Fullerene quinazolinone conjugates targeting Mycobacterium tuberculosis: a combined molecular docking, QSAR, and ONIOM approach
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Fullerene and its derivatives may bind to biological molecules, causing inhibitory effects. In this context, investigations of interactions of fullerene-based conjugates with proteins are of general interest. Particularly, fullerene and its derivatives demonstrate antibacterial properties; and one of the potential targets for drug design and health therapy is the inhibition of 6-oxopurine phosphoribosyltransferase in Mycobacterium tuberculosis (PDB code: 4RHY). In this article, the binding interactions between a series of quinazoline-4(3H)-ones and their fullerene derivatives with the target transferase were computationally investigated. Initially, we developed predictive quantitative structure-activity relationships (QSAR) models. Next, we introduced a simplified calculation schema that allows to evaluate relative binding affinities and to reveal specific mechanisms of action. For this purpose, the molecular docking approach was utilized to identify the native poses of the 18 transferase inhibitors. The binding pocket of the target protein was isolated and semi-empirical, and hybrid ONIOM scoring functions at different levels of theory were used to treat the ligands and the isolated binding pocket. The agreement within the calculated binding-free energies trends, as well as the agreement with the experimental data, suggests that the developed calculation schema can be used to estimate relative binding affinities towards 4RH. The combination of quantum-chemical models and QSAR models could be applied for future design of new selective inhibitors.
KeywordsMolecular docking Density functional theory Semi-empirical QSAR Mycobacterium tuberculosis
The authors thank the NSF-CREST Interdisciplinary Nanotoxicity Center’s NSF-CREST grants # HRD-0833178 and # HRD-1547754 for the support, and want to acknowledge the Extreme Science and Engineering Discovery Environment (XSEDE) by National Science Foundation grant number OCI-1053575 and XSEDE award allocation number DMR110088. The authors thank the Mississippi Center for Supercomputer Research (Oxford, MS) for a generous allotment of computer time. N.S. expresses her gratitude to BioSolveIT GmbH for licensed product. Results obtained using BioSolveIT software are the part of research conducted in the framework of Scientific Challenge fall 2016 organized by BioSolveIT GmbH.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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