Parameterization of Palmitoylated Cysteine, Farnesylated Cysteine, Geranylgeranylated Cysteine, and Myristoylated Glycine for the Martini Force Field
Bibliographic record
Abstract
Peripheral membrane proteins go through various post-translational modifications that covalently bind fatty acid tails to specific amino acids. These post-translational modifications significantly alter the lipophilicity of the modified proteins and allow them to anchor to biological membranes. Over 1000 different proteins have been identified to date that require such membrane-protein interactions to carry out their biological functions, including members of the Src and Ras superfamilies that play key roles in cell signaling and carcinogenesis. We have used all-atom simulations with the CHARMM36 force field to parameterize four of the most common post-translational modifications for the Martini 2.2 force field: palmitoylated cysteine, farnesylated cysteine, geranylgeranylated cysteine, and myristoylated glycine. The parameters reproduce the key features of clusters of configurations of the different anchors in lipid membranes as well as the water-octanol partitioning free energies of the anchors, which are crucial for the correct reproduction of the expected biophysical behavior of peripheral membrane proteins at the membrane-water interface. Implementation in existing Martini setup tools facilitates the use of the new parameters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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 | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".