Dual expression recombinase based (DERB) single vector system for high throughput screening and verification of protein interactions in living cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Identification of novel protein interactions and their mediators is fundamental in understanding cellular processes and is necessary for protein-targeted therapy. Evidently high throughput formatting of these applications in living cells would be beneficial, however no adequate system exists. We present a novel platform technology for the high throughput screening and verification of protein interactions in living cells. The platform's series of Dual Expression Recombinase Based (DERB) destiny vectors individually encode two sets of recombinase recognizable sequences for inserting the protein open reading frame (ORF) of interest, two sets of promoters and reporter tags in frame with the ORFs for detecting interactions. Introduction into living cells (prokaryotic and eukaryotic) enables the detection of protein interactions by fluorescence resonance energy transfer (FRET) or bimolecular fluorescence complementation (BiFC). The DERB platform shows advantages over current commercialized systems by DERB vectors validated through proof-of-principle experiments and the identification of an unknown interaction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it