Antimicrobial Peptide Simulations and the Influence of Force Field on the Free Energy for Pore Formation in Lipid Bilayers
Bibliographic record
Abstract
Due to antimicrobial resistance, the development of new drugs to combat bacterial and fungal infections is an important area of research. Nature uses short, charged, and amphipathic peptides for antimicrobial defense, many of which disrupt the lipid membrane in addition to other possible targets inside the cell. Computer simulations have revealed atomistic details for the interactions of antimicrobial peptides and cell-penetrating peptides with lipid bilayers. Strong interactions between the polar interface and the charged peptides can induce bilayer deformations - including membrane rupture and peptide stabilization of a hydrophilic pore. Here, we performed microsecond-long simulations of the antimicrobial peptide CM15 in a POPC bilayer expecting to observe pore formation (based on previous molecular dynamics simulations). We show that caution is needed when interpreting results of equilibrium peptide-membrane simulations, given the length of time single trajectories can dwell in local energy minima for 100's of ns to microseconds. While we did record significant membrane perturbations from the CM15 peptide, pores were not observed. We explain this discrepancy by computing the free energy for pore formation with different force fields. Our results show a large difference in the free energy barrier (ca. 40 kJ/mol) against pore formation predicted by the different force fields that would result in orders of magnitude differences in the simulation time required to observe spontaneous pore formation. This explains why previous simulations using the Berger lipid parameters reported pores induced by charged peptides, while with CHARMM based models pores were not observed in our long time-scale simulations. We reconcile some of the differences in the distance dependent free energies by shifting the free energy profiles to account for thickness differences between force fields. The shifted curves show that all the models describe small defects in lipid bilayers in a consistent manner, suggesting a common physical basis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".