Ligand-Based Virtual Screening and Molecular Docking Studies to Identify the Critical Chemical Features of Potent Cathepsin D Inhibitors

Authors

  • Sugunadevi Sakkiah,

    1. Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center, Plant Molecular Biology and Biotechnology Research Center, Research Institute of Natural Science, Gyeongsang National University, 501 Jinju-daero, Gazha-dong, Jinju 660-701, South Korea
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  • Sundarapandian Thangapandian,

    1. Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center, Plant Molecular Biology and Biotechnology Research Center, Research Institute of Natural Science, Gyeongsang National University, 501 Jinju-daero, Gazha-dong, Jinju 660-701, South Korea
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  • Keun Woo Lee

    Corresponding authorSearch for more papers by this author

Corresponding author: Keun Woo Lee,kwlee@gnu.ac.kr

Abstract

Cathepsin D is a major component of lysosomes and plays a major role in catabolism and degenerative diseases. The quantitative structure–activity relationship study was used to explore the critical chemical features of cathepsin D inhibitors. Top 10 hypotheses were built based on 36 known cathepsin D inhibitors using HypoGen/Discovery Studio v2.5. The best hypothesis Hypo1 consists of three hydrophobic, one hydrogen bond acceptor lipid, and one hydrogen bond acceptor features. The selected Hypo1 model was cross-validated using Fischer’s randomization method to identify the strong correlation between experimental and predicted activity value as well as the test set and decoy sets used to validate its predictability. Moreover, the best hypothesis was used as a 3D query in virtual screening of Scaffold database. Subsequently, the screened hit molecules were filtered by applying Lipinski’s rule of five, absorption, distribution, metabolism, and toxicity, and molecular docking studies. Finally, 49 compounds were obtained as potent cathepsin D inhibitors based on the consensus scoring values, critical interactions with protein active site residues, and predicted activity values. Thus, we suggest that the application of Hypo1 could assist in the selection of potent cathepsin D leads from various databases. Hence, this model was used as a valuable tool to design new candidate for cathepsin D inhibitors.

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