Constant pH simulations with the coarse-grained MARTINI model — Application to oleic acid aggregates
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Bibliographic record
Abstract
Long chain fatty acids are biologically important molecules with complex and pH sensitive aggregation behavior. The carboxylic head group of oleic acid is ionizable, with the pK a shifting to larger values, even above a value of 7, in certain aggregate states. While experiments have determined the macroscopic phase behavior, we have yet to understand the molecular level details for this complex behavior. This level of detail is likely required to fully appreciate the role of fatty acids in biology and for nanoscale biotechnological and industrial applications. Here, we introduce the use of constant pH molecular dynamics (MD) simulations with the coarse-grained MARTINI model and apply the method to oleic acid aggregates and a model lipid bilayer. By running simulations at different constant pH values, we determined titration curves and the resulting pK a for oleic acid in different environments. The coarse-grained model predicts positive pK a shifts, with a shift from 4.8 in water to 6.5 in a small micelle, and 6.6 in a dioleoylphosphatidylcholine (DOPC) bilayer, similar to experimental estimates. The size of the micelles increased as the pH increased, and correlated with the fraction of deprotonated oleic acid. We show this combination of constant pH MD and the coarse-grained MARTINI model can be used to model pH-dependent surfactant phase behavior. This suggests a large number of potential new applications of large-scale MARTINI simulations in other biological systems with ionizable molecules.
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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 it