Abscisic Acid Analogues That Act as Universal or Selective Antagonists of Phytohormone Receptors
Bibliographic record
Abstract
The plant hormone abscisic acid (ABA) plays many important roles in controlling plant development and physiology, from flowering to senescence. ABA is now known to exert its effects through a family of soluble ABA receptors, which in Arabidopsis thaliana has 13 members divided into three clades. Homologues of these receptors are present in other plants, also in relatively large numbers. Investigation of the roles of each homologue in mediating the diverse physiological roles of ABA is hampered by this genetic redundancy. We report herein the in vitro screening of a targeted ABA-like analogue library and identification of novel antagonist hits, including the analogue PBI686 that had been developed previously as a probe for identifying ABA-binding proteins. Further in vitro characterization of PBI686 and development of second-generation leads yielded both receptor-selective and universal antagonist hits. In planta assays in different species have demonstrated that these antagonist leads can overcome various ABA-induced physiological changes. While the general antagonists open up a hitherto unexplored avenue for controlling plant growth through inhibition of ABA-regulated physiological processes, the receptor-selective antagonist can be developed into chemical probes to explore the physiological roles of individual receptors.
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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 | 0.001 |
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".