Piggybacking on Classical Import and Other Non-Classical Mechanisms of Nuclear Import Appear Highly Prevalent within the Human Proteome
Bibliographic record
Abstract
One of the most conserved cellular pathways among eukaryotes is the extensively studied classical protein nuclear import pathway mediated by importin-α. Classical nuclear localization signals (cNLSs) are recognized by importin-α and are highly predictable due to their abundance of basic amino acids. However, various studies in model organisms have repeatedly demonstrated that only a fraction of nuclear proteins contain identifiable cNLSs, including those that directly interact with importin-α. Using data from the Human Protein Atlas and the Human Reference Interactome, and proteomic data from BioID/protein-proximity labeling studies using multiple human importin-α proteins, we determine that nearly 50% of the human nuclear proteome does not have a predictable cNLS. Surprisingly, between 25% and 50% of previously identified human importin-α cargoes do not have predictable cNLS. Analysis of importin-α cargo without a cNLS identified an alternative basic rich motif that does not resemble a cNLS. Furthermore, several previously suspected piggybacking proteins were identified, such as those belonging to the RNA polymerase II and transcription factor II D complexes. Additionally, many components of the mediator complex interact with at least one importin-α, yet do not have a predictable cNLS, suggesting that many of the subunits may enter the nucleus through an importin-α-dependent piggybacking mechanism.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".