Structure-Activity Investigation of a G Protein-Biased Agonist Reveals Molecular Determinants for Biased Signaling of the D2 Dopamine Receptor
Bibliographic record
Abstract
The dopamine D2 receptor (D2R) is known to elicit effects through activating two major signaling pathways mediated by either G proteins (Gi/o) or beta-arrestins. However, the specific role of each pathway in physiological or therapeutic activities is not known with certainty. One approach to the dissection of these pathways is through the use of drugs that can selectively modulate one pathway versus the other through a mechanism known as functional selectivity or biased signaling. Our laboratory has previously described a G protein signaling-biased agonist, MLS1547, for the D2R using a variety of in vitro functional assays. To further evaluate the biased signaling activity of this compound, we investigated its ability to promote D2R internalization, a process known to be mediated by beta-arrestin. Using multiple cellular systems and techniques, we found that MLS1547 promotes little D2R internalization, which is consistent with its inability to recruit beta-arrestin. Importantly, we validated these results in primary striatal neurons where the D2R is most highly expressed suggesting that MLS1547 will exhibit biased signaling activity in vivo. In an effort to optimize and further explore structure-activity relationships (SAR) for this scaffold, we conducted an iterative chemistry campaign to synthesize and characterize novel analogues of MLS1547. The resulting analysis confirmed previously described SAR requirements for G protein-biased agonist activity and, importantly, elucidated new structural features that are critical for agonist efficacy and signaling bias of the MLS1547 scaffold. One of the most important determinants for G protein-biased signaling is the interaction of a hydrophobic moiety of the compound with a defined pocket formed by residues within transmembrane five and extracellular loop two of the D2R. These results shed new light on the mechanism of biased signaling of the D2R and may lead to improved functionally-selective molecules.
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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".