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Identification of Zika Virus Inhibitors Using Homology Modeling and Similarity-Based Screening to Target Glycoprotein E. | LitMetric

AI Article Synopsis

  • * A computational screen was conducted on over 4 million commercial compounds using a model of ZIKV E, based on existing dengue virus structures, to find candidates with similar interaction patterns to the reference compound β-octyl glucoside (BOG).
  • * Six promising compounds were identified that showed effectiveness in preventing cell damage and viral infection in lab tests, indicating their potential as anti-ZIKV agents and laying the groundwork for future research.

Article Abstract

The World Health Organization has designated Zika virus (ZIKV) as a dangerous, mosquito-borne pathogen that can cause severe developmental defects. The primary goal of this work was identification of small molecules as potential ZIKV inhibitors that target the viral envelope glycoprotein (ZIKV E) involved in membrane fusion and viral entry. A homology model of ZIKV E containing the small molecule β-octyl glucoside (BOG) was constructed, on the basis of an analogous X-ray structure from dengue virus, and >4 million commercially available compounds were computationally screened using the program DOCK6. A key feature of the screen involved the use of similarity-based scoring to identify inhibitor candidates that make similar interaction energy patterns (molecular footprints) as the BOG reference. Fifty-three prioritized compounds underwent experimental testing using cytotoxicity, cell viability, and tissue culture infectious dose 50% (TCID50) assays. Encouragingly, relative to a known control (NITD008), six compounds were active in both the cell viability assay and the TCID50 infectivity assay, and they showed activity in a third caspase activity assay. In particular, compounds and (tested at 25 μM) and compound (tested at 10 μM) appeared to provide significant protection to infected cells, indicative of anti-ZIKV activity. Overall, the study highlights how similarity-based scoring can be leveraged to computationally identify potential ZIKV E inhibitors that mimic a known reference (in this case BOG), and the experimentally verified hits provide a strong starting point for further refinement and optimization efforts.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598728PMC
http://dx.doi.org/10.1021/acs.biochem.0c00458DOI Listing

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