Investigation of Membrane Protein Interactions Using the Split-Ubiquitin Membrane Yeast Two-Hybrid System
Bibliographic record
Abstract
Proteins are generally organized into molecular complexes, in which multiple interaction partners collaborate to carry out cellular processes. Thus, techniques to map protein-protein interactions have become pivotal for biological studies of as yet uncharacterized proteins. Investigation of interaction partners of membrane proteins is of special interest, as they play a major role in cellular processes and are often directly linked to human diseases. Owing to their hydrophobic nature, however, it has proven difficult to study their interaction partners. To circumvent this problem, a yeast-based genetic technology for the in vivo detection of membrane protein interactions, the split-ubiquitin membrane yeast two-hybrid (MYTH) system, has been developed. MYTH allows for detection of both stable and transient interactions and can be applied to large- and small-scale screens. It uses the split-ubiquitin approach, in which the reconstitution of two ubiquitin halves is mediated by a specific protein-protein interaction. Briefly, the bait membrane protein is fused to the C-terminal half of ubiquitin and an artificial transcription factor. The mutated N-terminal moiety of ubiquitin is fused to the prey protein. Upon interaction of bait and prey proteins, ubiquitin is reconstituted and further recognized by ubiquitin-specific proteases, which subsequently cleave off the transcription factor, thus resulting in reporter gene activation. To date, MYTH has been successfully applied to study interactions of membrane proteins from various organisms and has only recently been adapted for the identification of interaction partners of mammalian receptor tyrosine kinases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".