Investigating the Biosynthesis of Natural Products from Marine Proteobacteria: A Survey of Molecules and Strategies
Bibliographic record
Abstract
The phylum proteobacteria contains a wide array of Gram-negative marine bacteria. With recent advances in genomic sequencing, genome analysis, and analytical chemistry techniques, a whole host of information is being revealed about the primary and secondary metabolism of marine proteobacteria. This has led to the discovery of a growing number of medically relevant natural products, including novel leads for the treatment of multidrug-resistant Staphylococcus aureus (MRSA) and cancer. Of equal interest, marine proteobacteria produce natural products whose structure and biosynthetic mechanisms differ from those of their terrestrial and actinobacterial counterparts. Notable features of secondary metabolites produced by marine proteobacteria include halogenation, sulfur-containing heterocycles, non-ribosomal peptides, and polyketides with unusual biosynthetic logic. As advances are made in the technology associated with functional genomics, such as computational sequence analysis, targeted DNA manipulation, and heterologous expression, it has become easier to probe the mechanisms for natural product biosynthesis. This review will focus on genomics driven approaches to understanding the biosynthetic mechanisms for natural products produced by marine proteobacteria.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".