How To Make a Glycopeptide: A Synthetic Biology Approach To Expand Antibiotic Chemical Diversity
Bibliographic record
Abstract
Modification of natural product backbones is a proven strategy for the development of clinically useful antibiotics. Such modifications have traditionally been achieved through medicinal chemistry strategies or via in vitro enzymatic activities. In an orthogonal approach, engineering of biosynthetic pathways using synthetic biology techniques can generate chemical diversity. Here we report the use of a minimal teicoplanin class glycopeptide antibiotic (GPA) scaffold expressed in a production-optimized Streptomyces coelicolor strain to expand GPA chemical diversity. Thirteen scaffold-modifying enzymes from 7 GPA biosynthetic gene clusters in different combinations were introduced into S. coelicolor, enabling us to explore the criteria for in-cell GPA modification. These include identifying specific isozymes that tolerate the unnatural GPA scaffold and modifications that prevent or allow further elaboration by other enzymes. Overall, 15 molecules were detected, 9 of which have not been reported previously. Some of these compounds showed activity against GPA-resistant bacteria. This system allows us to observe the complex interplay between substrates and both non-native and native tailoring enzymes in a cell-based system and establishes rules for GPA synthetic biology and subsequent expansion of GPA chemical diversity.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".