Towards the structural study of a ZMPSTE24-mimic for the development of novel cancer therapeutics
Bibliographic record
Abstract
Cancer involves complex mechanisms affecting the cell cycle, apoptosis and senescence. p53 is an extensively studied protein that is heavily involved in modulating the development of tumors. As such, there has been great interest in targeting this protein for novel cancer therapeutics. However several issues, such as development of resistance, reduces the effectiveness of these treatments in clinical settings. There is now growing interesting in finding novel targets, which can induce cellular senescence in a p53 independent manner. One protein of interest is ZMPSTE24; natively, this protein is involved in the final cleavage step of lamin A processing. Defects of lamin A processing lead to diseases such as Hutchinson Gilford Progeria Syndrome (HGPS). Patients with HGPS display physical characteristics of accelerated aging and tend to pass away at young ages related to those symptoms. The possibility of ZMPSTE24 as a target for cancer therapeutics was first introduced when it was noticed that suffers of HGPS, despite the accelerated aging, had lower incidences of cancer compared to the normal population. Biochemical studies on progerin and ZMPSTE24 have demonstrated ability to induce cellular senescence in cancer cell lines independent of the presence of p53. Several factors prevent rational drug design with ZMPSTE24, typically due to the extensive membrane bound structural components. To bypass these issues, a model bacterial protein, thermolysin can be used. Thermolysin and ZMPSTE24 are both metalloproteases and share very similar active sites. This thesis will describe the design of a thermolysin active site mutant that mimics the active site of ZMPSTE24, and the expression, purification, characterization and crystallization trials of this protein. The thermolysin model will be used to then guide rational drug design of small molecule inhibitors of ZMPSTE24. These small molecules can be used to induce cellular senescence in tumors in a p53 independent manner, bypassing the issues facing p53 targeted or p53 involved therapeutics.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".