title: Structure-based virtual screening of the nociceptin receptor: hybrid docking and shape-based approaches for improved hit identification.
abstract: The antagonist-bound crystal structure of the nociceptin receptor (NOP), from the opioid receptor family, was recently reported along with those of the other opioid receptors bound to opioid antagonists. We recently reported the first homology model of the 'active-state' of the NOP receptor, which when docked with 'agonist' ligands showed differences in the TM helices and residues, consistent with GPCR activation after agonist binding. In this study, we explored the use of the active-state NOP homology model for structure-based virtual screening to discover NOP ligands containing new chemical scaffolds. Several NOP agonist and antagonist ligands previously reported are based on a common piperidine scaffold. Given the structure-activity relationships for known NOP ligands, we developed a hybrid method that combines a structure-based and ligand-based approach, utilizing the active-state NOP receptor as well as the pharmacophoric features of known NOP ligands, to identify novel NOP binding scaffolds by virtual screening. Multiple conformations of the NOP active site including the flexible second extracellular loop (EL2) loop were generated by simulated annealing and ranked using enrichment factor (EF) analysis and a ligand-decoy dataset containing known NOP agonist ligands. The enrichment factors were further improved by combining shape-based screening of this ligand-decoy dataset and calculation of consensus scores. This combined structure-based and ligand-based EF analysis yielded higher enrichment factors than the individual methods, suggesting the effectiveness of the hybrid approach. Virtual screening of the CNS Permeable subset of the ZINC database was carried out using the above-mentioned hybrid approach in a tiered fashion utilizing a ligand pharmacophore-based filtering step, followed by structure-based virtual screening using the refined NOP active-state models from the enrichment analysis. Determination of the NOP receptor binding affinity of a selected set of top-scoring hits resulted in identification of several compounds with measurable binding affinity at the NOP receptor, one of which had a new chemotype for NOP receptor binding. The hybrid ligand-based and structure-based methodology demonstrates an effective approach for virtual screening that leverages existing SAR and receptor structure information for identifying novel hits for NOP receptor binding. The refined active-state NOP homology models obtained from the enrichment studies can be further used for structure-based optimization of these new chemotypes to obtain potent and selective NOP receptor ligands for therapeutic development.
authors.affiliation: Astraea Therapeutics, LLC. , 320 Logue Avenue, Mountain View, California 94043, United States.
authors.forename: Pankaj R
authors.initials: PR
authors.lastname: Daga
authors.forename: Willma E
authors.initials: WE
authors.lastname: Polgar
authors.forename: Nurulain T
authors.initials: NT
authors.lastname: Zaveri
mesh_terms: D000465:Algorithms; D000701:Analgesics, Opioid / Q000737:chemistry*; D020134:Catalytic Domain; D018360:Crystallography, X-Ray; D062126:Databases, Chemical; D057166:High-Throughput Screening Assays; D006801:Humans; D008024:Ligands; D062105:Molecular Docking Simulation; D009292:Narcotic Antagonists / Q000737:chemistry*; D010880:Piperidines / Q000737:chemistry*; D011485:Protein Binding; D017433:Protein Structure, Secondary; D011957:Receptors, Opioid / Q000737:chemistry*; D012107:Research Design; D012243:Rhodopsin / Q000737:chemistry; D054852:Small Molecule Libraries / Q000737:chemistry*; D040681:Structural Homology, Protein; D013329:Structure-Activity Relationship; D013816:Thermodynamics; D014584:User-Computer Interface; D000094942:Nociceptin Receptor
chemical_list: D000701:Analgesics, Opioid; D008024:Ligands; D009292:Narcotic Antagonists; D010880:Piperidines; D011957:Receptors, Opioid; D054852:Small Molecule Libraries; D012243:Rhodopsin; D000094942:Nociceptin Receptor; C000722283:OPRL1 protein, human