Probing the extent of importin‐α targeting of the <scp>TAF8 NLS</scp> by eliminating its cationic net‐charge
Bibliographic record
Abstract
The nucleus, as the control center of the eukaryotic cell, is a prime target for therapeutic interventions due to its role in regulating genetic material. Importin-α is critical for successful nuclear import as it recognizes and binds to cargo proteins bearing a classical nuclear localization signal (NLS), which facilitates their transport from the cytoplasm into the nucleus. NLS tagging to 'actively' import therapeutics provides the most effective means of maximizing nuclear localization and therapeutic efficacy. However, traditional NLSs are highly cationic due to the recognition and binding requirements with importin-α. Because of their highly 'super-charged' nature, NLS-tagged therapeutics face significant challenges, including poor pharmacokinetics due to non-specific interactions. In this study, we engineered novel NLS tags with zero net charge to potentially overcome this limitation. Computational modeling and experimental validation revealed that these net-neutral NLSs bind to importin-α with similar modes and energies as their cationic counterpart. High-resolution structural determination and analysis by X-ray crystallography then confirmed their binding modes. Biophysical methods using circular dichroism, microscale thermophoresis, and cellular localization studies demonstrated that these NLSs maintain sufficiently stable complexes and acceptable binding to importin-α and are functional. Additionally, this study revealed that the minor NLS-binding site of importin-α, with its extensive cationic surface area, was particularly suited for interactions with the acidic residues of the net-neutral NLSs. This study provides a foundational understanding of NLS-importin interactions and presents net-neutral NLSs as viable candidates for next-generation NLS-therapeutic development and expands the scope of nuclear-targeting therapies.
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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 | 0.001 | 0.001 |
| 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".