Adsorption of an Antimicrobial Peptide on Self-Assembled Monolayers by Molecular Dynamics Simulation
Bibliographic record
Abstract
We present results of molecular dynamics simulations of the interaction of a 48 amino acid peptide, carnobacteriocin B2, with model hydrophobic, anionic, and cationic self-assembled monolayers (SAMs). The model monolayers were formed by placing alkanethiols, HS-(CH(2))(10)-X, where the terminal functional group X was chosen to be CH(3), COO(-), or NH(3)(+). The carboxylate and amine groups were modeled as either fully charged or partially charged. Furthermore, simulations are presented for nanopatterned SAMs consisting of parallel stripes of hydrophobic/anionic and anionic/cationic SAMs. These simulations help elucidate the mechanisms of interaction of the peptide with model surfaces that emulate the chemical heterogeneity of lipid bilayer membranes or peptide nanoarrays. The simulation results depict how the nanoscale chemical heterogeneity of surfaces can simultaneously alter the peptide's interaction with the surface and its secondary structure. Hydrophobic interactions result in the strongest adsorption of the peptide to the monolayer, simultaneously maintaining the structural integrity of the peptide. Electrostatic interactions, on the other hand, tend to enhance the solvation of the peptide, thereby causing radical changes in the secondary structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".