In the last decade the application of computational methodology in the all medicinal chemistry and biology fields has found an amazing development. All these efforts were focused on the searching of new leads possessing a specific bio-molecular target affinity.[1-4] In this study we focused on the identification of potential Hsp90 inhibitors by a Molecular Docking/Pharmacophore based virtual screening protocol on a subset of compounds (purchasable and non-purchasable) available in ZINC Database (http://zinc.docking.org). The entire subset database (~5 millions compounds) was submitted to a two accuracy levels molecular docking screening, as available in Glide, using a Hsp90 X-ray structure (PDB ID: 2BZ5). Further, the top scored compounds were discriminated using two different sets of pharmacophore hypotheses (Docking-derived and in-vacuo derived), both built using a training set of 49 known Hsp90 inhibitors (Binding Database [5]), belonging to three chemical classes (purines, pyrazoles, sulfones). In the case of Docking-derived pharmacophores, the training set was firstly docked into Hsp90 active site and the frozen top ranked conformations were used. In the case of in-vacuo derived pharmacophores, instead, a maximum of 103 conformers were generated for each compound belonging to the training set. This approach allowed to recognize a series of derivatives, presenting various core structures, which satisfy each pharmacophore template. Another approach by means the multivariate analysis was applied on an in house database of designed compounds incorporating triazolopyrimidine core structures. By the first approach dihydropyrazole and 2-amino-3-cyanopyridine core structures were selected. Meanwhile by the multivariate approach, pyridino/benzothieno[2,3-e][1,2,3]triazolo[1,5-a]pyrimidines emerged. The selected derivatives were synthesized in order to verify the molecular modeling results. Preliminary analysis of their biological activity performed on a panel of 60 tumor cell lines showed excellent antiproliferative activities (nanomolar scale) with low cytotoxicity and actually are undergoing in vivo screenings by NCI (Bethesda, USA). Moreover the synthesized compounds are currently under investigation to evidence their Hsp90 binding affinity. 1) A.M. Almerico, M. Tutone, A. Lauria, J. Comput. Aided Mol. Des., 2008, 22(5), 287-297. 2) A. Lauria, M. Ippolito, A.M. Almerico, J. Mol. Graphics Modell., 2009, 27(6), 712-722. 3) A. Lauria, M. Ippolito, A.M. Almerico, QSAR & Comb. Sci., 2009, 28, 387-395. 4) A. Lauria, M. Ippolito, M. Fazzari, M. Tutone, F. Di Blasi, F. Mingoia, A.M. Almerico, J. Mol. Graphics Modell., 2010, 29, 72-81. 5 T. Liu, Y. Lin, X. Wen, R.N. Jorrisen, M.K. Gilson, Nucleic Acids Res. 2007, 35, D198-D201.

"The long way from Molecular Modeling to Biological Screening: The discovery of new HSP90 Inhibitors."

Francesco Mingoia;
2010

Abstract

In the last decade the application of computational methodology in the all medicinal chemistry and biology fields has found an amazing development. All these efforts were focused on the searching of new leads possessing a specific bio-molecular target affinity.[1-4] In this study we focused on the identification of potential Hsp90 inhibitors by a Molecular Docking/Pharmacophore based virtual screening protocol on a subset of compounds (purchasable and non-purchasable) available in ZINC Database (http://zinc.docking.org). The entire subset database (~5 millions compounds) was submitted to a two accuracy levels molecular docking screening, as available in Glide, using a Hsp90 X-ray structure (PDB ID: 2BZ5). Further, the top scored compounds were discriminated using two different sets of pharmacophore hypotheses (Docking-derived and in-vacuo derived), both built using a training set of 49 known Hsp90 inhibitors (Binding Database [5]), belonging to three chemical classes (purines, pyrazoles, sulfones). In the case of Docking-derived pharmacophores, the training set was firstly docked into Hsp90 active site and the frozen top ranked conformations were used. In the case of in-vacuo derived pharmacophores, instead, a maximum of 103 conformers were generated for each compound belonging to the training set. This approach allowed to recognize a series of derivatives, presenting various core structures, which satisfy each pharmacophore template. Another approach by means the multivariate analysis was applied on an in house database of designed compounds incorporating triazolopyrimidine core structures. By the first approach dihydropyrazole and 2-amino-3-cyanopyridine core structures were selected. Meanwhile by the multivariate approach, pyridino/benzothieno[2,3-e][1,2,3]triazolo[1,5-a]pyrimidines emerged. The selected derivatives were synthesized in order to verify the molecular modeling results. Preliminary analysis of their biological activity performed on a panel of 60 tumor cell lines showed excellent antiproliferative activities (nanomolar scale) with low cytotoxicity and actually are undergoing in vivo screenings by NCI (Bethesda, USA). Moreover the synthesized compounds are currently under investigation to evidence their Hsp90 binding affinity. 1) A.M. Almerico, M. Tutone, A. Lauria, J. Comput. Aided Mol. Des., 2008, 22(5), 287-297. 2) A. Lauria, M. Ippolito, A.M. Almerico, J. Mol. Graphics Modell., 2009, 27(6), 712-722. 3) A. Lauria, M. Ippolito, A.M. Almerico, QSAR & Comb. Sci., 2009, 28, 387-395. 4) A. Lauria, M. Ippolito, M. Fazzari, M. Tutone, F. Di Blasi, F. Mingoia, A.M. Almerico, J. Mol. Graphics Modell., 2010, 29, 72-81. 5 T. Liu, Y. Lin, X. Wen, R.N. Jorrisen, M.K. Gilson, Nucleic Acids Res. 2007, 35, D198-D201.
2010
Istituto per lo Studio dei Materiali Nanostrutturati - ISMN
978-88-904055-2-5
HSP90
molecular modeling
antiproliferative activity
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/255282
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