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

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

Volume: 11 Issue: 3

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

Affiliation:

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

Abstract: 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.

Cite this article as:

Wadhwa Preeti, Saha Debasmita and Sharma Anuj, Combined 3D-QSAR and molecular docking study for identification of diverse natural products as potent Pf ENR inhibitors, Current Computer-Aided Drug Design 2015; 11 (3) . https://dx.doi.org/10.2174/1573409911666151030102113

DOI https://dx.doi.org/10.2174/1573409911666151030102113	Print ISSN 1573-4099
Publisher Name Bentham Science Publisher	Online ISSN 1875-6697