Integrated Cheminformatics-Molecular Docking Approach to Drug Discovery Against Viruses.


Journal

Infectious disorders drug targets
ISSN: 2212-3989
Titre abrégé: Infect Disord Drug Targets
Pays: United Arab Emirates
ID NLM: 101269158

Informations de publication

Date de publication:
2020
Historique:
received: 30 01 2018
revised: 03 08 2018
accepted: 11 10 2018
pubmed: 23 10 2018
medline: 15 12 2020
entrez: 23 10 2018
Statut: ppublish

Résumé

In the current study, we present an integrated in silico cheminformaticsmolecular docking approach to screen and test potential therapeutic compounds against viruses. Fluoroquinolones have been shown to inhibit HCV replication by targeting HCV NS3-helicase. Based on this observation, we hypothesized that natural analogs of fluoroquinolones will have similar or superior inhibitory potential while having potentially fewer adverse effects. To screen for natural analogs of fluoroquinolones, we devised an integrated in silico Cheminformatics-Molecular Docking approach. We used 17 fluoroquinolones as bait reference, to screen large databases of natural analogs. 10399 natural compounds and their derivatives were retrieved from the databases. From these compounds, molecules bearing physicochemical similarities with fluoroquinolones were analyzed using a cheminformatics-docking approach. From the 10399 compounds screened using our cheminformatics approach, only 20 compounds were found to share physicochemical similarities with fluoroquinolones, while the remaining 10379 compounds were physiochemically different from fluoroquinolones. Molecular docking analysis showed 32 amino acids in the HCV NS3 active site that were most frequently targeted by fluoroquinolones and their natural analogues, indicating a functional similarity between the two groups of compounds. This study describes a speedy and inexpensive approach to complement drug discovery and design against viral agents. The in silico analyses we used here can be employed to shortlist promising compounds/putative drugs that can be further tested in wet-lab.

Sections du résumé

BACKGROUND BACKGROUND
In the current study, we present an integrated in silico cheminformaticsmolecular docking approach to screen and test potential therapeutic compounds against viruses. Fluoroquinolones have been shown to inhibit HCV replication by targeting HCV NS3-helicase. Based on this observation, we hypothesized that natural analogs of fluoroquinolones will have similar or superior inhibitory potential while having potentially fewer adverse effects.
METHODS METHODS
To screen for natural analogs of fluoroquinolones, we devised an integrated in silico Cheminformatics-Molecular Docking approach. We used 17 fluoroquinolones as bait reference, to screen large databases of natural analogs. 10399 natural compounds and their derivatives were retrieved from the databases. From these compounds, molecules bearing physicochemical similarities with fluoroquinolones were analyzed using a cheminformatics-docking approach.
RESULTS RESULTS
From the 10399 compounds screened using our cheminformatics approach, only 20 compounds were found to share physicochemical similarities with fluoroquinolones, while the remaining 10379 compounds were physiochemically different from fluoroquinolones. Molecular docking analysis showed 32 amino acids in the HCV NS3 active site that were most frequently targeted by fluoroquinolones and their natural analogues, indicating a functional similarity between the two groups of compounds.
CONCLUSION CONCLUSIONS
This study describes a speedy and inexpensive approach to complement drug discovery and design against viral agents. The in silico analyses we used here can be employed to shortlist promising compounds/putative drugs that can be further tested in wet-lab.

Identifiants

pubmed: 30345931
pii: IDDT-EPUB-93849
doi: 10.2174/1871526518666181019162359
doi:

Substances chimiques

Antiviral Agents 0
Biological Products 0
Fluoroquinolones 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

150-159

Informations de copyright

Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Auteurs

Muhammad Faraz Anwar (MF)

National Center for Proteomics, University of Karachi, Karachi, Pakistan.
Department of Biological and Biomedical Sciences, Aga Khan University, Karachi, Pakistan.

Ramsha Khalid (R)

Department of Biochemistry, University of Karachi, Karachi, Pakistan.

Alina Hasanain (A)

Medical College, Aga Khan University, Karachi, Pakistan.

Sadaf Naeem (S)

Department of Biochemistry, University of Karachi, Karachi, Pakistan.

Shamshad Zarina (S)

National Center for Proteomics, University of Karachi, Karachi, Pakistan.

Syed Hani Abidi (SH)

Department of Biological and Biomedical Sciences, Aga Khan University, Karachi, Pakistan.
Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom.

Syed Ali (S)

Department of Biomedical Sciences, Nazarbayev University School of Medicine, Nazarbayev University, Astana, Kazakhstan.

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Classifications MeSH