Combining the Dihydrofolate Reductase Protein-Fragment Complementation Assay with Gene Deletions to Establish Genotype-to-Phenotype Maps of Protein Complexes and Interaction Networks
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Bibliographic record
Abstract
Systematically measuring the impact of gene deletion on protein-protein interactions is a promising approach to reveal the structural bases of protein interaction networks and to allow a better understanding of how genotypes translate into phenotypes. Genetic and protein-interaction tools in yeast now allow us to explore this third dimension of protein-protein interaction networks. Because it is scalable and quantitative, the protein-fragment complementation assay (PCA) using dihydrofolate reductase (DHFR) as the reporter protein provides an exceptionally powerful tool for such a purpose. Here, we describe a fully automated protocol that combines DHFR PCA for protein-protein interaction measurement and synthetic genetic array (SGA) technology for introducing mutant and other alleles into PCA strains using genetic crosses. In this, PCA strains are crossed with strains carrying a gene deletion and SGA markers, and the recombinant haploid progeny are selected by SGA. The resulting haploid strains, each expressing a DHFR-fragment fusion protein in a gene-specific haploid deletion background, are crossed to measure the interaction between the two recombinant proteins by PCA in a diploid homozygous deletion background. This approach can be used to measure a single protein interaction in a large array of genetic backgrounds or a large number of protein interactions in a small number of genetic backgrounds.
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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) | 0.000 | 0.000 |
| 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