Abstract
Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.
Highlights
Covalent modifications on nucleosomes, the basic building blocks on chromatins, including methylation, acetylation, phosphorylation, and ubiquination regulate downstream gene expression patterns in a context-dependent manner that form the fundamental molecular basis of epigenetics (Strahl and Allis, 2000; Berger, 2007)
Epigenetic dysfunction is tightly related with the pathogenesis and progression of several diseases including malignant diseases especially cancers and chronic diseases such as immune-mediated diseases, neurodegenerative disorders and diabetes which underscoring the importance of these covalent modifications (Best and Carey, 2010; Dawson and Kouzarides, 2012; Tough et al, 2016; Hwang et al, 2017)
The indications of approved epigenetic drugs are limited to malignant diseases such as myelodysplastic syndromes (MDS), acute myeloid leukemia (AML), chronic myelomonocytic leukemia (CML), peripheral T-cell lymphoma (PTCL), and cutaneous T-cell lymphoma (CTCL) while the applications of epigenetic drugs in chronic diseases treatment were less explored (Mann et al, 2007; Derissen et al, 2013; Laubach et al, 2015; Lee et al, 2015)
Summary
The basic building blocks on chromatins, including methylation, acetylation, phosphorylation, and ubiquination regulate downstream gene expression patterns in a context-dependent manner that form the fundamental molecular basis of epigenetics (Strahl and Allis, 2000; Berger, 2007). Based on their distinct functions, writers are usually divided into three categories, namely DNA methyltransferases (DNMTs), protein lysine/arginine methyltransferases (PKMTs/PRMTs) and histone acetyltransferases (HATs) These enzymes alter chromatin organization and contribute to downstream gene expression regulation through site-specific modification that are involved in the multiple function pathways (Gelato and Fischle, 2008). To elucidate their roles in physiological or pathological states, there has been increasing interest in the discovery of writer inhibitors through in silico approaches and many successful stories have been reported in the literature (Figure 2). We will present an overview of the current applications of computational methods used in hit identification targeting epigenetic writers
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