Exploring the Lead Compounds for Zika Virus NS2B-NS3 Protein: an e-Pharmacophore-Based Approach

Abstract

The rapid spread of the Zika virus and its association with the abnormal brain development constitute a global health emergency. With a continuing spread of the mosquito vector, the exposure is expected to accelerate in the coming years. Despite number of efforts, there is still no proper vaccine or medicine to combat this virus. Of note, the NS2B-NS3 protein is proven to be the potential target for the Zika virus therapeutics. Hence, e-pharmacophore-based drug design strategy was employed to identify potent inhibitors of NS2B-NS3 protein from ASINEX database consisting of 467,802 molecules. A 3D e-pharmacophore model was generated using PHASE module of Schrödinger Suite. The generated model consists of one hydrogen bond acceptor (A), two hydrogen bond donors (D), and two aromatic rings (R), ADDRR. The model was further evaluated for its ability to screen actives using enrichment analysis. Subsequently, high-throughput virtual screening protocol was employed, and the resultant hit molecules were also examined for its binding free energies and ADME properties using Prime MM-GBSA and Qikprop module of Schrodinger packages, respectively. Finally, the screened hit molecule was subjected to molecular dynamics simulation to examine its stability. Overall, the results from our analysis suggest that compound BAS 19192837 could be a potent inhibitor for the NS2B-NS3 protein of the Zika virus. It is also noteworthy to mention that our results are in good agreement with literature evidences. We hope that this result is of immense importance in designing potential drug molecules to combat the spread of Zika virus in the near future.

Research Models and Tools for the Identification of Antivirals and Therapeutics against Zika Virus Infection

“…As ZIKV proteins have been crystalized and ZIKV virions have been studied by electron microscopy [ 163 , 164 , 165 ], this method could be used to identify potential hit compounds that have good binding affinities for a real matured ZIKV protein structure obtained with cryo-electron microscopy [ 166 ]. A complementary approach to that of molecular docking analysis is to use pharmacophore searching algorithms to find ligands similar to one of known activity and chemical features (such as hydrogen bonds, charges, lipophilic areas) [ 167 , 168 ], which has recently been utilized to identify potent inhibitors of ZIKV NS2B-NS3 [ 169 ]. Next to structure-based drug discovery approaches, mathematical analysis of viral replication dynamics and antiviral treatment strategies have been performed for several viruses, including human immunodeficiency virus, hepatitis C virus, influenza A virus, Ebola virus, DENV and ZIKV [ 170 ].…”

In silico discovery and biological validation of ligands of FAD synthase, a promising new antimicrobial target

“…Therefore, these protocols usually implement a funnel-like strategy, which start from fast but less accurate methods (where a large number of molecules are filtered) and more accurate and time-consuming tools are used in the last steps [13][14][15]. Usually, pharmacophore-based tools are implemented in the first stages of VS, given their ability to quickly screen large compounds libraries [16][17][18]. While more sophisticated tools such as docking or molecular dynamics (MD) are implemented in the latter steps to predict ligand affinities [11,13,[19][20][21][22][23][24][25][26].…”

“…Special attention deserve the tools used in the first steps of the VS, because these impact the ability of the protocol to explore large compound libraries and the chemical space of the compounds, such as the pharmacophore-based strategies [16][17][18][27][28][29][30]. Despite the usefulness of these methods, which accelerate the first steps of the VS, these strategies have some limitations.…”

In silicodiscovery and biological validation of ligands of FAD synthase, a promising new antimicrobial target

“…Therefore, these protocols usually implement a funnel-like 15 strategy, which start from fast but less accurate methods (where a large number of 16 molecules are filtered) and more accurate and time-consuming tools are used in the last 17 steps [13][14][15]. Usually, pharmacophore-based tools are implemented in the first stages of 18 VS, given their ability to quickly screen large compounds libraries [16][17][18]. While more 19 sophisticated tools such as docking or molecular dynamics (MD) are implemented in the 20 latter steps to predict ligand affinities [11,13,[19][20][21][22][23][24][25][26].…”

“…Special attention deserve the tools used in the first steps of the VS, because these 22 impact the ability of the protocol to explore large compound libraries and the chemical 23 space of the compounds, such as the pharmacophore-based strategies [16][17][18][27][28][29][30].…”