Novel synthetic anti-microbial defensins through confrontational selection and screening of yeast libraries
Bibliographic record
Abstract
A are frequently used in animal feed to boost its efficacy. However, recent policies in EU and USA have restricted such use of antibiotics and Canada is expected to follow suit. Consequently, the need for novel anti-microbial agents is a most urgent issue. Defensins form a large group of secreted animal, plant or fungal peptides that kill a broad spectrum of microorganisms including food borne pathogens. They have a low probability of developing microbial resistance and are viewed as viable alternatives to antibiotics for both the food and pharmaceutical industries. The industrial development of vertebrate defensins is hindered by concerns of cytotoxicity. In contrast, the known fungal defensins show little side effects in animals and work at doses comparable to these of many antibiotics. We are developing a platform that will use DNA libraries for a huge variety of synthetic defensin-like peptides. We are expressing these libraries in yeast and screen them against model microorganisms. Our aim is to identify novel synthetic anti-microbial peptides that can be used as alternatives to antibiotics. These agents can be improved to incorporate a trypsin-target site to ensure their normal degradation in the stomach and to abolish any side effects on the normal microbiome of the animals. We also aim at the affordable production of such agents by the yeast K. lactis in milk whey. Our progress in these screens will be reported and discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".