Developing inhibitors of the guanosine triphosphate hydrolysis accelerating activity of Regulator of G protein Signaling-14
Bibliographic record
Abstract
Regulator of G protein Signaling-14 (RGS14), an intracellular inactivator of G protein-coupled receptor (GPCR) signaling, is considered an undruggable protein, given its shallow and relatively featureless protein-protein interaction interface combined with a distal allosteric site prone to nonspecific inhibition by thiol-reactive compounds. Here, we identify and validate a tractable chemotype that selectively and non-covalently inhibits RGS14 GTPase-accelerating protein (GAP) activity. Combining structure-guided virtual screening, ligand docking across multiple receptor conformers, and enrichment validation, we progressed from a first-generation active, Z90276197, to over 40 second-generation analogs with improved potency. These inhibitors are predicted to engage the solvent-exposed "canyon" in the RGS14 RGS-box that interacts with the Gα switch I region. Binding pose predictions underscored the importance of non-polar interactions and shape complementarity over polar interactions in engaging this Gα-binding canyon and revealed an "ambidextrous" pattern of R1-and R2-group orientations. GAP inhibition was confirmed in fluorescence-based and gold-standard radioactive GTP hydrolysis assays. Two second-generation analogs, Z55660043 and Z55627844, inhibited RGS14 GAP activity in both assays and without measurable cytotoxicity. Deep learning-based scoring of predicted docking poses further supported observed affinity gains from R3-group additions. One analog demonstrated favorable in vivo pharmacokinetics and CNS penetration. Collectively, our findings establish tractable, non-covalent, small molecule inhibition of a G protein regulatory interface and illustrate how machine learning-enhanced docking can guide ligand optimization for shallow protein surfaces. This work opens the door to future development of RGS14 inhibitors as potential therapeutics for central nervous system and metabolic 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".