Exploring the SARS-CoV-2 proteome in the search of potential inhibitors via structure-based pharmacophore modeling/docking approach

Maria Rita Gulotta, Anna Maria Almerico, Marco Tutone, Ugo Perricone, Maria Zappalà, Ugo Perricone, Maria Rita Gulotta, Giulia Culletta, Giulia Culletta

Risultato della ricerca: Articlepeer review

3 Citazioni (Scopus)

Abstract

To date, SARS-CoV-2 infectious disease, named COVID-19 by the World Health Organization (WHO) in February 2020, has caused millions of infections and hundreds of thousands of deaths. Despite the scientific community efforts, there are currently no approved therapies for treating this coronavirus infection. The process of new drug development is expensive and time-consuming, so that drug repurposing may be the ideal solution to fight the pandemic. In this paper, we selected the proteins encoded by SARS-CoV-2 and using homology modeling we identified the high-quality model of proteins. A structure-based pharmacophore modeling study was performed to identify the pharmacophore features for each target. The pharmacophore models were then used to perform a virtual screening against the DrugBank library (investigational, approved and experimental drugs). Potential inhibitors were identified for each target using XP docking and induced fit docking. MM-GBSA was also performed to better prioritize potential inhibitors. This study will provide new important comprehension of the crucial binding hot spots usable for further studies on COVID-19. Our results can be used to guide supervised virtual screening of large commercially available libraries.
Lingua originaleEnglish
pagine (da-a)77-
Numero di pagine16
RivistaComputation
Volume8
Stato di pubblicazionePublished - 2020

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • ???subjectarea.asjc.1700.1700???
  • Modelling and Simulation
  • Applied Mathematics

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