6533b838fe1ef96bd12a5348
RESEARCH PRODUCT
Exploring the SARS-CoV-2 Proteome in the Search of Potential Inhibitors via Structure-based Pharmacophore Modeling/Docking Approach
Ugo PerriconeMarco TutoneGiulia CullettaAnna Maria AlmericoMaria ZappalàMaria Rita Gulottasubject
General Computer ScienceComputer scienceComputational biologylcsh:QA75.5-76.95Theoretical Computer Science03 medical and health sciences0302 clinical medicineHomology modelingMM-GBSA030304 developmental biology0303 health sciencesVirtual screeningpharmacophoreSARS-CoV-2Applied MathematicsCOVID-19computational chemistryCOVID-19 SARS-CoV-2 computational chemistry structure-based pharmacophore docking MM-GBSADrug repositioningstructure-basedDrug developmentInfectious disease (medical specialty)Docking (molecular)030220 oncology & carcinogenesisModeling and Simulationdockinglcsh:Electronic computers. Computer sciencePharmacophoreDrugBankdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-09-01 | Computation |