Insights into the mechanism of membrane fusion induced by the plant defense element, plant-specific insert
Bibliographic record
Abstract
In plants, many natural defense mechanisms include cellular membrane fusion as a way to resist infection by external pathogens. Several plant proteins mediate membrane fusion, but the detailed mechanism by which they promote fusion is less clear. Understanding this process could provide valuable insights into these proteins' physiological functions and guide bioengineering applications ( i.e. the design of antimicrobial proteins). The plant-specific insert (PSI) from Solanum tuberosum can help reduce certain pathogen attack via membrane fusion. To gain new insights into the process of PSI-induced membrane fusion, a combined approach of NMR, FRET, and in silico studies was used. Our results indicate that (i) under acidic conditions, the PSI experiences a monomer-dimer equilibrium, and the dimeric PSI induces membrane fusion below a certain critical pH; (ii) after fusion, the PSI resides in a highly dehydrated environment with limited solvent accessibility, suggesting its capability in reducing repulsive dehydration forces between liposomes to facilitate fusion; and (iii) as shown by molecular dynamics simulations, the PSI dimer can bind stably to membrane surfaces and can bridge liposomes in close proximity, a critical step for the membrane fusion. In summary, this study provides new and unique insights into the mechanisms by which the PSI and similar proteins induce membrane fusion.
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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) | 0.001 | 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".