Application of an integrated cheminformatics-molecular docking approach for discovery for physicochemically similar analogs of fluoroquinolones as putative HCV inhibitors

Muhammad Faraz Anwar, Ramsha Khalid, Alina Hasanain, Sadaf Naeem, Shamshad Zarina, Syed Hani Abidi, Syed Ali

Research output: Contribution to journalArticle

Abstract

Background: Hepatitis C Virus (HCV) infection is a major public health concern across the globe. At present, direct-acting antivirals are the treatment of choice. However, the long-term effect of this therapy has yet to be ascertained. Previously, fluoroquinolones have been reported to inhibit HCV replication by targeting NS3 protein. Therefore, it is logical to hypothesize that the natural analogs of fluoroquinolones will exhibit NS3 inhibitory activity with substantially lesser side effects. Method: In this study, we tested the application of a recently devised integrated in-silico Cheminformatics-Molecular Docking approach to identify physicochemically similar natural analogs of fluoroquinolones from the available databases (Ambinter, Analyticon, Indofines, Specs, and TimTec). Molecular docking and ROC curve analyses were performed, using PatchDock and Graphpad software, respectively, to compare and analyze drug-protein interactions between active natural analogs, Fluoroquinolones, and HCV NS3 protein. Result: In our analysis, we were able to shortlist 18 active natural analogs, out of 10,399, that shared physicochemical properties with the template drugs (fluoroquinolones). These analogs showed comparable binding efficacy with fluoroquinolones in targeting 32 amino acids in the HCV NS3 active site that are crucial for NS3 activity. Our approach had around 80 % sensitivity and 70 % specificity in identifying physicochemically similar analogs of fluoroquinolones. Conclusion: Our current data suggest that our approach can be efficiently applied to identify putative HCV drug inhibitors that can be taken for in vitro testing. This approach can be applied to discover physicochemically similar analogs of virtually any drug, thus providing a speedy and inexpensive approach to complement drug discovery and design, which can tremendously economize on time and money spent on the screening of putative drugs.

Original languageEnglish
Article number107167
JournalComputational Biology and Chemistry
Volume84
DOIs
Publication statusPublished - Feb 2020

Fingerprint

Molecular Docking
Fluoroquinolones
Viruses
Hepacivirus
Inhibitor
Virus
Analogue
Drugs
Proteins
Pharmaceutical Preparations
Protein
ROC Curve
Public health
Globe
Drug Design
Preclinical Drug Evaluations
Drug Discovery
Amino acids
Screening
Receiver Operating Characteristic Curve

Keywords

  • Antiviral agents
  • Cheminformatics
  • Drug discovery
  • Molecular docking

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Organic Chemistry
  • Computational Mathematics

Cite this

Application of an integrated cheminformatics-molecular docking approach for discovery for physicochemically similar analogs of fluoroquinolones as putative HCV inhibitors. / Anwar, Muhammad Faraz; Khalid, Ramsha; Hasanain, Alina; Naeem, Sadaf; Zarina, Shamshad; Abidi, Syed Hani; Ali, Syed.

In: Computational Biology and Chemistry, Vol. 84, 107167, 02.2020.

Research output: Contribution to journalArticle

@article{1e27643aad314b1aac323f137ec11184,
title = "Application of an integrated cheminformatics-molecular docking approach for discovery for physicochemically similar analogs of fluoroquinolones as putative HCV inhibitors",
abstract = "Background: Hepatitis C Virus (HCV) infection is a major public health concern across the globe. At present, direct-acting antivirals are the treatment of choice. However, the long-term effect of this therapy has yet to be ascertained. Previously, fluoroquinolones have been reported to inhibit HCV replication by targeting NS3 protein. Therefore, it is logical to hypothesize that the natural analogs of fluoroquinolones will exhibit NS3 inhibitory activity with substantially lesser side effects. Method: In this study, we tested the application of a recently devised integrated in-silico Cheminformatics-Molecular Docking approach to identify physicochemically similar natural analogs of fluoroquinolones from the available databases (Ambinter, Analyticon, Indofines, Specs, and TimTec). Molecular docking and ROC curve analyses were performed, using PatchDock and Graphpad software, respectively, to compare and analyze drug-protein interactions between active natural analogs, Fluoroquinolones, and HCV NS3 protein. Result: In our analysis, we were able to shortlist 18 active natural analogs, out of 10,399, that shared physicochemical properties with the template drugs (fluoroquinolones). These analogs showed comparable binding efficacy with fluoroquinolones in targeting 32 amino acids in the HCV NS3 active site that are crucial for NS3 activity. Our approach had around 80 {\%} sensitivity and 70 {\%} specificity in identifying physicochemically similar analogs of fluoroquinolones. Conclusion: Our current data suggest that our approach can be efficiently applied to identify putative HCV drug inhibitors that can be taken for in vitro testing. This approach can be applied to discover physicochemically similar analogs of virtually any drug, thus providing a speedy and inexpensive approach to complement drug discovery and design, which can tremendously economize on time and money spent on the screening of putative drugs.",
keywords = "Antiviral agents, Cheminformatics, Drug discovery, Molecular docking",
author = "Anwar, {Muhammad Faraz} and Ramsha Khalid and Alina Hasanain and Sadaf Naeem and Shamshad Zarina and Abidi, {Syed Hani} and Syed Ali",
year = "2020",
month = "2",
doi = "10.1016/j.compbiolchem.2019.107167",
language = "English",
volume = "84",
journal = "Computational Biology and Chemistry",
issn = "1476-9271",
publisher = "Elsevier",

}

TY - JOUR

T1 - Application of an integrated cheminformatics-molecular docking approach for discovery for physicochemically similar analogs of fluoroquinolones as putative HCV inhibitors

AU - Anwar, Muhammad Faraz

AU - Khalid, Ramsha

AU - Hasanain, Alina

AU - Naeem, Sadaf

AU - Zarina, Shamshad

AU - Abidi, Syed Hani

AU - Ali, Syed

PY - 2020/2

Y1 - 2020/2

N2 - Background: Hepatitis C Virus (HCV) infection is a major public health concern across the globe. At present, direct-acting antivirals are the treatment of choice. However, the long-term effect of this therapy has yet to be ascertained. Previously, fluoroquinolones have been reported to inhibit HCV replication by targeting NS3 protein. Therefore, it is logical to hypothesize that the natural analogs of fluoroquinolones will exhibit NS3 inhibitory activity with substantially lesser side effects. Method: In this study, we tested the application of a recently devised integrated in-silico Cheminformatics-Molecular Docking approach to identify physicochemically similar natural analogs of fluoroquinolones from the available databases (Ambinter, Analyticon, Indofines, Specs, and TimTec). Molecular docking and ROC curve analyses were performed, using PatchDock and Graphpad software, respectively, to compare and analyze drug-protein interactions between active natural analogs, Fluoroquinolones, and HCV NS3 protein. Result: In our analysis, we were able to shortlist 18 active natural analogs, out of 10,399, that shared physicochemical properties with the template drugs (fluoroquinolones). These analogs showed comparable binding efficacy with fluoroquinolones in targeting 32 amino acids in the HCV NS3 active site that are crucial for NS3 activity. Our approach had around 80 % sensitivity and 70 % specificity in identifying physicochemically similar analogs of fluoroquinolones. Conclusion: Our current data suggest that our approach can be efficiently applied to identify putative HCV drug inhibitors that can be taken for in vitro testing. This approach can be applied to discover physicochemically similar analogs of virtually any drug, thus providing a speedy and inexpensive approach to complement drug discovery and design, which can tremendously economize on time and money spent on the screening of putative drugs.

AB - Background: Hepatitis C Virus (HCV) infection is a major public health concern across the globe. At present, direct-acting antivirals are the treatment of choice. However, the long-term effect of this therapy has yet to be ascertained. Previously, fluoroquinolones have been reported to inhibit HCV replication by targeting NS3 protein. Therefore, it is logical to hypothesize that the natural analogs of fluoroquinolones will exhibit NS3 inhibitory activity with substantially lesser side effects. Method: In this study, we tested the application of a recently devised integrated in-silico Cheminformatics-Molecular Docking approach to identify physicochemically similar natural analogs of fluoroquinolones from the available databases (Ambinter, Analyticon, Indofines, Specs, and TimTec). Molecular docking and ROC curve analyses were performed, using PatchDock and Graphpad software, respectively, to compare and analyze drug-protein interactions between active natural analogs, Fluoroquinolones, and HCV NS3 protein. Result: In our analysis, we were able to shortlist 18 active natural analogs, out of 10,399, that shared physicochemical properties with the template drugs (fluoroquinolones). These analogs showed comparable binding efficacy with fluoroquinolones in targeting 32 amino acids in the HCV NS3 active site that are crucial for NS3 activity. Our approach had around 80 % sensitivity and 70 % specificity in identifying physicochemically similar analogs of fluoroquinolones. Conclusion: Our current data suggest that our approach can be efficiently applied to identify putative HCV drug inhibitors that can be taken for in vitro testing. This approach can be applied to discover physicochemically similar analogs of virtually any drug, thus providing a speedy and inexpensive approach to complement drug discovery and design, which can tremendously economize on time and money spent on the screening of putative drugs.

KW - Antiviral agents

KW - Cheminformatics

KW - Drug discovery

KW - Molecular docking

UR - http://www.scopus.com/inward/record.url?scp=85076353500&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85076353500&partnerID=8YFLogxK

U2 - 10.1016/j.compbiolchem.2019.107167

DO - 10.1016/j.compbiolchem.2019.107167

M3 - Article

AN - SCOPUS:85076353500

VL - 84

JO - Computational Biology and Chemistry

JF - Computational Biology and Chemistry

SN - 1476-9271

M1 - 107167

ER -