A DNA as a Substrate and an Enzyme: Direct Profiling of Methyltransferase Activity by Cytosine Methylation of a DNAzyme
Bibliographic record
Abstract
Inhibiting DNA methyltransferase (MTase) activity is crucial for cancer treatment. Evaluating drug candidates as inhibitors of MTase requires the measurement of its activity. However, direct profiling of MTase activity remains an analytical challenge, since a complicated hydrolysis step using methylation-sensitive restriction enzymes (msRE) was inevitable in almost all previously reported methods. Taking advantage that DNA is the substrate of MTase and certain DNA sequences, known as DNAzymes, can also have enzyme-like activities, we herein developed an enzyme-free and label-free route for direct assaying MTase activity. Specifically, adding a cytosine at the meta-position of a peroxidase-mimicking DNAzyme can improve the catalytic activity of DNAzyme up to 5-fold. After methylation of the cytosine cap, the activity is further doubled. Based on these findings, direct assaying of MTase inhibitors was performed without the extra and complicated hydrolysis step. These new findings provide a helpful tool for screening drugs for cancer therapy. The idea of using the same DNA as a substrate and an enzyme could be a general way for assaying other enzymes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".