Using Biosensors to Study Protein–Protein Interaction in the Hippo Pathway
Bibliographic record
Abstract
The Hippo signaling network is dependent on protein–protein interactions (PPIs) as a mechanism of signal transduction to regulate organ size, cellular proliferation and differentiation, tumorigenesis, and other cellular processes. Current efforts aim to resolve the complex regulation of upstream Hippo components or focus on identifying targeted drugs for use in cancer therapy. Despite extensive characterization of the Hippo pathway interactome by affinity purification mass spectrometry (AP-MS) and other methodologies, previous research methods have not been sufficient to achieve these aims. In this review, we describe several recent studies that make use of luciferase-based biosensors as a new approach to study the Hippo Pathway. These biosensors serve as powerful tools with which to study PPIs both in vitro using purified biosensor proteins, and in real time in live cells. Notably, luciferase biosensors have excellent sensitivity and have been used to screen for upstream kinase regulators of the Hippo pathway. Furthermore, the high sensitivity and stability of these biosensors enables their application in high throughput screening for Hippo-targeted chemotherapeutics. Finally, we describe the strengths and weaknesses of this method relative to AP-MS and discuss potential future directions for using biosensors to study Hippo signaling.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".