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.

Figure 1

NOP receptor agonist ligands used in enrichment studies. Compound numbers and names of the ligands are explained in the text.

Figure 2

“Virtual Screening Funnel” depicting various steps used in virtual screening.

Figure 3

(a) Docked conformation of Ro-2 in the orthosteric binding site of active-state NOP receptor. Ro-2 is shown as green sticks, and the active site amino acids are shown in wire mode. (b) Manual pharmacophore defined using predicted bioactive conformation of Ro-2. Yellow sphere depicts an aromatic ring. Red sphere depicts a positively charged center, and two cyan spheres depict hydrophobic features.

Figure 4

Superposition of selected 12 active-state conformations of the NOP receptor after simulated annealing of the side-chains of the active site and EL2 loop.

Figure 5

(a) Superposition of four selected ligands by Mutual Alignment “Hypothesis” obtained from Surflex-Sim. (b) Proposed bioactive conformation of Ro-64-6198 obtained from the Surflex-Sim “Hypothesis”.

Figure 6

(a) Enrichment plots for nociceptin receptor homology models: inactive (black), active (red), and after similarity search (blue). (b) Enrichment plots for inactive and initial active homology models and selected 12 nociceptin receptor conformations after simulated annealing.

Figure 7

Enrichment plots for selected homology models (bold rows in Table 1) showing the percentage of the screened database (X-axis) vs the recovered active ligands (Y-axis). Red curve depicts the enrichment curve using docking, while the black curve illustrates the consensus (combined docking and shape-based approach) enrichment curve.

Figure 8

Dose–response binding curves for selected hit compounds from Table 3. These were determined by displacement of [³H]nociceptin binding to the NOP receptor by a range of concentrations of test compounds in competition binding experiments, as described in the Methods. The binding curves were generated using Prism (GraphPad, San Diego, CA). Each point represents the mean ± S.E.M. determined in Prism (n = 3). The IC₅₀ values were determined by Prism from the binding curves and were used to derive the K_i values shown in Table 3 using the Cheng–Prusoff equation.