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Molecular modeling and structure-based drug discovery approach reveals protein kinases as off-targets for novel anticancer drug RH1

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Abstract

Potential drug target identification and mechanism of action is an important step in drug discovery process, which can be achieved by biochemical methods, genetic interactions or computational conjectures. Sometimes more than one approach is implemented to mine out the potential drug target and characterize the on-target or off-target effects. A novel anticancer agent RH1 is designed as pro-drug to be activated by NQO1, an enzyme overexpressed in many types of tumors. However, increasing data show that RH1 can affect cells in NQO1-independent fashion. Here, we implemented the bioinformatics approach of modeling and molecular docking for search of RH1 targets among protein kinase species. We have examined 129 protein kinases in total where 96 protein kinases are in complexes with their inhibitor, 11 kinases were in the unbound state with any ligand and for 22 protein kinases 3D structure were modeled. Comparison of calculated free energy of binding of RH1 with indigenous kinase inhibitors binding efficiency as well as alignment of their pharmacophoric maps let us predict and ranked protein kinases such as KIT, CDK2, CDK6, MAPK1, NEK2 and others as the most prominent off-targets of RH1. Our finding opens new avenues in search of protein targets that might be responsible for curing cancer by new promising drug RH1 in NQO1-independent way.

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Acknowledgements

P.P.G and V.A.B were granted the travel doctoral student exchange tuition in the framework of the Erasmus Mundus EUPHRATES project (3rd Cohort), 2016–2017.

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This research was funded by Scientific Council of Lithuania (Scientific team project #MIP-033/2014); therefore, we thank the organization.

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Correspondence to Mindaugas Valius.

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Gupta, P.P., Bastikar, V.A., Kuciauskas, D. et al. Molecular modeling and structure-based drug discovery approach reveals protein kinases as off-targets for novel anticancer drug RH1. Med Oncol 34, 176 (2017). https://doi.org/10.1007/s12032-017-1011-5

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