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Study of V2 Vasopressin Receptor Hormone Binding Site Using in Silico Methods



Sebti Y1, 2 ; Sardari S2 ; Sadeghi HMM1 ; Ghahremani MH3 ; Innamorati G4
Authors
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Authors Affiliations
  1. 1. Department of Pharmaceutical Biotechnology, Isfahan Pharmaceutical Sciences Research Center, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
  3. 3. Department of Pharmacology and Toxicology, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Section of Immunology, Department of Pathology, University of Verona, Verona, Italy

Source: Research in Pharmaceutical Sciences Published:2015

Abstract

The antidiuretic effect of arginine vasopressin (AVP) is mediated by the vasopressin V2 receptor. The docking study of AVP as a ligand to V2 receptor helps in identifying important amino acid residues that might be involved in AVP binding for predicting the lowest free energy state of the protein complex. Whereas previous researchers were not able to detect the exact site of the ligand-receptor binding, we designed the current study to identify the vasopressin V2 receptor hormone binding site using bioinformatic methods. The 3D structure of nonapeptide hormone vasopressin was extracted from Protein Data Bank. Since no suitable template resembling V2 receptor was found, an ab initio approach was chosen to model the protein receptor. Using protein docking methods such as Hex protein-protein docking, the model of V2 receptor was docked to the peptide ligand AVP to identify possible binding sites.The residues that involved in binding site are W293, W296, D297, A300, and P301. The lowest free energy state of the protein complex was predicted after mutation in the above residues. The amount of gained energies permits us to compare the mutant forms with native forms and help to asses critical changes such as positive and negative mutations followed by ranking the best mutations. Based on the mutation/docking predictions, we found some mutants such as W293D and A300E possess positively inducing effect in ligand binding and some of them such as A300R present negatively inducing effect in ligand binding.