Revealing pH-dependence and independence of the characteristics of a <i>β</i> sheet-forming antimicrobial <i>peptide</i> <sup>†</sup>
Bibliographic record
Abstract
Membrane-active antimicrobial peptides (AMPs) are a promising potential solution to combat rising antimicrobial resistance (AMR) due to their selective interaction with negatively charged bacterial membranes, but their behavior is controlled by their charge states, which in turn depend on the local pH in which they find themselves. In this study, we employ constant pH molecular dynamics (CpHMD) simulations to investigate the pH-dependent behavior of a 13-residue-long positively charge AMP, GL13K, focusing on the deprotonation states of lysine residues in a single GL13K AMP and their impact on its structural dynamics. We determine pK a values of the critical lysine residues and show that the last lysine located near the C-terminus (LYS11) has a significant deprotonation difference with other lysine residues. We observe that increasing the pH results in changes in the metastable conformational states including collapse of the peptide and highlight the stabilization of a potentially therapeutically-relevant β hairpin configuration in pH levels leading to partial protonation of the lysines. Overall, our study shows the pH-dependent conformational dynamics and pK a variations of lysine residues in the GL13K antimicrobial peptide, providing critical insights into its structural behavior in solution. These findings establish a necessary rigorous foundation for further exploration of GL13K in more complex systems, advancing its potential development as an antimicrobial agent.
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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 | 0.000 |
| 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 | 0.001 |
| 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".