Peptide Inhibitors Disrupt the Serotonin 5-HT<sub>2C</sub>Receptor Interaction with Phosphatase and Tensin Homolog to Allosterically Modulate Cellular Signaling and Behavior
Bibliographic record
Abstract
Serotonin (5-hydroxytryptamine; 5-HT) signaling through the 5-HT(2C) receptor (5-HT(2C)R) is essential in normal physiology, whereas aberrant 5-HT(2C)R function is thought to contribute to the pathogenesis of multiple neural disorders. The 5-HT(2C)R interacts with specific protein partners, but the impact of such interactions on 5-HT(2C)R function is poorly understood. Here, we report convergent cellular and behavioral data that the interaction between the 5-HT(2C)R and protein phosphatase and tensin homolog (PTEN) serves as a regulatory mechanism to control 5-HT(2C)R-mediated biology but not that of the closely homologous 5-HT(2A)R. A peptide derived from the third intracellular loop of the human 5-HT(2C)R [3L4F (third loop, fourth fragment)] disrupted the association, allosterically augmented 5-HT(2C)R-mediated signaling in live cells, and acted as a positive allosteric modulator in rats in vivo. We identified the critical residues within an 8 aa fragment of the 3L4F peptide that maintained efficacy (within the picomolar range) in live cells similar to that of the 3L4F peptide. Last, molecular modeling identified key structural features and potential interaction sites of the active 3L4F peptides against PTEN. These compelling data demonstrate the specificity and importance of this protein assembly in cellular events and behaviors mediated by 5-HT(2C)R signaling and provide a chemical guidepost to the future development of drug-like peptide or small-molecule inhibitors as neuroprobes to study 5-HT(2C)R allostery and therapeutics for 5-HT(2C)R-mediated disorders.
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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".