A novel strigolactone receptor antagonist provides insights into the structural inhibition, conditioning, and germination of the crop parasite Striga
Bibliographic record
Abstract
Crop parasites of the Striga genera are a major biological deterrent to food security in Africa and are one of the largest obstacles to poverty alleviation on the continent. Striga seeds germinate by sensing small-molecule hormones, strigolactones (SLs), that emanate from host roots. Although SL receptors (Striga hermonthica HYPOSENSITIVE TO LIGHT [ShHTL]) have been identified, discerning their function has been difficult because these parasites cannot be easily grown under laboratory conditions. Moreover, many Striga species are obligate outcrossers that are not transformable, hence not amenable to genetic analysis. By combining phenotypic screening with ShHTL structural information and hybrid drug discovery methods, we discovered a potent SL perception inhibitor for Striga, dormirazine (DOZ). Structural analysis of this piperazine-based antagonist reveals a novel binding mechanism, distinct from that of known SLs, blocking access of the hormone to its receptor. Furthermore, DOZ reduces the flexibility of protein-protein interaction domains important for receptor signaling to downstream partners. In planta, we show, via temporal additions of DOZ, that SL receptors are required at a specific time during seed conditioning. This conditioning is essential to prime seed germination at the right time; thus, this SL-sensitive stage appears to be critical for adequate receptor signaling. Aside from uncovering a function for ShHTL during seed conditioning, these results suggest that future Ag-Biotech Solutions to Striga infestations will need to carefully time the application of antagonists to exploit receptor availability and outcompete natural SLs, critical elements for successful parasitic plant invasions.
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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) | 0.002 | 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".