Recent Progress in the Chemical Synthesis of Class II and S-Glycosylated Bacteriocins
Bibliographic record
Abstract
A wide variety of antimicrobial peptides produced by lactic acid bacteria have been identified and studied in the last decades. Known as bacteriocins, these ribosomally-synthesized peptides inhibit the growth of a wide range of bacterial species through numerous mechanisms and show a great variety of spectrum of activity. With their great potential as antimicrobial additives and alternatives to traditional antibiotics in food preservation and handling, animal production and in veterinary and medical medicine, the demand for bacteriocins is rapidly increasing. Bacteriocins are most often produced by fermentation but, in several cases, the low isolated yields and difficulties associated with their purification seriously limit their use on a large scale. Chemical synthesis has been proposed for their production and recent advances in peptide synthesis methodologies have allowed the preparation of several bacteriocins. Moreover, the significant cost reduction for peptide synthesis reagents and building blocks has made chemical synthesis of bacteriocins more attractive and competitive. From a protein engineering point of view, the chemical approach offers many advantages such as the possibility to rapidly perform amino acid substitution, use unnatural or modified residues, and make backbone and side chain modifications to improve potency, modify the activity spectrum or increase the stability of the targeted bacteriocin. This review summarized synthetic approaches that have been developed and used in recent years to allow the preparation of class IIa bacteriocins and S-linked glycopeptides from lactic acid bacteria. Synthetic strategies such as the use of pseudoprolines, backbone protecting groups, microwave irradiations, selective disulfide bridge formation and chemical ligations to prepare class II and S-glycosylsated bacteriocins are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".