Real-Time Protein-Fragment Complementation Assays for Studying Temporal, Spatial, and Spatiotemporal Dynamics of Protein–Protein Interactions in Living Cells
Bibliographic record
Abstract
Here, we present detailed protocols for direct, real-time protein-fragment complementation assays (PCAs) for studying the spatiotemporal dynamics of protein-protein interactions (PPIs). The assays require the use of two fluorescent reporter proteins-the "Venus" version of yellow fluorescent protein (vYFP), and the monomeric infrared fluorescent protein 1.4 (IFP 1.4)-or two luciferase reporter proteins-Renilla (Rluc) and Gaussia (Gluc). The luciferase PCAs can be used to study the temporal dynamics of PPIs in any cellular compartment and on membranes. The full reversibility of these PCAs assures accurate measurements of the kinetics of PPI assembly/disassembly for processes that occur anywhere in a living cell and over time frames of seconds to hours. vYFP PCA, and all PCAs based on green fluorescent protein and its variants, are irreversible and can be used to trap and visualize rare and transient complexes and follow dynamic relocalization of constitutive complexes. vYFP PCA is limited in that accurate measurements of temporal changes in PPIs are not possible owing to the slow maturation time of vYFP (minutes to hours) and the irreversibility of its PCA that traps the complexes, thereby preventing the dissociation of PPIs that, in some instances, might cause spurious mislocalization of protein complexes. The limitations of vYFP PCA are overcome with IFP PCA, which is fully reversible and thus can be used to study spatiotemporal dynamics of PPIs on the timescale of seconds. All of these PCAs are sensitive enough to detect interactions among proteins expressed at endogenous levels in vivo.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".