Binding free energy and counterion release for adsorption of the antimicrobial peptide lactoferricin B on a POPG membrane
Bibliographic record
Abstract
Molecular dynamics (MD) simulations are used to study the interaction of an anionic palmitoyl-oleoyl-phosphatidylglycerol (POPG) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with a POPG bilayer is employed as a model system for studying the details of membrane adsorption selectivity of cationic antimicrobial peptides. Seventy eight 4 ns MD production run trajectories of the equilibrated system, with six restrained orientations of LFCinB at 13 different separations from the POPG membrane, are generated to determine the free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the profile for this relatively large system, a variant of constrained MD and thermodynamic integration is used. A simplified method for relating the free energy profile to the LFCinB-POPG membrane binding constant is employed to predict a free energy of adsorption of -5.4+/-1.3 kcal/mol and a corresponding maximum adsorption binding force of about 58 pN. We analyze the results using Poisson-Boltzmann theory. We find the peptide-membrane attraction to be dominated by the entropy increase due to the release of counterions and polarized water from the region between the charged membrane and peptide, as the two approach each other. We contrast these results with those found earlier for adsorption of LFCinB on the mammalianlike palmitoyl-oleoyl-phosphatidylcholine membrane.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".