Structure–activity relationships of de novo designed cyclic antimicrobial peptides based on gramicidin S
Bibliographic record
Abstract
The cyclic beta-sheet structure possessed by the 10-residue antibiotic peptide gramicidin S was taken as the structural framework for the de novo design of biologically active peptides with membrane-active properties. We have shown from previous studies that gramicidin S is a broad-spectrum antibiotic effective against Gram-positive bacteria, Gram-negative bacteria, and fungi, but is toxic to human red blood cells. We tested the effect of ring size on antimicrobial activity and hemolytic activity on peptides varying from 4 to 16 residues. Interestingly, we were able to dissociate hemolytic activity and antimicrobial activity by increasing the ring size of the peptide to 14 residues (peptide GS14). Furthermore, we increased specificity for microbial membranes while decreasing toxicity to red blood cells by substituting enantiomers (D-amino acids for L-amino acids and vice versa) into the GS14 sequence. The enantiomeric substitutions all disrupted beta-sheet structure in benign medium and decreased peptide amphipathicity. The least amphipathic peptide, produced by substituting a D-Lys at position 4 of GS14 (peptide GS14K4), also had the highest therapeutic index, i.e., highest degree of specificity for microbial cells over human cells. Solution structures of GS14 analogs solved by NMR spectroscopy showed that the D-amino acid side chain was located on the nonpolar face of GS14K4. Another analog, a beta-sheet peptide with reduced amphipathicity (peptide GS14 K3L4), also had a lysine (lysine 3) on the nonpolar face as determined by the NMR structure. Both GS14K4 and GS14 K3L4 had reduced amphipathicity relative to GS14 and much higher therapeutic indices. Finally, the alteration of the nonpolar face hydrophobicity of GS14K4 analogs provided a range of activities and specificities, where the peptides with the intermediate hydrophobicities among the series had the highest therapeutic indices. The optimal peptide hydrophobicities varied depending on the microorganism being tested, with higher hydrophobicity requirements against Gram-positive bacteria and yeast compared with Gram-negative microorganisms. The net result of these studies suggests that it is possible to rationally design a cyclic membrane-active antimicrobial peptide with high specificity towards prokaryotic (bacterial and fungal) membranes and minimal toxicity to eukaryotic (e.g., mammalian) membranes.
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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 | 0.000 |
| 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".