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Article: Prediction and characterization of influenza virus polymerase inhibitors through blind docking and ligand based virtual screening

TitlePrediction and characterization of influenza virus polymerase inhibitors through blind docking and ligand based virtual screening
Authors
KeywordsBlind docking
Chemical similarity
Influenza
Ligand-based virtual screening
Molecular dynamics simulations
Pharmacophore models
Virus polymerase
Issue Date2021
Citation
Journal of Molecular Liquids, 2021, v. 321, article no. 114784 How to Cite?
AbstractInfluenza virus causes influenza, an infectious disease. The symptoms can range from mild to severe. Severe influenza infections have historically led to 250,000 to 500,000 deaths per year. Influenza viruses can be broadly classified into four types as A, B, C, and D. They are structurally similar, and place high demands on drugs. The heterotrimeric polymerase complex of the influenza virus represents a promising target for the design and improvement of novel lead compounds. Inhibition of its assembly could be proven to have an antiviral efficacy for both in vitro and in vivo activity. However, in vivo, potent lead compounds tackling this target have not been identified yet. In this study, we have used the information about two previously discovered molecules (referred to as ANA 1 and PAC-3), which revealed significant in vitro effects, and we have proposed new compounds using a computational protocol. Structural, chemical similarity calculations against the ZINC database were carried out using the Generic Match Algorithm (GMA). Putative inhibitor binding sites of the influenza virus polymerase were investigated based on the blind docking approach employing AutoDock Vina and Lead Finder. Furthermore, ligand-based virtual screenings were executed, calculating shape similarity using a Weighted Gaussian Algorithm (WEGA) and pharmacophoric properties by LigandScout. A handful of molecules were finally selected as promising inhibitors. The binding efficiency and the toxicity properties of the identified lead compound DB07972 (MolPort-002-025-261) were compared against the available drug of choice Baloxavir marboxil. The molecular dynamics simulations along with the MMPBSA calculations have shown that the novel lead compound DB07972 is capable of exhibiting better interaction and compactness. More interestingly, the novel lead compound was found to be less toxic than the control drug. The molecule was further evaluated using in vitro approaches. From the experimental analysis, the chosen compound, DB07972, showed maximal inhibition around 75% with 20 μM and was non-toxic to 100% healthy cells at the same concentration.
Persistent Identifierhttp://hdl.handle.net/10722/315341
ISSN
2023 Impact Factor: 5.3
2023 SCImago Journal Rankings: 0.918
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorPérez-Sánchez, Horacio-
dc.contributor.authorThirumal Kumar, D.-
dc.contributor.authorGeorge Priya Doss, C.-
dc.contributor.authorRodríguez-Schmidt, Ricardo-
dc.contributor.authorCerón-Carrasco, José Pedro-
dc.contributor.authorPeña-García, Jorge-
dc.contributor.authorYe, Zi Wei-
dc.contributor.authorYuan, Shuofeng-
dc.contributor.authorGünther, Stefan-
dc.date.accessioned2022-08-05T10:18:32Z-
dc.date.available2022-08-05T10:18:32Z-
dc.date.issued2021-
dc.identifier.citationJournal of Molecular Liquids, 2021, v. 321, article no. 114784-
dc.identifier.issn0167-7322-
dc.identifier.urihttp://hdl.handle.net/10722/315341-
dc.description.abstractInfluenza virus causes influenza, an infectious disease. The symptoms can range from mild to severe. Severe influenza infections have historically led to 250,000 to 500,000 deaths per year. Influenza viruses can be broadly classified into four types as A, B, C, and D. They are structurally similar, and place high demands on drugs. The heterotrimeric polymerase complex of the influenza virus represents a promising target for the design and improvement of novel lead compounds. Inhibition of its assembly could be proven to have an antiviral efficacy for both in vitro and in vivo activity. However, in vivo, potent lead compounds tackling this target have not been identified yet. In this study, we have used the information about two previously discovered molecules (referred to as ANA 1 and PAC-3), which revealed significant in vitro effects, and we have proposed new compounds using a computational protocol. Structural, chemical similarity calculations against the ZINC database were carried out using the Generic Match Algorithm (GMA). Putative inhibitor binding sites of the influenza virus polymerase were investigated based on the blind docking approach employing AutoDock Vina and Lead Finder. Furthermore, ligand-based virtual screenings were executed, calculating shape similarity using a Weighted Gaussian Algorithm (WEGA) and pharmacophoric properties by LigandScout. A handful of molecules were finally selected as promising inhibitors. The binding efficiency and the toxicity properties of the identified lead compound DB07972 (MolPort-002-025-261) were compared against the available drug of choice Baloxavir marboxil. The molecular dynamics simulations along with the MMPBSA calculations have shown that the novel lead compound DB07972 is capable of exhibiting better interaction and compactness. More interestingly, the novel lead compound was found to be less toxic than the control drug. The molecule was further evaluated using in vitro approaches. From the experimental analysis, the chosen compound, DB07972, showed maximal inhibition around 75% with 20 μM and was non-toxic to 100% healthy cells at the same concentration.-
dc.languageeng-
dc.relation.ispartofJournal of Molecular Liquids-
dc.subjectBlind docking-
dc.subjectChemical similarity-
dc.subjectInfluenza-
dc.subjectLigand-based virtual screening-
dc.subjectMolecular dynamics simulations-
dc.subjectPharmacophore models-
dc.subjectVirus polymerase-
dc.titlePrediction and characterization of influenza virus polymerase inhibitors through blind docking and ligand based virtual screening-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.molliq.2020.114784-
dc.identifier.scopuseid_2-s2.0-85097528403-
dc.identifier.volume321-
dc.identifier.spagearticle no. 114784-
dc.identifier.epagearticle no. 114784-
dc.identifier.isiWOS:000603403200067-

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