Using a high-throughput, whole-cell hydrogenase assay to identify potential small molecule inhibitors of [NiFe]-hydrogenase
Bibliographic record
Abstract
[NiFe]-hydrogenases are used by several human pathogens to catalyze the reversible conversion between molecular hydrogen and protons and electrons. Hydrogenases provide an increased metabolic flexibility for pathogens, such as Escherichia coli and Helicobacter pylori, by allowing the use of molecular hydrogen as an energy source to promote survival in anaerobic environments. With the rise of antimicrobial resistance and the desire for novel therapeutics, the [NiFe]-hydrogenases are alluring targets. Inhibiting the nickel insertion pathway of [NiFe]-hydrogenases is attractive as this pathway is required for the generation of functional enzymes and is orthogonal to human biochemistry. In this work, nickel availability for the production and function of E. coli [NiFe]-hydrogenase was explored through immunoblot and activity assays. Whole-cell hydrogenase activities were assayed in high throughput against a small molecule library of known bioactives. Iodoquinol was identified as a potential inhibitor of the nickel biosynthetic pathway of [NiFe]-hydrogenase through a two-step screening process, but further studies with immunoblot assays showed confounding effects dependent on the cell growth phase. This study highlights the significance of considering the growth phenotype for whole-cell based assays overall and its effects on various cellular processes influenced by metal trafficking and homeostasis.
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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 | 0.001 |
| 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) | 0.001 | 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".