Molecular docking and 3D-QSAR studies of HIV-1 protease inhibitors

Abstract

HIV-1 protease is an obligatory enzyme in the replication process of the HIV virus. The abundance of structural information on HIV-1PR has made the enzyme an attractive target for computer-aided drug design strategies. The daunting ability of the virus to rapidly generate resistant mutants suggests that there is an ongoing need for new HIV-1PR inhibitors with better efficacy profiles and reduced toxicity. In the present investigation, molecular modeling studies were performed on a series of 54 cyclic urea analogs with symmetric P2/P2' substituents. The binding modes of these inhibitors were determined by docking. The docking results also provided a reliable conformational superimposition scheme for the 3D-QSAR studies. To gain insight into the steric, electrostatic, hydrophobic and hydrogen-bonding properties of these molecules and their influence on the inhibitory activity, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed. Two different alignment schemes viz. receptor-based and atom-fit alignment, were used in this study to build the QSAR models. The derived 3D-QSAR models were found to be robust with statistically significant r(2) and r(2)(pred) values and have led to the identification of regions important for steric, hydrophobic and electronic interactions. The predictive ability of the models was assessed on a set of molecules that were not included in the training set. Superimposition of the 3D-contour maps generated from these models onto the active site of enzyme provided additional insight into the structural requirements of these inhibitors. The CoMFA and CoMSIA models were used to design some new inhibitors with improved binding affinity. Pharmacokinetic and toxicity predictions were also carried out for these molecules to gauge their ADME and safety profile. The computational results may open up new avenues for synthesis of potent HIV-1 protease inhibitors.

Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints

“…These findings align closely to comparative molecular field analysis performed on a set of HIV-1 protease inhibitors based on the cyclic urea core. 62 Although that analysis only considered ligand structure, it found that the field of favorable steric interaction overlays with residues 32, 82, 48, 50, and 84, which with the exception of residue 32 also ranked among the most important features identified in this study.…”

A combined 3D-QSAR and docking studies for the In-silicoprediction of HIV-protease inhibitors

“…The quality of the model was judged by cross-validated coefficient q 2 which should not be less than 0.5. The external predictivity was calculated by conventional correlation coefficient r 2 [22,23]. …”

Exploration of the structural requirements of HIV-protease inhibitors using pharmacophore, virtual screening and molecular docking approaches for lead identification

“…The pharmacoinformatics approaches including structure activity relationship (SAR), pharmacophore, virtual screening and molecular docking have become pivotal techniques in the pharmaceutical industry for lead discovery. Many groups have applied the pharmacoinformatics approaches to identify inhibitors [23][24][25][26][27][28][29] against HIV protease. Hence the current study explores the binding preferences of the inhibitory molecules of HIV protease in terms of space modelling study and virtual screening along with molecular docking.…”