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Receptor- and ligand-based study of fullerene analogues: comprehensive computational approach including quantum-chemical, QSAR and molecular docking simulations

Lucky AhmedInterdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, 1400 J.R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA. [email protected]Bakhtiyor RasulevInterdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, 1400 J.R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USAMalakhat A. TurabekovaDepartment of Civil and Environmental Engineering, Jackson State University, 1400 J.R. Lynch Street, P.O. Box 17068, Jackson, MS 39217, USADanuta LeszczyńskaDepartment of Civil and Environmental Engineering, Jackson State University, 1400 J.R. Lynch Street, P.O. Box 17068, Jackson, MS 39217, USAJerzy LeszczyńskiInterdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, 1400 J.R. Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA

2013en

ABI

Annotatsiya

Fullerene and its derivatives have potential antiviral activity due to their specific binding interactions with biological molecules. In this study fullerene derivatives were investigated by the synergic combination of three approaches: quantum-mechanical calculations, protein-ligand docking and quantitative structure-activity relationship methods. The protein-ligand docking studies and improved structure-activity models have been able both to predict binding affinities for the set of fullerene-C60 derivatives and to help in finding mechanisms of fullerene derivative interactions with human immunodeficiency virus type 1 aspartic protease, HIV-1 PR. Protein-ligand docking revealed several important molecular fragments that are responsible for the interaction with HIV-1 PR. In addition, a density functional theory method has been utilized to identify the optimal geometries and predict physico-chemical parameters of the studied compounds. The 5-variable GA-MLRA based model showed the best predictive ability (r(2)training = 0.882 and r(2)test = 0.738), with high internal and external correlation coefficients.

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Identifikatorlar

DOI: 10.1039/c3ob40878g

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