Computational fishing of new DNA methyltransferase inhibitors from natural products
作者： Wilson Maldonado-Rojas , Jesus Olivero-Verbel , Yovani Marrero-Ponce
DOI: 10.1016/J.JMGM.2015.04.010
关键词: Molecular descriptor 、 Combinatorial chemistry 、 Computational biology 、 Quantitative structure–activity relationship 、 Docking (molecular) 、 Biology 、 Pyrromycin 、 Methyltransferase 、 Protein Data Bank (RCSB PDB) 、 DNA Methyltransferase Inhibitor 、 Enzyme 、 Physical and Theoretical Chemistry 、 Spectroscopy 、 Materials Chemistry 、 Computer Graphics and Computer-Aided Design
摘要: DNA methyltransferase inhibitors (DNMTis) have become an alternative for cancer therapies. However, only two DNMTis been approved as anticancer drugs, although with some restrictions. Natural products (NPs) are a promising source of drugs. In order to find NPs novel chemotypes DNMTis, 47 compounds known activity against these enzymes were used build LDA-based QSAR model active/inactive molecules (93% accuracy) based on molecular descriptors. This classifier was employed identify potential 800 from NatProd Collection. 447 selected docked human (DNMT) structures (PDB codes: 3SWR and 2QRV) using AutoDock Vina Surflex-Dock, prioritizing according their score values, contact patterns at 4 A diversity. Six consensus identified virtual hits DNMTs, including 9,10-dihydro-12-hydroxygambogic, phloridzin, 2',4'-dihydroxychalcone 4'-glucoside, daunorubicin, pyrromycin centaurein. method is innovative computational strategy identifying useful in the identification potent selective
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