Computer Modeling of Human Delta Opioid Receptor

• Main Authors: Tatyana Dzimbova, Fatima Sapundzhi, Nevena Pencheva, Peter Milanov
• Format: Article
• Language: English
• Published: Bulgarian Academy of Sciences 2013-04-01
• Series: International Journal Bioautomation
• Subjects: Delta opioid receptor; DOR; Enkephalin; Docking
• Online Access: http://www.clbme.bas.bg/bioautomation/2013/vol_17.1/files/17.1_01.pdf

The development of selective agonists of δ-opioid receptor as well as the model of interaction of ligands with this receptor is the subjects of increased interest. In the absence of crystal structures of opioid receptors, 3D homology models with different templates have been reported in the literature. The problem is that these models are not available for widespread use. The aims of our study are: (1) to choose within recently published crystallographic structures templates for homology modeling of the human δ-opioid receptor (DOR); (2) to evaluate the models with different computational tools; and (3) to precise the most reliable model basing on correlation between docking data and in vitro bioassay results. The enkephalin analogues, as ligands used in this study, were previously synthesized by our group and their biological activity was evaluated. Several models of DOR were generated using different templates. All these models were evaluated by PROCHECK and MolProbity and relationship between docking data and in vitro results was determined. The best correlations received for the tested models of DOR were found between efficacy (erel) of the compounds, calculated from in vitro experiments and Fitness scoring function from docking studies. New model of DOR was generated and evaluated by different approaches. This model has good GA341 value (0.99) from MODELLER, good values from PROCHECK (92.6% of most favored regions) and MolProbity (99.5% of favored regions). Scoring function correlates (Pearson r = -0.7368, p-value = 0.0097) with erel of a series of enkephalin analogues, calculated from in vitro experiments. So, this investigation allows suggesting a reliable model of DOR. Newly generated model of DOR receptor could be used further for in silico experiments and it will give possibility for faster and more correct design of selective and effective ligands for δ-opioid receptor.