Insulin–Mimetic Dihydroxanthyletin-Type Coumarins from Angelica decursiva with Protein Tyrosine Phosphatase 1B and α-Glucosidase Inhibitory Activities and Docking Studies of Their Molecular Mechanisms
Bibliographic record
Abstract
As a traditional medicine, Angelica decursiva has been used for the treatment of many diseases. The goal of this study was to evaluate the potential of four natural major dihydroxanthyletin-type coumarins—(+)-trans-decursidinol, Pd-C-I, Pd-C-II, and Pd-C-III—to inhibit the enzymes, protein tyrosine phosphatase 1B (PTP1B) and α-glucosidase. In the kinetic study of the PTP1B enzyme’s inhibition, we found that (+)-trans-decursidinol, Pd-C-I, and Pd-C-II led to competitive inhibition, while Pd-C-III displayed mixed-type inhibition. Moreover, (+)-trans-decursidinol exhibited competitive-type, and Pd-C-I and Pd-C-II mixed-type, while Pd-C-III showed non-competitive type inhibition of α-glucosidase. Docking simulations of these coumarins showed negative binding energies and a similar proximity to residues in the PTP1B and α-glucosidase binding pocket, which means they are closely connected and strongly binding with the active enzyme site. In addition, dihydroxanthyletin-type coumarins are up to 40 µM non-toxic in HepG2 cells and have substantially increased glucose uptake and decreased expression of PTP1B in insulin-resistant HepG2 cells. Further, coumarins inhibited ONOO−-mediated albumin nitration and scavenged peroxynitrite (ONOO−), and reactive oxygen species (ROS). Our overall findings showed that dihydroxanthyletin-type coumarins derived from A. decursiva is used as a dual inhibitor for enzymes, such as PTP1B and α-glucosidase, as well as for insulin susceptibility.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".