Protocol for the purification and analysis of nuclear UFMylated proteins
Bibliographic record
Abstract
Protein UFMylation regulates numerous cellular processes including ribosome quality control and nuclear DNA repair. Here, we present a technique to isolate nuclei and purify UFMylated proteins under denaturing non-reducing conditions from commonly used mammalian cell line models such as hTERT-RPE1, HEK293, U2OS, and HCT116 cells. We then describe procedures for identifying and analyzing purified UFMylated proteins using mass spectrometry and western blot. For complete details on the use and execution of this protocol, please refer to Panichnantakul et al . 1 • Cell fractionation to separate nuclear and cytoplasmic UFMylated proteins • Affinity purification of UFMylated proteins from lysed and denatured nuclei • Semi-quantitative mass spectrometry to identify nuclear UFMylated proteins Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Protein UFMylation regulates numerous cellular processes including ribosome quality control and nuclear DNA repair. Here, we present a technique to isolate nuclei and purify UFMylated proteins under denaturing non-reducing conditions from commonly used mammalian cell line models such as hTERT-RPE1, HEK293, U2OS, and HCT116 cells. We then describe procedures for identifying and analyzing purified UFMylated proteins using mass spectrometry and western blot.
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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 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.033 |
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".