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Current Enzyme Inhibition


ISSN (Print): 1573-4080
ISSN (Online): 1875-6662

Research Article

CoMFA, CoMSIA and Docking Studies of Saquinavir Based Peptidomimetic Inhibitors of HIV-1 Protease

Author(s): Vandana Saini, Sakshi Piplani, Amita S. Dang and Ajit Kumar

Volume 12, Issue 2, 2016

Page: [161 - 169] Pages: 9

DOI: 10.2174/1573408011666151020213100

Price: $65


HIV protease has been one of the most considered target sites to combat HIV infection and Saquinavir is the forerunner of all therapeutic agents targeting the same. There has always been a quest for new HIV-protease inhibitor for AIDS-treatment. The current study deals with in-silico attempt to develop 3D-QSAR models based on CoMFA and CoMSIA studies, to design and evaluate new saquinavir based chemical entities for their anti-HIV Protease activity. Optimal CoMFA and CoMSIA models were generated using a set of saquinavir based 23 molecules (18 training and 05 test set). The leave-one out cross validation correlation coefficients were q2CoMFA= 0.681 and q2CoMSIA= 0.684. The correlation between the experimental activities and cross validated/predicted activities of the test set molecules was high and reflected robustness of the models (r2CoMFA= 0.967 and r2CoMSIA= 0.988). The CoMFA model suggested 76.4% steric and 23.6% electrostatic field contribution while the optimal CoMSIA model revealed that electrostatic, hydrophobic and hydrogen bonding interactions were significantly required for HIV-protease inhibition. The models were subjected to molecular docking studies for in-silico validation using different set of molecules derived from ZINC database with ≥ 95% similarity with saquinavir. 07 molecules having activity greater than saquinavir, as predicted, in common, by CoMFA and CoMSIA models, were docked against HIV-1 protease. The dock score and the predicted activity were observed to be significantly correlated with r2= -0.7142 (CoMFA); -0.6219 (CoMSIA) while the binding patterns were observed to be comparable to that of Saquinavir.

Keywords: AIDS, QSAR, comparative molecular field analysis, comparative molecular similarity indices analysis.

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