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International Journal of Computational Bioinformatics and In Silico Modeling
2013: Volume-2 Issue-5
ISSN: 2320-0634

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ABSTRACT   REFERENCES  
International Journal of Computational Bioinformatics and In Silico Modeling 2(5) 2013: 223-233

Molecular Dynamics, Docking and QSAR analysis of Napthoquinone derivatives as Topoisomerase I inhibitors


Sharvari Kulkarni1*, Pallavi Patil1, Bastikar Virupaksha2,4, Gupte Alpana3,4, Khadke Prashant2 and Shruti Baikerikar5

1Kolhapur Institute of Technology, Kolhapur, India.
2Department of Bioinformatics, JJT University, Rajasthan, India.
3Rajiv Gandhi Institute of Biotechnology and IT, Bharti Vidyapeeth, Pune, Maharashtra, India.
4Department of Structural Biology, University of Rome Tor Vergata, Rome, Italy.
5Thadomal Shahani Engineering College, Mumbai.

* Corresponding Author

ABSTRACT

The Topoisomerase I enzyme has become an attractive target for the treatment of cancer. In this paper molecular dynamics, 2D and 3D QSAR and molecular docking studies were performed on 90 naphthoquinone derivatives as Topoisomerase I inhibitors by using the human Topo I-DNA cleavable complex. This model has the drug intercalated with its planar pharmacophore between +1 and -1 bp flanking cleavage site. The docking analysis focuses the importance of Asn 722 and Thr 718 residues as necessary active site interaction residues. The 2D and 3D QSAR models also gave satisfactory results with r2 as 0.6298 and 0.7868 respectively. The docking analysis and the biological activity are also correlated by the QSAR equations, thus validating the binding analysis results. These results are also useful in order to understand the structural features required to improve the performance of naphthoquinone derivatives as Topoisomerase I inhibitors and include the pharmacophoric features to design and develop new better analogs.


Copyright © 2013 | AIZEON publishers | All rights reserved

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Citation: S Kulkarni et al. (2013). Molecular Dynamics, Docking and QSAR analysis of Napthoquinone derivatives as Topoisomerase I inhibitors Int J Comput Bioinfo In Silico Model 2(5): 223-233

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