Structural basis for the ability of MBD domains to bind methyl-CG and TG sites in DNA
Bibliographic record
Abstract
Cytosine methylation is a well-characterized epigenetic mark and occurs at both CG and non-CG sites in DNA. Both methylated CG (mCG)- and mCH (H = A, C, or T)-containing DNAs, especially mCAC-containing DNAs, are recognized by methyl-CpG–binding protein 2 (MeCP2) to regulate gene expression in neuron development. However, the molecular mechanism involved in the binding of methyl-CpG–binding domain (MBD) of MeCP2 to these different DNA motifs is unclear. Here, we systematically characterized the DNA-binding selectivities of the MBD domains in MeCP2 and MBD1–4 with isothermal titration calorimetry–based binding assays, mutagenesis studies, and X-ray crystallography. We found that the MBD domains of MeCP2 and MBD1–4 bind mCG-containing DNAs independently of the sequence identity outside the mCG dinucleotide. Moreover, some MBD domains bound to both methylated and unmethylated CA dinucleotide–containing DNAs, with a preference for the CAC sequence motif. We also found that the MBD domains bind to mCA or nonmethylated CA DNA by recognizing the complementary TG dinucleotide, which is consistent with an overlooked ligand of MeCP2, i.e. the matrix/scaffold attachment regions (MARs/SARs) with a consensus sequence of 5′-GGTGT-3′ that was identified in early 1990s. Our results also explain why MeCP2 exhibits similar binding affinity to both mCA- and hmCA-containing dsDNAs. In summary, our results suggest that in addition to mCG sites, unmethylated CA or TG sites also serve as DNA-binding sites for MeCP2 and other MBD-containing proteins. This discovery expands the genome-wide activity of MBD-containing proteins in gene regulation.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".