Lessons from assembling a microbial natural product and pre-fractionated extract library in an academic laboratory
Bibliographic record
Abstract
Abstract Microbial natural products are specialized metabolites that are sources of many bioactive compounds including antibiotics, antifungals, antiparasitics, anticancer agents, and probes of biology. The assembly of libraries of producers of natural products has traditionally been the province of the pharmaceutical industry. This sector has gathered significant historical collections of bacteria and fungi to identify new drug leads with outstanding outcomes—upwards of 60% of drug scaffolds originate from such libraries. Despite this success, the repeated rediscovery of known compounds and the resultant diminishing chemical novelty contributed to a pivot from this source of bioactive compounds toward more tractable synthetic compounds in the drug industry. The advent of advanced mass spectrometry tools, along with rapid whole genome sequencing and in silico identification of biosynthetic gene clusters that encode the machinery necessary for the synthesis of specialized metabolites, offers the opportunity to revisit microbial natural product libraries with renewed vigor. Assembling a suitable library of microbes and extracts for screening requires the investment of resources and the development of methods that have customarily been the proprietary purview of large pharmaceutical companies. Here, we report a perspective on our efforts to assemble a library of natural product-producing microbes and the establishment of methods to extract and fractionate bioactive compounds using resources available to most academic labs. We validate the library and approach through a series of screens for antimicrobial and cytotoxic agents. This work serves as a blueprint for establishing libraries of microbial natural product producers and bioactive extract fractions suitable for screens of bioactive compounds. One-Sentence Summary Natural products are key to discovery of novel antimicrobial agents: Here, we describe our experience and lessons learned in constructing a microbial natural product and pre-fractionated extract library.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".