Comparison of a Short Linear Antimicrobial Peptide with Its Disulfide-Cyclized and Cyclotide-Grafted Variants against Clinically Relevant Pathogens
Bibliographic record
Abstract
According to the World Health Organization (WHO) the development of resistance against antibiotics by microbes is one of the most pressing health concerns. The situation will intensify since only a few pharmacological companies are currently developing novel antimicrobial compounds. Discovery and development of novel antimicrobial compounds with new modes of action are urgently needed. Antimicrobial peptides (AMPs) are known to be able to kill multidrug-resistant bacteria and, therefore, of interest to be developed into antimicrobial drugs. Proteolytic stability and toxicities of these peptides are challenges to overcome, and one strategy frequently used to address stability is cyclization. Here we introduced a disulfide-bond to cyclize a potent and nontoxic 9mer peptide and, in addition, as a proof-of-concept study, grafted this peptide into loop 6 of the cyclotide MCoTI-II. This is the first time an antimicrobial peptide has been successfully grafted onto the cyclotide scaffold. The disulfide-cyclized and grafted cyclotide showed moderate activity in broth and strong activity in 1/5 broth against clinically relevant resistant pathogens. The linear peptide showed superior activity in both conditions. The half-life time in 100% human serum was determined, for the linear peptide, to be 13 min, for the simple disulfide-cyclized peptide, 9 min, and, for the grafted cyclotide 7 h 15 min. The addition of 10% human serum led to a loss of antimicrobial activity for the different organisms, ranging from 1 to >8-fold for the cyclotide. For the disulfide-cyclized version and the linear version, activity also dropped to different degrees, 2 to 18-fold, and 1 to 30-fold respectively. Despite the massive difference in stability, the linear peptide still showed superior antimicrobial activity. The cyclotide and the disulfide-cyclized version demonstrated a slower bactericidal effect than the linear version. All three peptides were stable at high and low pH, and had very low hemolytic and cytotoxic activity.
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".