Homology modeling and 3D-QSAR study of benzhydrylpiperazine δ opioid receptor agonists

The binding affinity of a series of benzhydrylpiperazine δ opioid receptor agonists were pooled and evaluated by using 3D-QSAR and homology modeling/molecular docking methods. Ligand-based CoMFA and CoMSIA 3D-QSAR analyses with 46 compounds were performed on benzhydrylpiperazine analogues by taking the most active compound BW373U86 as the template. The models were generated successfully with q² value of 0.508 and r² value of 0.964 for CoMFA, and q² value of 0.530 and r² value of 0.927 for CoMSIA. The predictive capabilities of the two models were validated on the test set with R²_pred value of 0.720 and 0.814, respectively. The CoMSIA model appeared to work better in this case. A homology model of active form of δ opioid receptor was established by Swiss-Model using a reported crystal structure of active μ opioid receptor as a template, and was further optimized using nanosecond scale molecular dynamics simulation. The most active compound BW373U86 was docked to the active site of δ opioid receptor and the lowest energy binding pose was then used to identify binding residues such as s Gln105, Lys108, Leu125, Asp128, Tyr129, Leu200, Met132, Met199, Lys214, Trp274, Ile277, Ile304 and Tyr308. The docking and 3D-QSAR results showed that hydrogen bond and hydrophobic interactions played major roles in ligand-receptor interactions. Our results highlight that an approach combining structure-based homology modeling/molecular docking and ligand-based 3D-QSAR methods could be useful in designing of new opioid receptor agonists.