The CRY1-HsF predicted interaction interface serves as a molecular platform for bioengineering or selecting modulating mutants
Bibliographic record
Abstract
Introduction: High-temperature stress imposes an energetic cost on plant growth and negatively impacts agricultural productivity. This stress rapidly triggers the activation of Heat shock Factor (HsF) proteins, a family of transcription factors that maintain proteostasis. The cryptochrome CRY1 can physically interact with HsFA1d proteins to facilitate nucleus translocation and the regulation of genes that contributes to stress tolerance. Methods: We combined structural predictions with experimental testing using yeast-two-hybrid and bimolecular fluorescence complementation assays. Results: We confirm that CRY1 PHR domain interacts extensively with multiple HsF proteins through their HR-A region of the conserved oligomerization HR-A/B domain interface. This interaction partially relies on salt bridges provided by the N- and C-terminus of HR-A region and a conserved interface centered around W352 of CRY1. HsFA3 shows the strongest affinity to CRY1 in yeast-two-hybrid assays notably thanks to a glutamate residue that interacts with R211 and R435 of CRY1. Mutating equivalent residue positions within HsFA1e or HsFC1 to a glutamate increased their interaction to CRY1. Discussion: Overall, our analysis allowed the identification of mutant candidate that could be used in selection or bioengineering endeavors to improve thermal stress tolerance.
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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) | 0.003 | 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".