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

Abstract

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 R2pred 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.