Analysis of the Contribution of Saturated and Polyunsaturated Phospholipid Monolayers to the Binding of Proteins
Bibliographic record
Abstract
The binding of peripheral proteins to membranes results in different biological effects. The large diversity of membrane lipids is thought to modulate the activity of these proteins. However, information on the selective binding of peripheral proteins to membrane lipids is still largely lacking. Lipid monolayers at the air/water interface are useful model membrane systems for studying the parameters responsible for peripheral protein membrane binding. We have thus measured the maximum insertion pressure (MIP) of two proteins from the photoreceptors, Retinitis pigmentosa 2 (RP2) and recoverin, to estimate their binding to lipid monolayers. Photoreceptor membranes have the unique characteristic that more than 60% of their fatty acids are polyunsaturated, making them the most unsaturated natural membranes known to date. These membranes are also thought to contain significant amounts of saturated phospholipids. MIPs of RP2 and recoverin have thus been measured in the presence of saturated and polyunsaturated phospholipids. MIPs higher than the estimated lateral pressure of biomembranes have been obtained only with a saturated phospholipid for RP2 and with a polyunsaturated phospholipid for recoverin. A new approach was then devised to analyze these data properly. In particular, a parameter called the synergy factor allowed us to highlight the specificity of RP2 for saturated phospholipids and recoverin for polyunsaturated phospholipids as well as to demonstrate clearly the preference of RP2 for saturated phospholipids that are known to be located in microdomains.
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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 | 0.001 |
| 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".