Oligomer Dynamics of LL‐37 Truncated Fragments Probed by <i>α</i>‐Hemolysin Pore and Molecular Simulations
Bibliographic record
Abstract
Oligomerization of antimicrobial peptides (AMPs) is critical in their effects on pathogens. LL-37 and its truncated fragments are widely investigated regarding their structures, antimicrobial activities, and application, such as developing new antibiotics. Due to the small size and weak intermolecular interactions of LL-37 fragments, it is still elusive to establish the relationship between oligomeric states and antimicrobial activities. Here, an α-hemolysin nanopore, mass spectrometry (MS), and molecular dynamic (MD) simulations are used to characterize the oligomeric states of two LL-37 fragments. Nanopore studies provide evidence of trapping events related to the oligomer formation and provide further details on their stabilities, which are confirmed by MS and MD simulations. Furthermore, simulation results reveal the molecular basis of oligomer dynamics and states of LL-37 fragments. This work provides unique insights into the relationship between the oligomer dynamics of AMPs and their antimicrobial activities at the single-molecule level. The study demonstrates how integrating methods allows deciphering single molecule level understanding from nanopore sensing approaches.
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 | 0.000 |
| 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) | 0.001 | 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".