Genome assembly, analysis, and mining of <i>Kocuria flava</i> NIO_001: a thiopeptide antibiotic synthesizing bacterium isolated from marine sponge
Bibliographic record
Abstract
Genome mining has been a key strategy for finding biosynthetic gene clusters (BGCs) coding for secondary metabolites in the recent past. Actinomycetia is among the important bacterial classes found in marine habitats, renowned for producing high-value secondary metabolites. Kocuria is one such gram-positive bacteria that has been reported to produce the potent antibacterial molecule kocurin/PM181104. The objective of this study was to confirm the production of kocurin/PM181104 followed by sequencing, assembly, and mining of the genome of Kocuria flava NIO_001. AntiSMASH analysis predicted the BGCs involved in the production of kocurin along with eight promising secondary metabolite-producing BGCs including non-alpha poly-amino acids like e-polylysin (NAPAA), ribosomally synthesized and post-translationally modified peptide like (RiPP-like), non-ribosomal peptide synthetase like (NRPS-like), NRPS-independent IucA/IucC-like siderophores (NI-siderophore), type III polyketide synthase (T3PKS), ε-Poly-l-lysine (NAPAA), terpene, and betalactone. Kyoto Encyclopedia for Genes and Genomes pathway analysis showed the presence of biosynthetic pathways involved in terpenoid backbone synthesis and the presence of certain hemolysin-like proteins. The present investigation is highly valuable for designing experiments to overproduce this potent antibiotic molecule by using a reverse engineering approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".