Molecular Features Related to HIV Integrase Inhibition Obtained from Structure- and Ligand-Based Approaches

Abstract

Among several biological targets to treat AIDS, HIV integrase is a promising enzyme that can be employed to develop new anti-HIV agents. The aim of this work is to propose a mechanistic interpretation of HIV-1 integrase inhibition and to rationalize the molecular features related to the binding affinity of studied ligands. A set of 79 HIV-1 integrase inhibitors and its relationship with biological activity are investigated employing 2D and 3D QSAR models, docking analysis and DFT studies. Analyses of docking poses and frontier molecular orbitals revealed important features on the main ligand-receptor interactions. 2D and 3D models presenting good internal consistency, predictive power and stability were obtained in all cases. Significant correlation coefficients (r2 = 0.908 and q2 = 0.643 for 2D model; r2 = 0.904 and q2 = 0.719 for 3D model) were obtained, indicating the potential of these models for untested compounds. The generated holograms and contribution maps revealed important molecular requirements to HIV-1 IN inhibition and several evidences for molecular modifications. The final models along with information resulting from molecular orbitals, 2D contribution and 3D contour maps should be useful in the design of new inhibitors with increased potency and selectivity within the chemical diversity of the data.

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.…”

“…The data set was divided into two classes, either 'active' (pIC 50 > 4) or 'inactive' (pIC 50 < 4), and then partitioned into training and test sets. To take both structural diversity and pIC 50 distribution into consideration, Magalhas Ude et al [33] and de Carvalho et al [34] made a manual selection to ensure that both factors were well represented in both training and test sets.…”

“…PLS is also used in the establishment of 2D-QSAR models. De Carvalho et al [34] utilized PLS to generate their 2D models, which showed significant correlation coefficients (q 2 = 0.643, r 2 = 0.908).…”

“…Clustering of docking pose is a post-processing step to identify compounds with diverse chemotypes for further synthesis [60,61]. Importantly, docking can also generate relatively reliable structures for other computational approaches [34,55,63,67].…”

Convergent QSAR studies on a series of NK3receptor antagonists for schizophrenia treatment

“…All validation coefficients were evaluated according to acceptable reference values 9,11,32 . Our strategy was based on many works which combine different QSAR methods in the drug design [33][34][35][36][37][38] .…”