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10.1007/s10822-015-9831-x http://scihub22266oqcxt.onion/10.1007/s10822-015-9831-x C4395532!4395532!25616366     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Comput+Aided+Mol+Des 2015 ; 29 (5): 421-39 Nephropedia Template TP {{tp|p=25616366|t=2015. Pharmacophore modeling improves virtual screening for novel peroxisome proliferator-activated receptor-gamma ligands |pdf=|usr=}}{{25616366}} gab.com Text Pharmacophore modeling improves virtual screening for novel peroxisome proliferator-activated receptor-gamma ligands JO: J Comput Aided Mol Des http://www.kidney.de/pget.php?&tw=1&pmid=25616366 #FOAvip Peroxisome proliferator-activated receptor-gamma (PPAR?) is a nuclear hormone receptor involved in regulating various metabolic and immune processes. The PPAR family of receptors possesses a large binding cavity that imparts promiscuity of ligand binding not common to other nuclear receptors. This feature increases the challenge of using computational methods to identify PPAR ligands that will dock favorably into a structural model. Utilizing both ligand- and structure-based pharmacophore methods, we sought to improve agonist prediction by grouping ligands according to pharmacophore features, and pairing models derived from these features with receptor structures for docking. For 22 of the 33 receptor structures evaluated we observed an increase in true positive rate (TPR) when screening was restricted to compounds sharing molecular features found in rosiglitazone. A combination of structure models used for docking resulted in a higher TPR (40%) when compared to docking with a single structure model (less than 20%). Prediction was also improved when specific protein-ligand interactions between the docked ligands and structure models were given greater weight than the calculated free energy of binding. A large-scale screen of compounds using a marketed drug database verified the predictive ability of the selected structure models. This study highlights the steps necessary to improve screening for PPAR? ligands using multiple structure models, ligand-based pharmacophore data, evaluation of protein-ligand interactions, and comparison of docking datasets. The unique combination of methods presented here holds potential for more efficient screening of compounds with unknown affinity for PPAR? that could serve as candidates for therapeutic development. Twit Text FOAVip Pharmacophore modeling improves virtual screening for novel peroxisome proliferator-activated receptor-gamma ligands ????J Comput Aided Mol Des http://www.kidney.de/pget.php?&tw=1&pmid=25616366 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25616366 #FOAvip English Wikipedia <ref>{{Cite pmid|25616366}}</ref> Pharmacophore modeling improves virtual screening for novel peroxisome proliferator-activated receptor-gamma ligands #MMPMID25616366 Lewis SN; Garcia Z; Hontecillas R; Bassaganya-Riera J; Bevan DR J Comput Aided Mol Des 2015[May]; 29 (5): 421-39 PMID25616366show ga Peroxisome proliferator-activated receptor-gamma (PPAR?) is a nuclear hormone receptor involved in regulating various metabolic and immune processes. The PPAR family of receptors possesses a large binding cavity that imparts promiscuity of ligand binding not common to other nuclear receptors. This feature increases the challenge of using computational methods to identify PPAR ligands that will dock favorably into a structural model. Utilizing both ligand- and structure-based pharmacophore methods, we sought to improve agonist prediction by grouping ligands according to pharmacophore features, and pairing models derived from these features with receptor structures for docking. For 22 of the 33 receptor structures evaluated we observed an increase in true positive rate (TPR) when screening was restricted to compounds sharing molecular features found in rosiglitazone. A combination of structure models used for docking resulted in a higher TPR (40%) when compared to docking with a single structure model (less than 20%). Prediction was also improved when specific protein-ligand interactions between the docked ligands and structure models were given greater weight than the calculated free energy of binding. A large-scale screen of compounds using a marketed drug database verified the predictive ability of the selected structure models. This study highlights the steps necessary to improve screening for PPAR? ligands using multiple structure models, ligand-based pharmacophore data, evaluation of protein-ligand interactions, and comparison of docking datasets. The unique combination of methods presented here holds potential for more efficient screening of compounds with unknown affinity for PPAR? that could serve as candidates for therapeutic development. äPharmacophore modeling improves virtual screening for novel peroxisome(epmc).pdf DeepDyve Pubget Overpricing