An Unbiased Chemical Proteomics Method Identifies FabI as the Primary Target of 6-OH-BDE-47
Bibliographic record
Abstract
Determination of the physical interactions of environmental chemicals with cellular proteins is important for characterizing biological and toxic mechanism of action. Yet despite the discovery of numerous bioactive natural brominated compounds, such as hydroxylated polybrominated diphenyl ethers (OH-PBDEs), their corresponding protein targets remain largely unclear. Here, we reported a systematic and unbiased chemical proteomics assay (Target Identification by Ligand Stabilization, TILS) for target identification of bioactive molecules based on monitoring ligand-induced thermal stabilization. We first validated the broad applicability of this approach by identifying both known and unexpected proteins bound by diverse compounds (anticancer drugs, antibiotics). We then applied TILS to identify the bacterial target of 6-OH-BDE-47 as enoyl-acyl carrier protein reductase (FabI), an essential and widely conserved enzyme. Using affinity pull-down and in vitro enzymatic assays, we confirmed the potent antibacterial activity of 6-OH-BDE-47 occurs via direct binding and inhibition of FabI. Conversely, overexpression of FabI rescued the growth inhibition of Escherichia coli by 6-OH-BDE-47, validating it as the primary in vivo target. This study documents a chemical proteomics strategy for identifying the physical and functional targets of small molecules, and its potential high-throughput application to investigate the modes-of-action of environmental compounds.
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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) | 0.000 | 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 | 0.001 |
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".