Identification of TRIO‐GEFD1 chemical inhibitors using the yeast exchange assay
Bibliographic record
Abstract
BACKGROUND INFORMATION: Rho GTPases are involved in many biological processes and participate in cancer development. Their activation is catalysed by exchange factors [RhoGEFs (Rho GTPase guanine nucleotide-exchange factor)] of the Dbl family. RhoGEFs display proto-oncogenic features, thus appearing as candidate targets for anticancer drugs. Dominant-negative Rho GTPase mutants have been widely used to block RhoGEF signalling. However, these tools suffer from limitations, due to the high number of RhoGEFs and the complex mechanisms that control Rho GTPase activation. RESULTS: RhoG-T17N is a poor inhibitor of its exchange factor TRIO-GEFD1 (first exchange domain of the exchange factor TRIO) in vivo: although it binds to TRIO-GEFD1, RhoG-T17N does not block the downstream signalling. Using the yeast exchange assay, we show that in the presence of TRIO-GEFD1, RhoG-T17N can bind to its effectors, which illustrates how negative mutants may produce misleading interpretations and emphasizes the need for new types of RhoGEF inhibitors. In that prospect, we adapted the yeast exchange assay method to identify RhoGEF inhibitors. Using this novel approach, we screened a 3500-chemical-compound library and identified a potential inhibitor of TRIO-GEFD1. This molecule inhibited TRIO-GEFD1 in vitro. Among the chemical analogues of this compound, we identified two molecules with better inhibitory activity. The three TRIO-GEFD1 inhibitors had no effect on ARHGEF17 and ARNO [ARF (ADP-ribosylation factor) nucleotide-binding-site opener], two exchange factors for RhoA and Arf1 respectively. CONCLUSIONS: The development of RhoGEF inhibitors appears as a valuable tool for the study of Rho GTPase signalling pathways. The yeast exchange assay adaptation we present here is suitable to screen for chemical or peptide libraries and identify candidate inhibitors.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".