Membrane Yeast Two-Hybrid (MYTH) Mapping of Full-Length Membrane Protein Interactions
Bibliographic record
Abstract
Mapping of protein interaction networks is a major strategy for obtaining a global understanding of protein function in cells and represents one of the primary goals of proteomics research. Membrane proteins, which play key roles in human disease and as drug targets, are of considerable interest; however, because of their hydrophobic nature, mapping their interactions presents significant technical challenges and requires the use of special methodological approaches. One powerful approach is the membrane yeast two-hybrid (MYTH) assay, a split-ubiquitin-based system specifically suited to the study of full-length membrane protein interactions in vivo using the yeast Saccharomyces cerevisiae as a host. The system can be used in both low- and high-throughput formats to study proteins from a wide range of different organisms. There are two primary variants of MYTH: integrated (iMYTH), which involves endogenous expression and tagging of baits and is suitable for studying native yeast membrane proteins, and traditional (tMYTH), which involves ectopic plasmid-based expression of tagged baits and is suitable for studying membrane proteins from other organisms. Here we provide an introduction to the MYTH assay, including both the iMYTH and tMYTH variants. MYTH can be set up in almost any laboratory environment, with results typically obtainable within 4 to 6 wk.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".