Title

A QSAR model of Olanzapine derivatives as potential inhibitors for 5-HT2A Receptor

Authors

Pooja Mitra, Aishwarya Rastogi, Mayank Rajpoot, Ajay Kumar, Vivek Srivastava*

Affiliation

¹Department of Biotechnology, Rama University Uttar Pradesh, Kanpur, India;

Email

viveksrivastavabio@gmail.com

Article Type

Hypothesis

Date

Received July 22, 2017; Accepted July 23, 2017; Published October 31, 2017

Schizophrenia is a complex, chronic mental disorder, affecting about 21 million people worldwide. It is characterized by symptoms, including distortions in thinking, perception, emotions, disorganized speech, sense of self and behavior. Recently, a numbers of marketed drugs for Schizophrenia are available against dopamine D2 and serotonin 5-HT2A receptors. Here, we docked Olanzapine derivatives (collected from literature) with 5-HT2A Receptor using the program AutoDock 4.2. The docked protein inhibitor complex structure was optimized using molecular dynamics simulation for 5ps with the CHARMM-22 force field using NAMD (NAnoscale Molecular Dynamics program) incorporated in visual molecular dynamics (VMD 1.9.2) and then evaluating the stability of complex structure by calculating RMSD values. NAMD is a parallel, object-oriented molecular dynamics code designed for high-performance simulation of large biomolecular systems. A quantitative structure activity relationship (QSAR) model was built using energy-based descriptors as independent variable and pKi value as dependent variable of eleven known Olanzapine derivatives with 5-HT2A Receptor, yielding correlation coefficient r2 of 0.63861. The predictive performance of QSAR model was assessed using different crossvalidation procedures. Our results suggest that a ligand-receptor binding interaction for 5-HT2A receptor using a QSAR model is promising approach to design more potent 5-HT2A receptor inhibitors prior to their synthesis.

Keywords

Schizophrenia, 5-HT2A, Receptor, Olanzapine derivatives, AutoDock 4.2, NAMD

Citation

Mitra et al. Bioinformation 13(10): 339-342 (2017)

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.