Ebuilding, but all loop conformations that did not place the Ca

Ebuilding, but all loop conformations that did not place the Ca and Cc atoms of ?His2647.29 within 0.8 A of the equivalent positions of these atoms in the A2AAR structure 3EML were discarded. The sidechain orientations for all other residues were sampled and minimized together with 8 (Figure 5), the ligand that was used in this refinement. All optimizations in this and the third round were done with PLOP and the pose of 8 was the one obtained from docking. Refinements in the third round again used the most selective ligand identified in the previous rounds (8) and optimized the sidechains of the same residues as before. Multiple structures were generated, clustered by sidechain conformations and assessed by calculating their ability to rank the ligands over the decoys of rounds one and two (assessed via the value of the area under the curve [logAUC] of receiver-operator characteristic [logROC] plots). For each sidechain conformation cluster, the best structure according to the logAUC criterion was kept and used in docking (models C and D; Fig. 1C and 1D, respectively).DockingAll calculations were carried out using DOCK3.5.54 [29?2] and the approximately 2.2 M molecules of ZINC’s lead-like subset [33]. The molecules in this subset are between 250 and 350 g/mol in molecular weight, have less than 7 rotatable bonds, and have an xlogP between 2.5 and 3.5. The docking spheres used as anchor points in the binding site to position the database molecules in the orthosteric pocket were calculated based on the 78919-13-8 supplier heavy-atom positions of carazolol and 1 when superimposing the backbone atoms of the b2-adrenergic receptor (PDB 1480666 code 2RH1) and A2AAR, respectively, with the A1AR model. Where necessary, spheres were moved manually to obtain a more homogenous distribution. During docking, every molecule was fitted onto spheres chosen by the algorithm based on the similarity of the distances between the spheres and corresponding heavy atoms in the molecules. Each molecule pose was minimized for 25 steps with the simplex method. Finally, the binding affinity was estimated by adding the electrostatic and van der Waals interaction energies and correcting for solvation penalty. These energy terms were obtained from precalculated values stored on cubic grids. To emphasize the highly conserved interaction of adenosine with Asn2546.55, partial charges on the polar side chain atoms were amplified by 0.4 units in such a way that the overall 69056-38-8 charge of the residue remained neutral. After docking, the top 500 poses were inspected visually to filter outIn Silico Screening for A1AR AntagonistsFigure 4. Chart 1. Reference compounds (known selective A1AR antagonists) mentioned in the text. Ki values are as follows, with targets other than human A1AR in parentheses: 1: Ki 0.8 nM [8]; 2: Ki 18 nM; 3: Ki 1 nM; 4: Ki 1 nM [11]; 5: Ki 3 nM (bovine A1AR [20]); 6: Ki 584 nM [21]. doi:10.1371/journal.pone.0049910.gmolecules with unsatisfied hydrogen bond donors or acceptors, incorrect protonation states, unlikely binding modes due to incorrect parametrization, or highly strained conformations. The selected molecules were acquired from their respective vendors as listed in the ZINC database.Selectivity Ratios of known AR LigandsAll ligands annotated with an activity value against at least one of the investigated AR subtypes were downloaded from version 12 of the ChEMBL database [34]. The data was made uniform by keeping only affinities measured as Ki. Ki-values described as “greate.Ebuilding, but all loop conformations that did not place the Ca and Cc atoms of ?His2647.29 within 0.8 A of the equivalent positions of these atoms in the A2AAR structure 3EML were discarded. The sidechain orientations for all other residues were sampled and minimized together with 8 (Figure 5), the ligand that was used in this refinement. All optimizations in this and the third round were done with PLOP and the pose of 8 was the one obtained from docking. Refinements in the third round again used the most selective ligand identified in the previous rounds (8) and optimized the sidechains of the same residues as before. Multiple structures were generated, clustered by sidechain conformations and assessed by calculating their ability to rank the ligands over the decoys of rounds one and two (assessed via the value of the area under the curve [logAUC] of receiver-operator characteristic [logROC] plots). For each sidechain conformation cluster, the best structure according to the logAUC criterion was kept and used in docking (models C and D; Fig. 1C and 1D, respectively).DockingAll calculations were carried out using DOCK3.5.54 [29?2] and the approximately 2.2 M molecules of ZINC’s lead-like subset [33]. The molecules in this subset are between 250 and 350 g/mol in molecular weight, have less than 7 rotatable bonds, and have an xlogP between 2.5 and 3.5. The docking spheres used as anchor points in the binding site to position the database molecules in the orthosteric pocket were calculated based on the heavy-atom positions of carazolol and 1 when superimposing the backbone atoms of the b2-adrenergic receptor (PDB 1480666 code 2RH1) and A2AAR, respectively, with the A1AR model. Where necessary, spheres were moved manually to obtain a more homogenous distribution. During docking, every molecule was fitted onto spheres chosen by the algorithm based on the similarity of the distances between the spheres and corresponding heavy atoms in the molecules. Each molecule pose was minimized for 25 steps with the simplex method. Finally, the binding affinity was estimated by adding the electrostatic and van der Waals interaction energies and correcting for solvation penalty. These energy terms were obtained from precalculated values stored on cubic grids. To emphasize the highly conserved interaction of adenosine with Asn2546.55, partial charges on the polar side chain atoms were amplified by 0.4 units in such a way that the overall charge of the residue remained neutral. After docking, the top 500 poses were inspected visually to filter outIn Silico Screening for A1AR AntagonistsFigure 4. Chart 1. Reference compounds (known selective A1AR antagonists) mentioned in the text. Ki values are as follows, with targets other than human A1AR in parentheses: 1: Ki 0.8 nM [8]; 2: Ki 18 nM; 3: Ki 1 nM; 4: Ki 1 nM [11]; 5: Ki 3 nM (bovine A1AR [20]); 6: Ki 584 nM [21]. doi:10.1371/journal.pone.0049910.gmolecules with unsatisfied hydrogen bond donors or acceptors, incorrect protonation states, unlikely binding modes due to incorrect parametrization, or highly strained conformations. The selected molecules were acquired from their respective vendors as listed in the ZINC database.Selectivity Ratios of known AR LigandsAll ligands annotated with an activity value against at least one of the investigated AR subtypes were downloaded from version 12 of the ChEMBL database [34]. The data was made uniform by keeping only affinities measured as Ki. Ki-values described as “greate.