Protein Interactions with Dopamine Receptors as Potential New Drug Targets for Treating Schizophrenia
Bibliographic record
Abstract
One strategy for developing new treatments is to focus on the neural signaling pathways implicated in the pathophysiology of schizophrenia. Dysfunction within the dopamine neurotransmitter system has been widely linked to the pathophysiology of schizophrenia. The classical target of existing antipsychotic medications for schizophrenia is the D2 dopamine receptor (D2R). Most effective antipsychotics for schizophrenia principally antagonize the D2R subtype. The dopamine receptor family is a functionally diverse class of G-protein-coupled receptors (GPCR), present throughout the nervous system. The classical view of GPCR function is that downstream effects are mediated almost exclusively by G-protein-dependent pathways. The recent discovery of interactions between the dopamine receptors and various other receptors and regulatory proteins points to alternative signaling routes. Using yeast two-hybrid, co-immunoprecipitation, glutathione-S-transferase pull-down, and in vitro binding assays, more than 20 dopamine receptor interacting proteins have been determined, many of which are relevant to schizophrenia. These proteins selectively regulate specific signaling pathways and functions of dopamine receptors via protein–protein interactions, without affecting other signaling pathways and dopamine receptor functions. Thus, targeting protein–protein interactions represents a promising alternative treatment strategy for schizophrenia, which might avoid the side-effects of existing antipsychotics that simply block the ligand-binding site of the dopamine receptor. In this chapter, we discuss the proteins that interact with dopamine receptors, regulatory mechanisms for these interactions, and promising avenues for future research into novel drugs for schizophrenia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".