Surveying the landscape of optogenetic methods for detection of protein–protein interactions
Bibliographic record
Abstract
Mapping the protein-protein interaction (PPi) landscape is of critical importance to furthering our understanding how cells and organisms function. Optogenetic methods, that is, approaches that utilize genetically encoded fluorophores or fluorogenic enzyme reactions, uniquely enable the visualization of biochemical phenomena in live cells with high spatial and temporal accuracy. Applying optogenetic methods to the detection of PPis requires the engineering of protein-based systems in which an optical signal undergoes a substantial change when the two proteins of interest interact. In recent years, researchers have developed a number of creative and effective optogenetic methods that achieve this goal, and used them to further elaborate our map of the PPi landscape. In this review, we provide an introduction to the general principles of optogenetic PPi detection, and then provide a number of representative examples of how these principles have been applied. We have organized this review by categorizing methods based on whether the signal generated is reversible or irreversible in nature, and whether the signal is localized or nonlocalized at the subcellular site of the PPi. We discuss these techniques giving both their benefits and drawbacks to enable rational choices about their potential use. This article is categorized under: Laboratory Methods and Technologies > Imaging Laboratory Methods and Technologies > Macromolecular Interactions, Methods Analytical and Computational Methods > Analytical Methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".