Chemical genomics in yeast: linking biologically active compounds to their intracellular targets
Bibliographic record
Abstract
Target specific chemical inhibitors are highly valuable as both research tools and therapeutic leads, but it is often difficult to identify their mechanism of action or cellular target. Here I have studied genome-wide chemical-genetic interaction profiles in the budding yeast Saccharomyces cerevisiae, by testing the complete set of viable deletion mutants for hypersensitivity to inhibitory compounds. Integration of chemical-genetic and genetic interaction data reveals information about the mode of action of bioactive compounds. First, in a series of proof-of-concept experiments I showed that because a loss-of-function mutation in a gene encoding the target of an inhibitory compound models the primary effect of the compound, crossing such a mutation into the set of viable mutants and scoring the resultant double mutants for reduced fitness generates a genetic interaction profile for the target gene resembling the chemical-genetic interaction profile of its inhibitory compound. Therefore, clustering the compound-specific profiles with a compendium of large-scale genetic interaction profiles enables the identification of target pathways or proteins and thus provides a powerful means for inferring mechanism of action. In the second phase of this project, I expanded our matrix of chemical-genetic interactions by profiling 85 diverse compounds and natural product extracts, including a number of human therapeutics, using parallel fitness tests and a microarray-based readout. Hierarchical clustering of the dataset associates compounds of similar mode of action and reveals insight into the cellular pathways affected by the compounds. In particular, my analysis establishes a cell membrane target for papuamide B, a high molecular weight cyclic lipopeptide with potent anti-fungal and anti-HIV activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".