A High-Throughput Screen of the GTPase Activity of Escherichia coli EngA to Find an Inhibitor of Bacterial Ribosome Biogenesis
Bibliographic record
Abstract
The synthesis of ribosomes is an essential process, which is aided by a variety of trans-acting factors in bacteria. Among these is a group of GTPases essential for bacterial viability and emerging as promising targets for new antibacterial agents. Herein, we describe a robust high-throughput screening process for inhibitors of one such GTPase, the Escherichia coli EngA protein. The primary screen employed an assay of phosphate production in a 384-well density. Reaction conditions were chosen to maximize sensitivity for the discovery of competitive inhibitors while maintaining a strong signal amplitude and low noise. In a pilot screen of 31,800 chemical compounds, 44 active compounds were identified. Furthermore, we describe the elimination of nonspecific inhibitors that were detergent sensitive or reactive as well as those that interfered with the high-throughput phosphate assay. Four inhibitors survived these common counterscreens for nonspecificity, but these chemicals were also inhibitors of the unrelated enzyme dihydrofolate reductase, suggesting that they too were promiscuously active. The high-throughput screen of the EngA protein described here provides a meticulous pilot study in the search for specific inhibitors of GTPases involved in ribosome biogenesis.
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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 | 0.001 | 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 | 0.001 |
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".