Investigation on the isoform selectivity of novel kinesin-like protein 1 (KIF11) inhibitor using chemical feature based pharmacophore, molecular docking, and quantum mechanical studies. (April 2016)

• Record Type: Journal Article
• Authors: Karunagaran, Subramanian; Subhashchandrabose, Subramaniyan; Lee, Keun Woo; Meganathan, Chandrasekaran
• Is Part Of: Computational biology and chemistry. Volume 61(2016)
• Page Start: 47
• Page End: 61
• Publication Date: 2016-04
• Subjects: Kinesin like protein-1 -- Pharmacophore modeling -- Molecular docking -- Density functional theory; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.01.002
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Ligand based pharmacophore model, docking and density function approaches were employed to reveal the lead candidate for novel kinesin like protein-1. Highlights: Ligand based pharmacophore model was developed for kinesin like protein-1. Validity and predictability of model has been validated by various potent methods. Validated model used as query in virtual screening to retrieve potent candidates. Molecular interaction and binding mode of hits were studied by molecular docking. Electronic properties and charge transfer of lead candidates were studied by DFT. Abstract: Kinesin-like protein (KIF11) is a molecular motor protein that is essential in mitosis. Removal of KIF11 prevents centrosome migration and causes cell arrest in mitosis. KIF11 defects are linked to the disease of microcephaly, lymph edema or mental retardation. The human KIF11 protein has been actively studied for its role in mitosis and its potential as a therapeutic target for cancer treatment. Pharmacophore modeling, molecular docking and density functional theory approaches was employed to reveal the structural, chemical and electronic features essential for the development of small molecule inhibitor for KIF11. Hence we have developed chemical feature based pharmacophore models using Discovery Studio v 2.5 (DS). The best hypothesis (Hypo1) consisting of four chemical features (two hydrogen bond acceptor, one hydrophobic and one ring aromatic) has exhibited high correlation co-efficient of 0.9521, cost difference of 70.63 and low RMS value of 0.9475. This Hypo1 is cross validated by Cat Scramble method; test set and decoy set to prove its robustness, statistical significance and predictability respectively. The well validated Hypo1 was used as 3Dquery to perform virtual screening. The hits obtained from the virtual screening were subjected to various scrupulous drug-like filters such as Lipinski's rule of five and ADMET properties. Finally, six hit compounds were identified based on the molecular interaction and its electronic properties. Our final lead compound could serve as a powerful tool for the discovery of potent inhibitor as KIF11 agonists.