Autophagy-related intrinsically disordered proteins in intra-nuclear compartments
Bibliographic record
Abstract
Recent analyses indicated that autophagy can be regulated via some nuclear transcriptional networks and many important players in the autophagy and other forms of programmed cell death are known to be intrinsically disordered. To this end, we analyzed similarities and differences in the intrinsic disorder distribution of nuclear and non-nuclear proteins related to autophagy. We also looked at the peculiarities of the distribution of the intrinsically disordered autophagy-related proteins in various intra-nuclear organelles, such as the nucleolus, chromatin, Cajal bodies, nuclear speckles, promyelocytic leukemia (PML) nuclear bodies, nuclear lamina, nuclear pores, and perinucleolar compartment. This analysis revealed that the autophagy-related proteins constitute about 2.5% of the non-nuclear proteins and 3.3% of the nuclear proteins, which corresponds to a substantial enrichment by about 32% in the nucleus. Curiously, although, in general, the autophagy-related proteins share similar characteristics of disorder with a generic set of all non-nuclear proteins, chromatin and nuclear speckles are enriched in the intrinsically disordered autophagy proteins (29 and 37% of these proteins are disordered, respectively) and have high disorder content at 0.24 and 0.27, respectively. Therefore, our data suggest that some of the nuclear disordered proteins may play important roles in autophagy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".