Abstract B220: Artificially inducing protein-membrane anchorage: Introducing a new therapeutic modality.
Bibliographic record
Abstract
Abstract We aim to develop an innovative therapeutic modality of inhibiting aberrant protein function through suppression of activation and/or nuclear translocation. Nature has developed prenylation, a post-translational modification that covalently attaches a hydrophobic prenyl group to a protein to facilitate protein-membrane association to the plasma membrane. We hypothesize that mimicking Nature by artificially inducing protein-membrane anchorage through the use of a rationally designed protein-membrane anchor (PMA), we can simultaneously inhibit the activation and nuclear translocation of oncogenic proteins. Our aim is to explore the therapeutic potential of the protein-membrane anchor and potentially develop a novel drug modality that can be utilized by cancer patients. Our proof-of-concept PMA was design to target signal transducer and activator of transcription 3 (Stat3) protein. Constitutively-active Stat3 directly contributes to the progression of cancer and is present in numerous human cancers. A number of studies have shown that down-regulation of this oncogene via iRNA knockdown induces cellular apoptosis. Thus, Stat3 is an attractive target for the development of potent anti-cancer therapeutics for cancer. Our proto-type PMA 1 was composed of two binding modules: a recognition motif to bind the protein and an anchor to sequester the protein complex to the membrane. The PMA was comprised of a potent Stat3 recognition sequence GpYLPQTV-NH2 covalently attached to a cholesterol membrane anchor. We tested the ability of our PMA to anchor Stat3 to the cell membrane in MDA-MB-231 breast cancer cells which are known to have constitutively-active Stat3. We immunostained these cells with membrane stain FM-4–64 (red), anti-Stat3 antibody (green) and DAPI (nucleus, blue). In the absence of PMA 1, there was strong Stat3 nuclear presence. Most excitingly, in the presence of 25 μM concentration PMA 1, we observed complete sequestration of Stat3 to the cell membrane through PMA-Stat3 association. Currently, we are designing and synthesizing more drug-like, nonphosphorylated PMAs that are less prone to metabolic degradation. We will conduct further studies to determine the biochemical and biological utility of these PMAs. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr B220.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".