Abstract
DNA methyltransferases (DNMTs) are a family of epigenetic enzymes for which inhibition is an attractive strategy for the treatment of cancer and other diseases. In synergy with experimental approaches, computational methods are increasingly being used to identify and optimize the activity of inhibitors of DNMTs as well as to rationalize at the molecular level of the mechanism of established inhibitors. Recently, a crystallographic structure of the methyltransferase domain of human DNMT1 bound to unmethylated DNA was published encouraging the application of structure-based approaches to design and optimize the activity of currently known inhibitors. Herein, we review the progress in the discovery and optimization of inhibitors of DNMTs using computational approaches including homology modeling, docking, pharmacophore modeling, molecular dynamics, and virtual screening.
Keywords: Cancer, docking, drug discovery, enzyme inhibitor, epigenetics, homology modeling, molecular dynamics, natural products, structure-activity relationships, virtual screening
Current Medicinal Chemistry
Title:Inhibitors of DNA Methyltransferases: Insights from Computational Studies
Volume: 19 Issue: 21
Author(s): J. Yoo and J. L. Medina-Franco
Affiliation:
Keywords: Cancer, docking, drug discovery, enzyme inhibitor, epigenetics, homology modeling, molecular dynamics, natural products, structure-activity relationships, virtual screening
Abstract: DNA methyltransferases (DNMTs) are a family of epigenetic enzymes for which inhibition is an attractive strategy for the treatment of cancer and other diseases. In synergy with experimental approaches, computational methods are increasingly being used to identify and optimize the activity of inhibitors of DNMTs as well as to rationalize at the molecular level of the mechanism of established inhibitors. Recently, a crystallographic structure of the methyltransferase domain of human DNMT1 bound to unmethylated DNA was published encouraging the application of structure-based approaches to design and optimize the activity of currently known inhibitors. Herein, we review the progress in the discovery and optimization of inhibitors of DNMTs using computational approaches including homology modeling, docking, pharmacophore modeling, molecular dynamics, and virtual screening.
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Cite this article as:
Yoo J. and L. Medina-Franco J., Inhibitors of DNA Methyltransferases: Insights from Computational Studies, Current Medicinal Chemistry 2012; 19 (21) . https://dx.doi.org/10.2174/092986712801323289
DOI https://dx.doi.org/10.2174/092986712801323289 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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