Systematic <i>in vitro</i> optimization of antimicrobial peptides against <i>Escherichia coli</i>
Bibliographic record
Abstract
Abstract Objectives Antimicrobial resistance is a growing concern and claims over 1 million lives per year. The discovery of new antimicrobial drugs is expensive and often generates low profitability, with very low success rates. One way to combat this is by the improvement of known antimicrobials, such as antimicrobial peptides (AMPs). The aim of this study was to improve the antimicrobial activities of two known AMPs, UyCT3 and indolicidin, with the use of peptide libraries and growth curves. Methods Peptide permutation libraries were synthesized for two AMPs, indolicidin and UyCT3, which included 520 peptides. These peptides were subsequently tested against MG1655-K12, to which subsequent peptide design was performed, then tested against three clinically Gram-negative relevant drug-resistant isolates. Best-performing candidates were subjected to a haemolysis assay for toxicity validation. Results Single amino acid permutations of UyCT3 and indolicidin were sufficient to inhibit growth of MG1655-K12, and subsequent generations of peptide design were able to inhibit growth of clinical isolates at concentrations as low as 5 µM. Our best-performing AMP, UyCT3I5A, W6Y, K10I, F13I, was not seen to be toxic towards sheep RBCs. Conclusions The efficacy of the AMPs improved with the use of our peptide library technology, whereby an AMP was found that inhibited bacterial growth of clinical Gram-negative isolates 4-fold better than its WT counterpart.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".