Using the Wimley–White Hydrophobicity Scale as a Direct Quantitative Test of Force Fields: The MARTINI Coarse-Grained Model
Bibliographic record
Abstract
The partitioning of proteins and peptides at the membrane/water interface is a key step in many processes, including the action of antimicrobial peptides, cell-penetrating peptides, and toxins, as well as signaling. To develop a computational model that can be used to accurately represent such systems, the underlying model must be able to quantitatively represent the partitioning preferences of amino acids in the lipid membrane. The MARTINI model provides a consistent set of parameters for building coarse-grained models of systems involving lipids and proteins. Even though MARTINI is parametrized to reproduce the partitioning behavior of small molecules, its ability to reproduce partitioning preferences of amino acids at lipid/water interfaces has never been tested. In this study, we measured the partitioning free energies of side chains of amino acids using alchemical simulations and umbrella sampling. The pentapeptides of sequence Ac-WLXLL were simulated at the POPC/water and cyclohexane/water interfaces using MARTINI, and the computed free energies were compared with the Wimley-White hydrophobicity scale. The free energy values obtained using the free energy perturbation, thermodynamic integration, and umbrella sampling methods were compared to gain insight into the most efficient method and the degree of sampling required to obtain statistically accurate free energies for use with atomistic force fields in future work. With the standard MARTINI water model, the amino acids D, E, K, and R were found to be significantly too favorable in hydrophobic environments, whereas with the polarizable water model, the amino acids D, E, K, and R were found to give correct free energies of partitioning. The amino acids P and F showed significant deviations from the experimental values. This model system will be used in future improvements to the MARTINI model.
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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 | 0.001 | 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".