Yeast as a Host to Screen for Modulators and Regulatory Regions of Mammalian Protein Kinase C Isoforms
Bibliographic record
Abstract
Expression of various mammalian protein kinase C (PKC) isoforms in yeast including Saccharomyces cerevisiae and Schizosaccharomyces pombe results in phenotypic changes such as substantial increases in the cell doubling time, alterations in cell morphology, enhanced production of cytoplasmic vesicles, and elevated uptake of external Ca2+. These phenotypes are the consequence of mammalian PKC catalytic activity. They require the expression of a mammalian PKC isoform, its proper domain structure including a functional catalytic domain, and typically activators such as phorbol esters. The observed phenotype is proportional to mammalian PKC catalytic activity and can be easily evaluated by observing the yeast cell doubling time. As a result, mammalian PKC activation by cell membrane-permeable activators such as tumor promoting phorbol esters, co-expressed protein ligands, or changes in PKC structure such as by random cDNA mutagenesis can be estimated through the resulting yeast colony size on agar plates or yeast culture density on microtiter plates. In this chapter, yeast is introduced as a host to screen libraries of PKC mutants, pharmacologic compounds, or PKC-binding proteins, for their impact on PKC catalytic activity. These libraries may originate from natural or synthetic pools of reagents or random cDNA mutagenesis strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".