LEAfing through literature: late embryogenesis abundant proteins coming of age—achievements and perspectives
Bibliographic record
Abstract
To deal with increasingly severe periods of dehydration related to global climate change, it becomes increasingly important to understand the complex strategies many organisms have developed to cope with dehydration and desiccation. While it is undisputed that late embryogenesis abundant (LEA) proteins play a key role in the tolerance of plants and many anhydrobiotic organisms to water limitation, the molecular mechanisms are not well understood. In this review, we summarize current knowledge of the physiological roles of LEA proteins and discuss their potential molecular functions. As these are ultimately linked to conformational changes in the presence of binding partners, post-translational modifications, or water deprivation, we provide a detailed summary of current knowledge on the structure-function relationship of LEA proteins, including their disordered state in solution, coil to helix transitions, self-assembly, and their recently discovered ability to undergo liquid-liquid phase separation. We point out the promising potential of LEA proteins in biotechnological and agronomic applications, and summarize recent advances. We identify the most relevant open questions and discuss major challenges in establishing a solid understanding of how these intriguing molecules accomplish their tasks as cellular sentinels at the limits of surviving water scarcity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".