Computational Design of Potent and Selective <scp>d</scp>-Peptide Agonists of the Glucagon-like Peptide-2 Receptor
Bibliographic record
Abstract
Here, we designed three d-GLP-2 agonists that activated the glucagon-like peptide-2 receptor (GLP-2R) cyclic adenosine monophosphate (cAMP) accumulation without stimulating the glucagon-like peptide-1 receptor (GLP-1R). All the d-GLP-2 agonists increased the protein kinase B phosphorylated (p-AKT) expression levels in a time- and concentration-dependent manner in vitro. The most effective d-GLP-2 analogue boosted the AKT phosphorylation 2.28 times more effectively compared to the native l-GLP-2. The enhancement in the p-AKT levels induced by the d-GLP-2 analogues could be explained by GLP-2R's more prolonged activation, given that the d-GLP-2 analogues induce a lower β-arrestin recruitment. The higher stability to protease degradation of our d-GLP-2 agonists helps us envision their potential applications in enhancing intestinal absorption and treating inflammatory bowel illness while lowering the high dosage required by the current treatments.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".