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Title

Molecular Docking studies of D2 Dopamine receptor with Risperidone derivatives

 

Authors

Kiran Bhargava1*, Rajendra Nath1, Prahlad Kumar Seth2, Kamlesh Kumar Pant1 & Rakesh Kumar Dixit1

 

Affiliation

1Department of Pharmacology and Therapeutics, King George’s Medical University Erstwhile CSMMU, Lucknow 226003, UP, India; 2Biotech Park, Lucknow-226021, UP, India

 

Email

kbhargavaphd@gmail.com; *Corresponding author

 

Article Type

Hypothesis

 

Date

Received October 25, 2013; Revised November 13, 2013; Accepted December 18, 2013; Published January 29, 2014

 

Abstract

In this work, 3D model of D2 dopamine receptor was determined by comparative homology modeling program MODELLER. The computed model’s energy was minimized and validated using PROCHECK and Errat tool to obtain a stable model structure and was submitted in Protein Model Database (PMDB-ID: PM0079251). Stable model was used for molecular docking against Risperidone and their 15 derivatives using AutoDock 4.2, which resulted in energy-based descriptors such as Binding Energy, Ligand Efficiency, Inhib Constant, Intermol energy, vdW + Hbond + desolv Energy, Electrostatic Energy, Total Internal Energy and Torsional Energy. After that, we have built quantitative structure activity relationship (QSAR) model, which was trained and tested on Risperidone and their 15 derivatives having activity value pKi in µM. For QSAR modeling, Multiple Linear Regression model was engendered using energy-based descriptors yielding correlation coefficient r2 of 0.513. To assess the predictive performance of QSAR models, different cross-validation procedures were adopted. Our results suggests that ligand-receptor binding interactions for D2 employing QSAR modeling seems to be a promising approach for prediction of pKi value of novel antagonists against D2 receptor. 

 

Keywords

Schizophrenia, Docking, AutoDock, Risperidone analogues, D2 dopamine receptor.

 

Citation

Bhargava et al.   Bioinformation 10(1): 008-012 (2014)
 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.