Free energy of WALP23 dimer association in DMPC, DPPC, and DOPC bilayers
Bibliographic record
Abstract
The MARTINI coarse-grained model is used to gain insight into the association of WALP23 helices in three different lipid membranes: DMPC, DPPC and DOPC. Potentials of mean force describing the association of two WALP23 helices embedded in different lipid bilayers indicate no barrier of association and a stabilization of more than 20 kJ mol(-1) of the associated state relative to the fully dissociated state. Association is strongest in DMPC, followed by DPPC and DOPC. Helix-helix association appears to be enthalpically favorable in all lipid bilayers, while the entropic contribution appears favorable only in the presence of significant positive hydrophobic mismatch, in DMPC lipids. The interpretation of this requires care given the coarse-grained nature of the simulations, but the sign of the thermodynamic quantities agrees with experimental measurements on dimerization of (AALALAA)3 peptides and the observed association free energies are within the experimental range. Both protein-protein and lipid-lipid interactions appear to strongly favor protein dimerization, while the interactions between a dimer and lipid are unfavorable relative to the interactions between two separated monomers and lipids. Dimers with antiparallel orientation appear to be thermodynamically favored over parallel dimers, particularly in conditions of greater hydrophobic mismatch, but elucidating the detailed origin of this likely requires simulations of helices for which there is structural data on the dimer. We analyze 3D density, membrane order, and membrane thickness maps using new freely available analysis programs. Although these properties differ somewhat for each lipid, perturbations extend to about 1 nm for lipid density, ~2 nm for ordering and ~2.5 nm for thickness. A striking feature is the appearance and extent of systematic density fluctuations around the helices.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".