8. Genome Guided Discovery of Chlorinated Natural Products in Streptomyces curacoi
Bibliographic record
Abstract
Natural products derived from plants, animals, and microbes have long been a rich source of molecules that exhibit biological activity. Bacteria of the genus Streptomyces are one of the most important sources of natural products today, producing more than half of all known antibiotics. One class of natural products that have potent antibiotic activity are those that contain halogens. There are many examples of halogenated natural products such as the antibiotics vancomycin and chloramphenicol. Incorporation of halogen atoms into drugs is a common strategy to enhance their bioactivity and specificity. Rapid advances in DNA sequencing have led to genome mining approaches to discover new natural products. This technique can also be used to find bioactive halogenated products by analyzing the genomes for sequences encoding the ‘halogenases’ that are responsible for addition of the halogen. One class of compounds that have biological activity against Streptococcus pneumoniae, Staphylococcus aureus, and Staphylococcus epidermidis are the desotamides. These compounds inhibit bacterial RNA polymerases. A cluster of genes that is likely responsible for the production this compound has been discovered in the Streptomyces curacoi genome; however this cluster is unique in that it also contains a halogenase gene. This study aims to discover a halogenated desotamide derivative from Streptomyces curacoi based on the genomic information. It is hypothesized that this derivative will have enhanced or new biological activity.
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 | 0.001 |
| 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) | 0.001 | 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".