Optimizing dimodular nonribosomal peptide synthetases and natural dipeptides in an <i>Escherichia coli</i> heterologous host
Bibliographic record
Abstract
Nonribosomal peptides are an important class of natural products that have a broad range of biological activities. Their structural complexity often prevents simple chemical synthesis, and production from the natural producer is often low, which deters pharmaceutical development. Expression of biosynthetic machinery in heterologous host organisms like Escherichia coli is one way to access these structures, and subsequent optimization of these systems is critical for future development. We utilized the aureusimine biosynthetic gene cluster as a model system to identify the optimal conditions to produce nonribosomal peptides in the isopropyl β-d-1-thiogalactopyranoside (IPTG)-inducible T7 promoter system of pET28. Single reaction monitoring of nonribosomal products was used to find the optimal concentration of IPTG, postinduction temperature, and the effect of amino acid precursor supplementation. In addition, principle component analysis of these extracts identified 3 previously undiscovered pyrazine products of the aureusimine biosynthetic locus, highlighting the utility of heterologously expressing nonribosomal peptide synthetases to find new products.
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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 | 0.001 | 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".