Identification, expression, and purification of DNA cytosine 5-methyltransferases with short recognition sequences
Bibliographic record
Abstract
ABSTRACT Background DNA methyltransferases (MTases) are enzymes that induce methylation, one of the representative epigenetic modifications of DNA, and are also useful tools for analyzing epigenomes. However, regarding DNA cytosine 5-methylation, MTases identified so far have drawbacks in that their recognition sequences overlap with those for intrinsic DNA methylation in mammalian cells and/or that the recognition sequence is too long for fine epigenetic mapping. To identify MTases with short recognition sequences that never overlap with the CG dinucleotide, we systematically investigated the 25 candidate enzymes identified using a database search, which showed high similarity to known cytosine 5-MTases recognizing short sequences. Results We identified MTases with six new recognition sequences, including TCTG, CC, CNG, TCG, GCY, and GGCA. Because the recognition sequence never overlapped with the CG dinucleotide, MTases recognizing the CC dinucleotide were promising. Conclusions In the current study, we established a procedure for producing active CC-methylating MTases and applied it to nucleosome occupancy and methylome sequencing to prove the usefulness of the enzyme for fine epigenetic mapping. MTases that never overlap with CG dinucleotides would allow us to profile multiple epigenomes simultaneously.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".