Mapping the dynamic Interactomes of “druggable” membrane proteins: roles in human health and disease (1095.20)
Bibliographic record
Abstract
Despite extensive research in the past decade, there is a lack of in‐depth understanding of protein networks associated with integral membrane proteins because of their unique biochemical features, enormous complexity and multiplicity. This is a major obstacle to understanding the biology of deregulation of these integral membrane proteins which leads to numerous human diseases, and consequently hinders our development of improved and more targeted therapies to help treat these diseases. To address this challenge, we previously developed an in vivo genetic system, called the Membrane Yeast Two‐Hybrid (MYTH) assay, to identify and characterize protein interactors of all yeast ABC transporters and human receptor tyrosine kinases (RTKs), as well as selected cancer stem cell receptors (CSCRs)5 and G‐protein coupled receptors (GPCRs). However, despite MYTH being a robust technology suitable for mapping the PPIs of a wide‐range of membrane proteins, we have found that a significant percentage of mammalian integral membrane proteins cannot be properly analyzed using this system.To address this, we have recently developed a new variant of MYTH suitable for use in mammalian cells, which we have called the Mammalian Membrane Two‐Hybrid (MaMTH) system. During my talk, I will discuss exciting new findings indicating that MaMTH can detect stimuli (hormone/agonist)‐ and phosphorylation‐dependent PPIs. In addition, I will show that MaMTH allows for monitoring of the phosphorylation states of ErbB‐receptor mutants and drug‐induced activity changes of oncogenic variants of the Epidermal Growth Factor Receptor (EGFR). In conclusion, our study illustrates that MaMTH is a powerful tool for investigating dynamic interactomes of human integral membrane proteins and promises significant contributions to therapeutic research.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 0.001 |
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".