Bibliographic record
Abstract
Environmental samples are excellent sources of natural products that possess numerous kinds of therapeutic activities. One important family of natural products is the nonribosomal peptides, which includes penicillin, cyclosporin, viomycin and daptomycin (Cubicin). These peptides are made in bacteria or fungi by large enzymes called nonribosomal peptide synthetases (NRPS). NRPSs are true macromolecular machines or nanofactories, with modular assembly-line logic, a complex catalytic cycle, moving parts and many active sites. Visualization of large fragments of NRPSs at various functional states is required to understand the manner in which NRPSs synthesize their important products. Many excellent structural experiments have been performed to date. Recently, we added to the structural knowledge by visualizing the first module of the NRPS, which makes linear gramicidin, a clinical topical antibiotic, in all its major functional states. These experiments show how the individual domains, including an unusual tailoring domain, function together in assembly-line synthesis. Along with the ever-expanding body of biophysical, biochemical and genetic work, this work brings us closer to a fundamental understanding of these natural antibiotic nanofactories, and perhaps the ability to exploit them to produce novel therapeutics.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".