Antibiotic Dereplication Using the Antibiotic Resistance Platform
Bibliographic record
Abstract
One of the main challenges in the search for new antibiotics from natural product extracts is the re-discovery of common compounds. To address this challenge, dereplication, which is the process of identifying known compounds, is performed on samples of interest. Methods for dereplication such as analytical separation followed by mass spectrometry are time-consuming and resource-intensive. To improve the dereplication process, we have developed the antibiotic resistance platform (ARP). The ARP is a library of approximately 100 antibiotic resistance genes that have been individually cloned into Escherichia coli. This strain collection has many applications, including a cost-effective and facile method for antibiotic dereplication. The process involves the fermentation of antibiotic-producing microbes on the surface of rectangular Petri dishes containing solid medium, thereby allowing for the secretion and diffusion of secondary metabolites through the medium. After a 6 day fermentation period, the microbial biomass is removed, and a thin agar-overlay is added to the Petri dish to create a smooth surface and enable the growth of the E. coli indicator strains. Our collection of ARP strains is then pinned onto the surface of the antibiotic-containing Petri dish. The plate is next incubated overnight to allow for E. coli growth on the surface of the overlay. Only strains containing resistance to a specific antibiotic (or class) grow on this surface enabling rapid identification of the produced compound. This method has been successfully used for the identification of producers of known antibiotics and as a means to identify those producing novel compounds.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".