Combined 3D-QSAR and molecular docking study for identification of diverse natural products as potent Pf ENR inhibitors

Author(s): Preeti Wadhwa, Debasmita Saha, Anuj Sharma.

Journal Name: Current Computer-Aided Drug Design

Volume 11 , Issue 3 , 2015

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An in-house library of 200 molecules from natural plant products was designed in order to evaluate their binding to Plasmodium ACP enoyl reductase (ENR), a promising biological target for antimalarial chemotherapeutics. The binding site of PfENR was explored computationally and the molecules were docked using AutoDock. Furthermore, the top-ranked scaffolds from docking studies were also compared with known PfENR inhibitors using 3D-QSAR. To this effect, a 3D-QSAR model was derived from a set of experimentally established PfENR inhibitors, using Comparative Molecular Force Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). The best optimum CoMFA model exhibited a leave-one-out correlation coefficient (q2) and a noncross- validated correlation coefficient (r2) of 0.630 and 0.911, respectively. The result of this cumulative approach proposed five structurally distinct natural products as potent PfENR inhibitors. This study may lay a stepping stone towards Functional oriented synthesis (FOS) of novel PfENR inhibitors in future.

Keywords: Malaria, Plasmodium ACP enoylreductase (FabI), Molecular docking, CoMFA analysis, drug design.

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Article Details

Year: 2015
Page: [245 - 257]
Pages: 13
DOI: 10.2174/1573409911666151030102113
Price: $65

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PDF: 52
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