Using yeast as a model to study membrane proteins
Bibliographic record
Abstract
PURPOSE OF REVIEW: Many cellular processes are controlled via either stable or transient protein-protein interactions (PPIs). Protein complexes are 'molecular machines' in which multiple interactive partners carry out various cellular functions. Given that almost a third of the proteome consists of membrane proteins and that more than 50% of currently available drugs are targeted toward them, investigation of membrane protein complexes has taken center stage over the past years. Thus, gaining an in-depth understanding of PPI networks will give us more insight into the functional relationship as well as downstream effectors of protein complexes, hence opening strategies for new drug target definitions. RECENT FINDINGS: Studying membrane proteins in yeast has recently been applied to many different classes of proteins with diverse functions and structures including membrane transporters. Techniques such as the split-ubiquitin membrane yeast two-hybrid or variants of the protein-fragment complementation assay have been successfully applied to both large-scale genome-wide screens and as smaller-scale PPI studies in a reliable and robust fashion. SUMMARY: Yeast-based methods to study membrane PPI in vivo offer a powerful tool for the investigation of protein complexes from various organisms, including mammals. The investigation of global protein maps will serve as a foundation for mechanistic and quantitative studies of poorly characterized gene products and disease-associated proteins. Identification of PPIs is also of great interest for drug discovery as many human diseases result from abnormal PPIs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".