Molecular dynamics investigation of myelin basic protein stability on lipid membranes
Bibliographic record
Abstract
Simulation of proteins and membranes composed of synthetic lipids on computer clusters provides molecular information that complements experimental data. This paper describes molecular dynamics (MD) approaches to study the properties of biological membranes and proteins using the freely available GROMACS package on the C-terminal α-helical peptide of myelin basic protein (MBP). We simulated a mixed membrane – consisting of 2-dimyristoyl-sn-glycero-3-phosphocholine/1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine (DMPC/DMPE), and a pure DMPC membrane, composed of 188 and 248 lipids, respectively – for 200 ns at 309K. The DMPC membrane was approximately three times more fluid compared to the DMPC/DMPE system, with the diffusion coefficients (D) being 0.0207x10-5 cm2/s and 0.0068x10-5 cm2/s, respectively. In addition, we simulated the 14-residue peptide representing the C-terminal α-helical region of murine MBP, with sequence NH2-A141YDAQGTLSKIFKL154-COOH, in both membrane systems for 200 ns. The negatively-charged N-terminal end of the peptide penetrated further into the DMPC bilayer than into the mixed DMPC/DMPE bilayer. Reduced peptide accessibility to a formal positive charge of the DMPC amine ‘N’ atom surrounded by methyl and methylene groups may be the cause [1]. The peptide lost its α-helical structure in DMPC/DMPE but not in the DMPC bilayer. These findings show that membrane composition affects MBP’s interaction with it, a phenomenon that provides insights into myelin structure – and that may eventually be relevant to understanding the pathogenesis of multiple sclerosis (MS).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".