Faculty Opinions recommendation of Metabolic suppression identifies new antibacterial inhibitors under nutrient limitation.
Bibliographic record
Abstract
Characterizing novel drugs and chemical probes of biological systems is hindered by difficulties in identifying the mechanism of action (MOA) of biologically active molecules.Here we present a metabolite suppression approach to explore the MOA of antibacterial compounds under nutrient restriction.We assembled an array of metabolites that can be screened for suppressors of inhibitory molecules.Further, we identified inhibitors of E. coli growth under nutrient limitation and charted their interactions with our metabolite array.This strategy led to the discovery and characterization of three novel antibacterial compounds, MAC168425, MAC173979 and MAC13772.We showed that MAC168425 interferes with glycine metabolism, MAC173979 is a time-dependent inhibitor of p-aminobenzoic acid biosynthesis and MAC13772 inhibits biotin biosynthesis.We conclude that metabolite suppression profiling is an effective approach to focus MOA studies on compounds impairing metabolic capabilities.Such bioactives can serve as chemical probes of bacterial physiology and as leads for antibacterial drug development.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.037 |
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".