Identification of Novel HIV‐1 Integrase Inhibitors Using Shape‐Based Screening, QSAR, and Docking Approach

Abstract

The objective of this study is to identify novel HIV-1 integrase (IN) inhibitors. Here, shape-based screening and QSAR have been successfully implemented to identify the novel inhibitors for HIV-1 IN, and in silico validation is performed by docking studies. The 2D QSAR model of benzodithiazine derivatives was built using genetic function approximation (GFA) method with good internal (cross-validated r 2 = 0.852) and external prediction (r 2 pred = 0:650). Best docking pose of highly active molecule of the benzodithiazine derivatives was used as a template for shape-based screening of ZINC database. Toxicity prediction was also performed using Deductive Estimation of Risk from Existing Knowledge (DEREK) program to filter non-toxic molecules. Inhibitory activities of screened non-toxic molecules were predicted using derived QSAR models. Active, non-toxic screened molecules were also docked into the active site of HIV-1 IN using AUTO-DOCK and DOCK program. Some molecules docked similarly as highly active molecule of the benzodithiazine derivatives. These molecules also followed the same docking interactions in both the programs. Finally, four benzodithiazine derivatives were identified as novel HIV-1 integrase inhibitors based on QSAR predictions and docking interactions. ADME properties of these molecules were also computed using Discovery Studio.

Development of HIV-1 integrase inhibitors: recent molecular modeling perspectives

“…The 2D contribution maps identified a favored carbonyl-hydroxy-aromatic motif, and the 3D maps further suggested that bulky groups in the meta and para positions would increase its biological activity. Similar schemes can also be seen in the studies of Gupta et al [37] and de Carvalho et al [34]. For 3D-QSAR, the alignment of compound structures is an important step, although there is no golden rule.…”

“…Absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) filtering is efficient for removing those compounds that cannot sustain further clinical examinations [86]. After the first step of screening, Gupta et al [37] found that most of the screened molecules had a similar scaffold. The screened 300 hits were further subjected to ADME/Tox and QSAR studies, and only 22 molecules exhibited nontoxic and good QSAR predictions.…”

“…After two steps of dimensionality reduction, a 2D-QSAR model of curcumin derivatives was obtained using multiple linear regressions (MLR) [32] and showed statistically significance (q 2 = 0.825, r 2 = 0.891) and good predictive (r 2 pred ¼ 0:849). Early in 2012, Gupta et al [37] published a 2D-QSAR model of benzodithiazine derivatives with good internal and external prediction (q 2 = 0.852, r 2 = 0.922, r 2 pred ¼ 0:650) using genetic function approximation (GFA). PLS is also used in the establishment of 2D-QSAR models.…”

“…It might be that parallel docking with different algorithms would provide more reliable results. Gupta et al [37] proposed a docking scheme with two different programs, Autodock and DOCK. Autodock uses a Lamarckian genetic algorithm (LGA), whereas DOCK uses an incremental construction algorithm (IC).…”

Effect of HIV-1 Subtype C integrase mutations implied using molecular modeling and docking data

“…The conserved DDE motif coordinates two divalent Mg2+ ions essential for the catalysis of integration [ 4 , 5 ]. The critical step during retroviral life cycle is the integration of the viral double stranded DNA into the host chromosome [ 6 ]. The HIV-1 integrase enzyme plays a crucial role by removing a dinucleotide next to the conserved cytosineadenine sequence from each 3’end of the viral DNA.…”