Combination of the CHARMM27 force field with united‐atom lipid force fields
Bibliographic record
Abstract
Computer simulations offer a valuable way to study membrane systems, from simple lipid bilayers to large transmembrane protein complexes and lipid-nucleic acid complexes for drug delivery. Their accuracy depends on the quality of the force field parameters used to describe the components of a particular system. We have implemented the widely used CHARMM22 and CHARMM27 force fields in the GROMACS simulation package to (i) combine the CHARMM22 protein force field with two sets of united-atom lipids parameters; (ii) allow comparisons of the lipid CHARMM27 force field with other lipid force fields or lipid-protein force field combinations. Our tests do not show any particular issue with the combination of the all-atom CHARMM22 force field with united-atoms lipid parameters, although pertinent experimental data are lacking to assess the quality of the lipid-protein interactions. The conversion utilities allow automatic generation of GROMACS simulation files with CHARMM nucleic acids and protein parameters and topologies, starting from pdb files using the standard GROMACS pdb2gmx method. CMAP is currently not implemented.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".