Development of Substrate‐Derived Sirtuin Inhibitors with Potential Anticancer Activity
Bibliographic record
Abstract
RhoGDIα is a key regulator of Rho proteins, coordinating their GTP/GDP and membrane/cytosol cycle. Recently, it was demonstrated by quantitative mass spectrometry that RhoGDIα is heavily targeted by post-translational lysine acetylation. For one site in its N-terminal domain, namely K52, we reported earlier that acetylation completely switches off RhoGDIα function. Herein we show that K52-acetylated RhoGDIα is specifically deacetylated by the sirtuin deacetylase Sirt2. We show that acetylation at K52 decelerates cervical cancer cell proliferation, suggesting RhoGDIα acetylation to be a promising therapeutic target. We demonstrate that treatment of cervical cancer cells with a RhoGDIα-derived K52-trifluoroacetylated, substrate-derived peptidic sirtuin inhibitor severely impairs cell proliferation. Finally, we conclude that the potency of substrate-derived sirtuin inhibitors depends on structural features, the substrate-derived amino acid sequence as a determinant for selectivity, as well as the presence of an acetyl-lysine analogue to increase its potency. These data reveal a prospective therapeutic potential for novel substrate-derived sirtuin inhibitors.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".