Regulators of G-Protein Signaling and Their G Substrates: Promises and Challenges in Their Use as Drug Discovery Targets
Bibliographic record
Abstract
Because G-protein coupled receptors (GPCRs) continue to represent excellent targets for the discovery and development of small-molecule therapeutics, it is posited that additional protein components of the signal transduction pathways emanating from activated GPCRs themselves are attractive as drug discovery targets. This review considers the drug discovery potential of two such components: members of the “regulators of G-protein signaling” (RGS protein) superfamily, as well as their substrates, the heterotrimeric G-protein α subunits. Highlighted are recent advances, stemming from mouse knockout studies and the use of “RGS-insensitivity” and fast-hydrolysis mutations to Gα, in our understanding of how RGS proteins selectively act in (patho)physiologic conditions controlled by GPCR signaling and how they act on the nucleotide cycling of heterotrimeric G-proteins in shaping the kinetics and sensitivity of GPCR signaling. Progress is documented regarding recent activities along the path to devising screening assays and chemical probes for the RGS protein target, not only in pursuits of inhibitors of RGS domain-mediated acceleration of Gα GTP hydrolysis but also to embrace the potential of finding allosteric activators of this RGS protein action. The review concludes in considering the Gα subunit itself as a drug target, as brought to focus by recent reports of activating mutations to GNAQ and GNA11 in ocular (uveal) melanoma. We consider the likelihood of several strategies for antagonizing the function of these oncogene alleles and their gene products, including the use of RGS proteins with Gαq selectivity.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".