Assembling the glycopeptide antibiotic scaffold: The biosynthesis of from <i>Streptomyces toyocaensis</i> NRRL15009
Bibliographic record
Abstract
The glycopeptide antibiotics vancomycin and teicoplanin are vital components of modern anti-infective chemotherapy exhibiting outstanding activity against Gram-positive pathogens including members of the genera Streptococcus, Staphylococcus, and Enterococcus. These antibiotics also provide fascinating examples of the chemical and associated biosynthetic complexity exploitable in the synthesis of natural products by actinomycetes group of bacteria. We report the sequencing and annotation of the biosynthetic gene cluster for the glycopeptide antibiotic from Streptomyces toyocaensis NRRL15009, the first complete sequence for a teicoplanin class glycopeptide. The cluster includes 34 ORFs encompassing 68 kb and includes all of the genes predicted to be required to synthesize and regulate its biosynthesis. The gene cluster also contains ORFs encoding enzymes responsible for glycopeptide resistance. This role was confirmed by insertional inactivation of the d-Ala-d-lactate ligase, vanAst, which resulted in the predicted -sensitive phenotype and impaired antibiotic biosynthesis. These results provide increased understanding of the biosynthesis of these complex natural products.
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) | 0.000 | 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 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".