Crystal structure of fatty acid thioesterase A bound by 129 fragments provides diverse development opportunities
Bibliographic record
Abstract
Abstract BACKGROUND In order to alleviate the growing issue of herbicide resistance, diversification of the herbicide portfolio is necessary. A promising yet underutilized mode‐of‐action is the inhibition of fatty acid thioesterases (FATs), which terminate de novo fatty acid (FA) biosynthesis by releasing FAs from acyl carrier protein (ACP) cofactors. These enzymes impact plant growth and sterility by determining the amount and length of FAs present. In this study we report a crystallographic fragment screening approach for the identification of novel chemical matter targeting FATs. RESULTS We have solved the crystal structure of Arabidopsis thaliana FatA to 1.5 Å and conducted a crystallographic fragment screen which identified 129 unique fragments bound in 141 different poses. Ten fragments demonstrated on‐scale potency, two of these exploiting different interactions to known herbicides. Elaboration of one of the fragments resulted in an improvement of affinity from ~20 μ m to ~90 n m K D . Finally, superposition of our crystal structures revealed that some fragments exploit large conformational changes in the substrate binding site. CONCLUSION We have fully enabled FatA as a target for rapid, rational hit‐to‐lead development, with robust structural, biophysical and biochemical assays. We provide a set of fragment hits which represent diverse, novel scaffolds that both recapitulate interactions made by current herbicides, and also target novel regions within the active and dimer sites. Our fragments can be readily merged and allow for effective catalogue‐based structure–activity relationship (SAR) exploration. Together these data will accelerate the development of novel, alternative herbicides to combat herbicide resistance. © 2025 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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) | 0.000 | 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) | 0.003 | 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 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".