Interaction of an Antimicrobial Peptide with a Model Lipid Bilayer Using Molecular Dynamics Simulation
Bibliographic record
Abstract
We present results of molecular dynamics simulations of the interaction of a positively charged antimicrobial peptide, carnobacteriocin B2, with a mixed (anionic-zwitterionic) lipid bilayer carrying a net negative charge. When the peptide is initially immersed in an aqueous medium, it approaches the bilayer surface because of electrostatic attraction. Insertion of the single peptide in the bilayer, however, is not spontaneous. Simulations are also conducted by employing initial configurations where the peptide is partially or completely inserted into the bilayer. When the peptide is partially inserted into the bilayer, it experiences a slight loss of helical structure with the appearance of a hinge region in the C-terminal helix. Complete insertion of the peptide in the bilayer results in a stable straight helix with the N- and C-terminals electrostatically tethered to the opposing headgroups of the bilayer. The charged amino acids of the peptide do not cross the charged headgroups of the bilayer in any of the simulations, nor is any bilayer disruption observed in these studies. These results show that single peptides do not spontaneously penetrate lipid membranes and corroborate deductions from previous experimental studies that alternate mechanisms are necessary for their penetration into lipid bilayers.
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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) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".