Image‐Based Analysis of Protein Stability
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Bibliographic record
Abstract
Short half-life proteins regulate many essential processes, including cell cycle, transcription, and apoptosis. However, few well-characterized protein-turnover pathways have been identified because traditional methods to measure protein half-life are time and labor intensive. To overcome this barrier, we developed a protein stability probe and high-content screening pipeline for novel regulators of short half-life proteins using automated image analysis. Our pilot probe consists of the short half-life protein c-MYC (MYC) fused to Venus fluorescent protein (MYC-Venus). This probe enables protein half-life to be scored as a function of fluorescence intensity and distribution. Rapid turnover prevents maximal fluorescence of the probe due to the relatively longer maturation time of the fluorescent protein. Cells expressing the MYC-Venus probe were analyzed using a pipeline in which automated confocal microscopy and image analyses were used to score MYC-Venus stability by two strategies: assaying the percentage of cells with Venus fluorescence above background, and phenotypic comparative analysis. To evaluate this high-content screening pipeline and our probe, a kinase inhibitor library was screened by confocal microscopy to identify known and novel kinases that regulate MYC stability. Compounds identified were shown to increase the half-life of both MYC-Venus and endogenous MYC, validating the probe and pipeline. Fusion of another short half-life protein, myeloid cell leukemia 1 (MCL1), with Venus also demonstrated an increase in percent Venus-positive cells after treatment with inhibitors known to stabilize MCL1. Together, the results validate the use of our automated microscopy and image analysis pipeline of stability probe-expressing cells to rapidly and quantitatively identify regulators of short half-life proteins. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
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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 | 0.001 |
| 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) | 0.001 | 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