A computational investigation on the substrate preference of ten-eleven-translocation 2 (TET2)
Bibliographic record
Abstract
TET proteins iteratively convert 5-methylcytosine (5mC) into 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC) in a Fe(ii)/α-ketoglutarate-dependent manner. Our previous biochemical studies revealed that TET proteins are more active on 5mC than on 5hmC and 5fC. However, the source of the substrate preference of TET proteins still remains largely elusive. Here, we investigated the substrate binding and catalytic mechanisms of oxidation reactions mediated by TET2 on different substrates through computational approaches. In accordance with previous experimental reports, our computational results suggest that TET2 can bind to different substrates with comparable binding affinities and the hydrogen abstraction step in the catalytic cycle acts as the rate-limiting step. Further structural characterization of the intermediate structures revealed that the 5-substitution groups on 5hmC and 5fC adopt an unfavorable orientation for hydrogen abstraction, which leads to a higher energy barrier for 5hmC and 5fC (compared to 5mC) and thus a lower catalytic efficiency. In summary, our mechanical insights demonstrate that substrate preference is the intrinsic property of TET proteins and our theoretical calculation results can guide further dry-lab or wet-lab studies on the catalytic mechanism of TET proteins as well as other Fe(ii)/α-ketoglutarate (KG)-dependent dioxygenases.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".