Bacterial Aggregation Triggered by Fibril Forming Tryptophan-Rich Sequences: Effects of Peptide Side Chain and Membrane Phospholipids
Bibliographic record
Abstract
The influence of side chain residue and phospholipid characteristics of the cytoplasmic membrane upon the fibrillation and bacterial aggregation of arginine (Arg) and tryptophan (Trp) rich antimicrobial peptides (AMPs) has not been well described to date. Here, we utilized the structural advantages of HHC-10 and 4Har HHC-10 (Har, l -homoarginine) that are highly active Trp-rich AMPs and investigated their fibril formation and activity behavior against bacteria. The peptides revealed time-dependent self-assembly of polyproline II (PPII) α-helices, but by comparison, 4Har HHC-10 tended to form higher ordered fibrils due to relatively strong cation−π stacking of Trp with Har residue. Both peptides rapidly killed S. aureus and E. coli at their MICs and caused aggregation of bacteria at higher concentrations. This bacterial aggregation was accompanied by the formation of morphologically distinct electron-dense nanostructures, likely including but not limited to peptides alone. Both HHC-10-derived peptides caused blebs and buds in the E. coli membrane that are rich in POPE phospholipid that promotes negative curvature. However, the main population of S. aureus cells retained their cocci structure upon treatment with HHC peptides even at concentration higher than the MICs. In contrast, the cell aggregation was not induced by HHC fibrils that were most likely stabilized through intra-/intermolecular cation−π stacking. It is proposed that masking of these interactions might have resulted in diminished membrane association/insertion of the HHC nanostructures. The peptides caused aggregation of POPC/POPG (1/3) and POPE/POPG (3/1) liposomes. Nonetheless, disaggregation of the former vesicles was observed at ratios of lipid to peptide of greater than 6 and 24 for HHC-10 and 4Har HHC-10, respectively. Collectively, our results revealed dose-dependent bacterial aggregation mediated by Trp-rich AMPs that was profoundly influenced by the degree of peptide’s self-association and the composition and intrinsic curvature of the cytoplasmic membrane lipids.
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) | 0.000 | 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 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".