Biological Insights of the Dopaminergic Stabilizer ACR16 at the Binding Pocket of Dopamine D2 Receptor
Bibliographic record
Abstract
The dopamine D2 receptor (D2R) plays an important part in the human central nervous system and it is considered to be a focal target of antipsychotic agents. It is structurally modeled in active and inactive states, in which homodimerization reaction of the D2R monomers is also applied. The ASP2314 (also known as ACR16) ligand, a D2R stabilizer, is used in tests to evaluate how dimerization and conformational changes may alter the ligand binding space and to provide information on alterations in inhibitory mechanisms upon activation. The administration of the D2R agonist ligand ACR16 [ 3 H](+)-4-propyl-3,4,4 a,5,6,10 b -hexahydro-2 H -naphtho[1,2- b ][1,4]oxazin-9-ol ((+)PHNO) revealed K i values of 32 nM for the D2 high R and 52 μM for the D2 low R. The calculated binding affinities of ACR16 with post processing molecular dynamics (MD) simulations analyses using MM/PBSA for the monomeric and homodimeric forms of the D2 high R were −9.46 and −8.39 kcal/mol, respectively. The data suggests that the dimerization of the D2R leads negative cooperativity for ACR16 binding. The dimerization reaction of the D2 high R is energetically favorable by −22.95 kcal/mol. The dimerization reaction structurally and thermodynamically stabilizes the D2 high R conformation, which may be due to the intermolecular forces formed between the TM4 of each monomer, and the result strongly demonstrates dimerization essential for activation of the D2R.
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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) | 0.002 | 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".