An Unbiased Chemical Proteomics Method Identifies FabI as the Primary Target of 6-OH-BDE-47
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it