Identification of Binding Mode and Prospective Structural Features of Novel Nef Protein Inhibitors as Potential Anti-HIV Drugs

Abstract

Human immunodeficiency virus (HIV)-negative factor (Nef) protein is an accessory pathogenic factor, which plays a significant role in acquired immune deficiency syndrome (AIDS). Nef deficient HIV virus took a longer time to progress into AIDS. Therefore, targeting Nef protein is considered as a key strategy towards HIV/AIDS treatment. Up-to-date, only few compounds were reported as Nef inhibitors. This has prompted us to provide a first account of an integrated computational framework in order to identify more potential Nef inhibitors. Herein, using a hybrid ligand (shape similarity and pharmacophore) and structure-(molecular docking) based virtual screening approaches combined with molecular dynamics as well as post dynamics analysis, potential new hits were identified as HIV-Nef inhibitors. The top ranked compounds of molecular docking from the shape similarity-based library (ZINC04177596, ∆ G -28.7482 kcal/mol) and pharmacophore-based library (ZINC36617540, ∆ G -20.2271 kcal/mol) possess comparatively better binding affinities than the reference molecule, B9 (∆ G = -18.0694 kcal/mol). Both these hits (ZINC04177596 and ZINC36617540) showed similar binding mode at the binding site as like the prototype, B9. Hydrophobic and electrostatic interactions seemed to be the prominent binding forces that hold these ligands at the dimer interface of Nef protein. Finally, a set of chemical structural features that can be used as a guide in the design of novel potential Nef inhibitors is also highlighted herein. We believe that the information gained from this study would be of great importance in the discovery and design of potential small molecules targeting HIV-Nef.

Exploration of potential inhibitors for tuberculosis via structure‐based drug design, molecular docking, and molecular dynamics simulation studies

“…Computer‐aided drug design produces numerous experimental advantages: (i) The administered doses able to optimized for simple and slight 24 ; (ii) reducing of drug interactions and better safety and efficacy 25 ; (iii) successful prevention of resistance in target proteins 26 ; (iv) using structure‐based drug design through computational studies have inferior expenditure of money and time 27,28 . These CADD strategies have shown great promise in identifying bioactive molecules for anticancer and antiviral activities 29,30 . Recently, we have identified the chemical constituents of Tinospora Cordifolia as possible inhibitors of COVID‐19 targets via computational approach 31 .…”

“…27,28 These CADD strategies have shown great promise in identifying bioactive molecules for anticancer and antiviral activities. 29,30 Recently, we have identified the chemical constituents of Tinospora Cordifolia as possible inhibitors of COVID-19 targets via computational approach. 31 As part of our ongoing programme for the identification bioactive compounds via computational approach, 32 we therefore, in the present investigation employed CADD for the investigation of a chemical library for potential inhibitors for TB.…”

Discovery of potent Covid‐19 main protease inhibitors using integrated drug‐repurposing strategy

“…[15][16][17] These virtual screening strategies have shown great promise in identifying bioactive molecules from large libraries. [18][19][20] In addition to these approaches, Prime MM/GBSA (molecular mechanics/generalized born surface area) analysis, AutoQSAR techniques, and molecular dynamics (MD) simulation were performed to contemplate more efficacious drugs. We believed that hits resulted from our integrated approach provide a clue to the control of the emerging SARS-CoV-2 pandemic.…”

“…Avarol, which is one such notable example, was originally known as an antibacterial compound, which was then identified as a potential drug molecule for Alzheimer's disease and HIV through drug‐repurposing approaches 15–17 . These virtual screening strategies have shown great promise in identifying bioactive molecules from large libraries 18–20 . In addition to these approaches, Prime MM/GBSA (molecular mechanics/generalized born surface area) analysis, AutoQSAR techniques, and molecular dynamics (MD) simulation were performed to contemplate more efficacious drugs.…”

Hyphenated 3D-QSAR statistical model-drug repurposing analysis for the identification of potent neuraminidase inhibitor

“…An amalgamation of both conventional drug discovery process and computational strategies would lead to remarkable development in drug discovery [15]. Virtual screening is known as the most dynamic and advantageous technology in revelation of novel drug-like compounds [16][17][18][19]. Most importantly, pharmacophore-based virtual screening and 3D-QSAR modeling has shown great promise in sorting large libraries of bioactive molecules and analyzing the biological activity of ligands.…”