NoLogo: a new statistical model highlights the diversity and suggests new classes of Crm1-dependent nuclear export signals
Bibliographic record
Abstract
BACKGROUND: Crm1-dependent Nuclear Export Signals (NESs) are clusters of alternating hydrophobic and non-hydrophobic amino acid residues between 10 to 15 amino acids in length. NESs were largely thought to follow simple consensus patterns, based on which they were categorized into 6-10 classes. However, newly discovered NESs often deviate from the established consensus patterns. Thus, identifying NESs within protein sequences remains a bioinformatics challenge. RESULTS: We describe a probabilistic representation of NESs using a new generative model we call NoLogo that can account for a large diversity of NESs. Using this model to predict NESs, we demonstrate improved performance over PSSM and GLAM2 models, but do not achieve the performance of the state-of-the-art NES predictor LocNES. Our findings illustrate that over 30% of NESs are best described by novel NES classes rather than the 6-10 classes proposed by current/existing models. Finally, many NESs have additional hydrophobic residues either upstream or downstream of the canonical four residues, suggesting possible functionality. CONCLUSION: Applying the NoLogo model highlights the observation that NESs are more diverse than previously appreciated. Our work questions the practice of assigning each NES to one of several predefined NES classes. Finally, our analysis suggests a novel and testable biophysical perspective on interaction between Crm1 receptor and Crm1-dependent NESs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".