained interaction mechanisms between luteolin and the catalytic zinc ion in MMPs (see J. Phys. Org. Chem. 2012, 25, 1306), we perform PM6 quantum chemistry calculations together with modeling of ligand−water exchange reactions to investigate the relevant structural modifications for the inhibition of MMP activities by luteolin. The calculations indicate that among the possible modified positions of A, B, and C rings of the luteolin molecule, 5, 7, 2′, 3′, and 4′ should be the five suitable modified positions, and the several usual substituent groups that have stronger electron-donating abilities should be the suitable substituent groups. We further find that with the increasing number of these substituent groups, the biological activities for the modified luteolin molecules on MMP inhibition can be obviously improved. Our calculated results are in agreement with previous relevant experimental results. This paper gives a new approach for designing the new MMP inhibitors having higher biological activities by carrying out the structural modifications of the luteolin molecule.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".