Computational fishing of new DNA methyltransferase inhibitors from natural products

• A LDA-based QSAR model was built to identify new DNMT inhibitors.
• Virtual screening was performed on 800 NPs from NatProd Collection data base.
• AutoDock Vina and Surflex-Dock were employed for molecular docking on DNMTs structures.
• Contact patterns and molecular diversity analysis were used to prioritize DNMTis.
• Six consensus NPs were identified as potential DNMTis.

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.

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