Curcumin Protects Membranes through a Carpet or Insertion Model Depending on Hydration
Bibliographic record
Abstract
Curcumin is the main ingredient in turmeric, a common Indian spice. Curcumin shows a broad spectrum of effects, including anti-Alzheimer's and antioxidant properties. An interaction between curcumin and lipid membranes has been speculated as the root cause of this activity, and the molecule is often proposed to protect the bilayer. However, the detailed molecular mechanism of this protection is disputed. There is evidence that curcumin either (a) lies flat on the bilayer and provides a "carpet" for protection by forming a steric barrier, or (b) inserts into the membrane and stiffens tails, thereby protecting against peptide insertion. We studied the interaction between curcumin and 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) bilayers at different concentrations using high-resolution X-ray diffraction and molecular dynamics (MD) computer simulations. We observed curcumin molecules forming a carpet in dehydrated bilayers, whereas in hydrated membranes the curcumin molecules were found to insert into the bilayers. From calculations of the potential of mean force (PMF), we find two minima, a metastable state in the headgroup region, at |z| ≈ 22 Å, and a global minimum in the hydrophobic membrane core, at |z| ≈ 9 Å. The population of the two states depends on membrane hydration. Experiments may thus observe curcumin in a carpet or inserted position, depending on the osmotic pressure conditions created, for instance, by salts, buffer solutions, substrates, or macromolecular solutes. In the carpet model, curcumin dehydrates lipid bilayers and decreases fluidity. When inserted, curcumin leads to a further fluidification of the membranes and an increase in tail fluctuations, contrary to cholesterol's condensing effect.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".