Phosphoenolpyruvate Carboxykinase: Possible Therapeutic Targets for Insulin Resistant Type‐II Diabetes
Bibliographic record
Abstract
Maintaining blood glucose levels is not just the responsibility of insulin and glucagon but also of a rate‐controlling enzyme known as Phosphoenolpyruvate Carboxykinase (PEPCK). This molecule plays a central role in glucose homeostasis by controlling hepatic gluconeogenesis. Developing functional inhibitors of PEPCK would be useful in the treatment of diabetes and the model of PEPCK reveals several sites of interest. Cytosolic, mammalian PEPCK is a GTP‐dependent enzyme that catalyzes the rate‐controlling step in gluconeogenesis: conversion of oxaloacetate to phosphoenolpyruvate. Two divalent metal ions are required for enzyme activity. The positively charged active site creates an electrostatic environment tailored to stabilize the localized negative charge of the enzyme's substrates. There is no consensus on the order in which substrates bind but upon binding, a mobile lid domain (Ω‐loop) closes, causing the substrates to be positioned closer to one another. Only upon closure of the lid, does another structural feature, the P‐loop, move which repositions the GTP molecule. Finally, the N‐ and C‐terminal lobes move inward, All of these steps are required for catalysis to occur and so are possible targets for the inactivation of PEPCK. The Ashbury College SMART team (Students Modeling A Research Topic) used Jmol interactive software and 3D printed using Zcorp plaster modeling to create PEPCK.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".