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10.1021/cb500696t http://scihub22266oqcxt.onion/10.1021/cb500696t C4301084!4301084 !25420233     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=25420233 &cmd=llinks): Failed to open stream: HTTP request failed! 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DFGmodel: predicting protein kinase structures in inactive states for structure-based discovery of type-II inhibitors |pdf=|usr=}}{{25420233 }} gab.com Text DFGmodel: predicting protein kinase structures in inactive states for structure-based discovery of type-II inhibitors JO: ACS Chem Biol http://www.kidney.de/pget.php?&tw=1&pmid=25420233 #FOAvip Protein kinases exist in equilibrium of active and inactive states, in which the aspartate-phenylalanine-glycine motif in the catalytic domain undergoes conformational changes that are required for function. Drugs targeting protein kinases typically bind the primary ATP-binding site of an active state (type-I inhibitors) or utilize an allosteric pocket adjacent to the ATP-binding site in the inactive state (type-II inhibitors). Limited crystallographic data of protein kinases in the inactive state hampers the application of rational drug discovery methods for developing type-II inhibitors. Here, we present a computational approach to generate structural models of protein kinases in the inactive conformation. We first perform a comprehensive analysis of all protein kinase structures deposited in the Protein Data Bank. We then develop DFGmodel, a method that takes either a known structure of a kinase in the active conformation or a sequence of a kinase without a structure, to generate kinase models in the inactive conformation. Evaluation of DFGmodel's performance using various measures indicates that the inactive kinase models are accurate, exhibiting RMSD of 1.5 Å or lower. The kinase models also accurately distinguish type-II kinase inhibitors from likely nonbinders (AUC > 0.70), suggesting that they are useful for virtual screening. Finally, we demonstrate the applicability of our approach with three case studies. For example, the models are able to capture inhibitors with unintended off-target activity. Our computational approach provides a structural framework for chemical biologists to characterize kinases in the inactive state and to explore new chemical spaces with structure-based drug design. Twit Text FOAVip DFGmodel: predicting protein kinase structures in inactive states for structure-based discovery of type-II inhibitors ????ACS Chem Biol http://www.kidney.de/pget.php?&tw=1&pmid=25420233 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25420233 #FOAvip English Wikipedia <ref>{{Cite pmid|25420233 }}</ref> DFGmodel: predicting protein kinase structures in inactive states for structure-based discovery of type-II inhibitors #MMPMID25420233 Ung PM ; Schlessinger A ACS Chem Biol 2015[Jan]; 10 (1 ): 269-78 PMID25420233 show ga Protein kinases exist in equilibrium of active and inactive states, in which the aspartate-phenylalanine-glycine motif in the catalytic domain undergoes conformational changes that are required for function. Drugs targeting protein kinases typically bind the primary ATP-binding site of an active state (type-I inhibitors) or utilize an allosteric pocket adjacent to the ATP-binding site in the inactive state (type-II inhibitors). Limited crystallographic data of protein kinases in the inactive state hampers the application of rational drug discovery methods for developing type-II inhibitors. Here, we present a computational approach to generate structural models of protein kinases in the inactive conformation. We first perform a comprehensive analysis of all protein kinase structures deposited in the Protein Data Bank. We then develop DFGmodel, a method that takes either a known structure of a kinase in the active conformation or a sequence of a kinase without a structure, to generate kinase models in the inactive conformation. Evaluation of DFGmodel's performance using various measures indicates that the inactive kinase models are accurate, exhibiting RMSD of 1.5 Å or lower. The kinase models also accurately distinguish type-II kinase inhibitors from likely nonbinders (AUC > 0.70), suggesting that they are useful for virtual screening. Finally, we demonstrate the applicability of our approach with three case studies. For example, the models are able to capture inhibitors with unintended off-target activity. Our computational approach provides a structural framework for chemical biologists to characterize kinases in the inactive state and to explore new chemical spaces with structure-based drug design. |*Models, Chemical [MESH] |*Molecular Docking Simulation [MESH] |Binding Sites [MESH] |Computational Biology [MESH] |Databases, Protein [MESH] |Drug Discovery/*methods [MESH] |Oligopeptides/*chemistry [MESH] |Protein Conformation [MESH] |Protein Kinase Inhibitors/*chemistry/pharmacology [MESH] |Protein Kinases/*chemistry/metabolism [MESH]DFGmodel: predicting protein kinase structures in inactive states for (epmc).pdf DeepDyve Pubget Overpricing