Preferential adsorption to air–water interfaces: a novel cryoprotective mechanism for LEA proteins
Bibliographic record
Abstract
Late embryogenesis abundant (LEA) proteins comprise a diverse family whose members play a key role in abiotic stress tolerance. As intrinsically disordered proteins, LEA proteins are highly hydrophilic and inherently stress tolerant. They have been shown to stabilise multiple client proteins under a variety of stresses, but current hypotheses do not fully explain how such broad range stabilisation is achieved. Here, using neutron reflection and surface tension experiments, we examine in detail the mechanism by which model LEA proteins, AavLEA1 and ERD10, protect the enzyme citrate synthase (CS) from aggregation during freeze-thaw. We find that a major contributing factor to CS aggregation is the formation of air bubbles during the freeze-thaw process. This greatly increases the air-water interfacial area, which is known to be detrimental to folded protein stability. Both model LEA proteins preferentially adsorb to this interface and compete with CS, thereby reducing surface-induced aggregation. This novel surface activity provides a general mechanism by which diverse members of the LEA protein family might function to provide aggregation protection that is not specific to the client protein.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".