Analysis of Membrane Protein Complexes Using the Split-Ubiquitin Membrane Yeast Two-Hybrid System
Bibliographic record
Abstract
Recent research has begun to elucidate the global network of cytosolic and membrane protein interactions. The resulting interactome map facilitates numerous biological studies, including those for cell signalling, protein trafficking and protein regulation. Due to the hydrophobic nature of membrane proteins such as tyrosine kinases, G-protein coupled receptors, membrane bound phosphatases and transporters it is notoriously difficult to study their relationship to signaling molecules, the cytoskeleton, or any other interacting partners. Although conventional yeast-two hybrid is a simple and robust technique that is effective in the identification of specific protein-protein interactions, it is limited in its use for membrane proteins. However, the split-ubiquitin membrane based yeast two-hybrid assay (MYTH) has been described as a tool that allows for the identification of membrane protein interactions. In the MYTH system, ubiquitin has been split into two halves, each of which is fused to a protein, at least one of which is membrane bound. Upon interaction of these two proteins, the two halves of ubiquitin are reconstituted and a transcription factor that is fused to the membrane protein is released. The transcription factor then enters the nucleus and activates transcription of reporter genes. Currently, large-scale MYTH screens using cDNA or gDNA libraries are performed to identify and map the binding partners of various membrane proteins. Thus, the MYTH system is proving to be a powerful tool for the elucidation of specific protein-protein interactions, contributing greatly to the mapping of the membrane protein interactome.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".