Proteomic-based evaluation of nuclear transport of NLS-tagged trastuzumab-emtansine with enhanced cytotoxic potency
Bibliographic record
Abstract
ABSTRACT Nuclear transport receptors (NTRs) are the only proteins able to transport large molecular weight payloads into the nucleus. A dominant area of molecular therapeutic research is the extension of the use of NTRs to target the nucleus for the development of pharmaceuticals or as tools for investigating fundamental biological questions. Although several examples of synthesized peptides harbouring nuclear localization signal (NLS) sequences conjugated to various payloads exist in the literature, the assumption has been that transport occurs by classical nuclear localization via the NTRs importin-α and importin-β. This assumption is relevant to nuclear-targeted therapeutics that aim for full potential clinical impact. In addition, fundamental research can benefit from unbiased approaches to investigate the role of NTRs. Herein, we report the construction of a novel NLS-modified agent composed of trastuzumab-emtansine (T-DM1) coupled to cell accumulator (Accum), a technology that enables monoclonal antibodies to escape endosome entrapment and accumulate conjugated payloads in the nucleus without abrogating affinity or specificity to target antigens. Accum harbours a classical NLS sequence from SV-40 large T-antigen. We demonstrate that routing T-DM1 to the nucleus successfully increased cytotoxic potency in the HER2-positive cell line SKBR3. More importantly, through the development of a novel bait-prey proteomic approach, we show that the non-classical NTR importin 7 and not importin-α/importin-β was required for the cytotoxicity effect. This result was validated by siRNA knock down. Our findings also indicate that by discovering an unanticipated NTR regulator of an NLS-modified agent, this study demonstrates the utility of combining an unbiased proteomic approach to probe NTR function in mammalian cell system and, is a foresight for future NLS-based development initiatives.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".