ArticleScientific reports2024
Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Computer-aided drug design in acute myeloid leukemia: a comprehensive review of advances, challenges, and future prospect.Journal of computer-aided molecular design · 2026Review
- Fine particulate matter exacerbates childhood asthma via DNMT3A-mediated modulation of GPX4 DNA methylation.Scientific reports · 2026Article
- DNMT1 targets SRD5A2 to induce mitochondrial homeostasis and EMT in urothelial cells of hypospadias.Molecular biology reports · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor-protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.
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