Chemical-genetic approaches for exploring the mode of action of natural products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Determining the mode of action of bioactive compounds, including natural products, is a central problem in chemical biology. Because many genes are conserved from the yeast Saccharomyces cerevisiae to humans and a number of powerful genomics tools and methodologies have been developed for this model system, yeast is making a major contribution to the field of chemical genetics. The set of barcoded yeast deletion mutants, including the set of approximately 5000 viable haploid and homozygous diploid deletion mutants and the complete set of approximately 6000 heterozygous deletion mutants, containing the set of approximately 1000 essential genes, are proving highly informative for identifying chemical-genetic interactions and deciphering compound mode of action. Gene deletions that render cells hypersensitive to a specific drug identify pathways that buffer the cell against the toxic effects of the drug and thereby provide clues about both gene and compound function. Moreover, compounds that show similar chemical-genetic profiles often perturb similar target pathways. Gene dosage can be exploited to discover connections between compounds and their targets. For example, haploinsufficiency profiling of an antifungal compound, in which the set of approximately 6000 heterozygous diploid deletion mutants are scored for hypersensitivity to a compound, may identify the target directly. Creating deletion mutant collections in other fungal species, including the major human fungal pathogen Candida albicans, will expand our chemical genomics tool set, allowing us to screen for antifungal lead drugs directly. The yeast deletion mutant collection is also being exploited to map large-scale genetic interaction data obtained from genome-wide synthetic lethal screens and the integration of this data with chemical genetic data should provide a powerful system for linking compounds to their target pathway. Extensive application of chemical genetics in yeast has the potential to develop a small molecule inhibitor for the majority of all approximately 6000 yeast genes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it