An improved genetic system for detection and analysis of protein nuclear import signals
Bibliographic record
Abstract
BACKGROUND: Nuclear import of proteins is typically mediated by their physical interaction with soluble cytosolic receptor proteins via a nuclear localization signal (NLS). A simple genetic assay to detect active NLSs based on their function in the yeast Saccharomyces cerevisiae has been previously described. In that system, a chimera consisting of a modified bacterial LexA DNA binding domain and the transcriptional activation domain of the yeast Gal4 protein is fused to a candidate NLS. A functional NLS will redirect the chimeric fusion to the yeast cell nucleus and activate transcription of a reporter gene. RESULTS: We have reengineered this nuclear import system to expand its utility and tested it using known NLS sequences from adenovirus E1A. Firstly, the vector has been reconstructed to reduce the level of chimera expression. Secondly, an irrelevant "stuffer" sequence from the E. coli maltose binding protein was used to increase the size of the chimera above the passive diffusion limit of the nuclear pore complex. The improved vector also contains an expanded multiple cloning site and a hemagglutinin epitope tag to allow confirmation of expression. CONCLUSION: The alterations in expression level and composition of the fusions used in this nuclear import system greatly reduce background activity in beta-galactosidase assays, improving sensitivity and allowing more quantitative analysis of NLS bearing sequences.
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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".