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J Mol Graph Model. 2015 Jul;60:43-54. doi: 10.1016/j.jmgm.2015.04.010. Epub 2015 Jun 3.

Computational fishing of new DNA methyltransferase inhibitors from natural products.

Author information

1
Environmental and Computational Chemistry Group, School of Pharmaceutical Sciences, Zaragocilla Campus, University of Cartagena, 130015 Cartagena, Colombia.
2
Environmental and Computational Chemistry Group, School of Pharmaceutical Sciences, Zaragocilla Campus, University of Cartagena, 130015 Cartagena, Colombia. Electronic address: joliverov@unicartagena.edu.co.

Abstract

DNA methyltransferase inhibitors (DNMTis) have become an alternative for cancer therapies. However, only two DNMTis have been approved as anticancer drugs, although with some restrictions. Natural products (NPs) are a promising source of drugs. In order to find NPs with novel chemotypes as DNMTis, 47 compounds with known activity against these enzymes were used to build a LDA-based QSAR model for active/inactive molecules (93% accuracy) based on molecular descriptors. This classifier was employed to identify potential DNMTis on 800 NPs from NatProd Collection. 447 selected compounds were docked on two human DNA methyltransferase (DNMT) structures (PDB codes: 3SWR and 2QRV) using AutoDock Vina and Surflex-Dock, prioritizing according to their score values, contact patterns at 4 Å and molecular diversity. Six consensus NPs were identified as virtual hits against DNMTs, including 9,10-dihydro-12-hydroxygambogic, phloridzin, 2',4'-dihydroxychalcone 4'-glucoside, daunorubicin, pyrromycin and centaurein. This method is an innovative computational strategy for identifying DNMTis, useful in the identification of potent and selective anticancer drugs.

KEYWORDS:

Anticancer; Cluster; Docking; QSAR; Screening

PMID:
26099696
DOI:
10.1016/j.jmgm.2015.04.010
[Indexed for MEDLINE]

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