Montelukast is a dual-purpose inhibitor of SARS-CoV-2 infection and virus-induced IL-6 expression identified by structure-based drug repurposing

Max Luedemann, Daniela Stadler, Cho Chin Cheng, Ulrike Protzer, Percy A. Knolle, Sainitin Donakonda

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Drug-repurposing has been instrumental to identify drugs preventing SARS-CoV-2 replication or attenuating the disease course of COVID-19. Here, we identify through structure-based drug-repurposing a dual-purpose inhibitor of SARS-CoV-2 infection and of IL-6 production by immune cells. We created a computational structure model of the receptor binding domain (RBD) of the SARS-CoV-2 spike 1 protein, and used this model for insilico screening against a library of 6171 molecularly defined binding-sites from drug molecules. Molecular dynamics simulation of candidate molecules with high RBD binding-scores in docking analysis predicted montelukast, an antagonist of the cysteinyl-leukotriene-receptor, to disturb the RBD structure, and infection experiments demonstrated inhibition of SARS-CoV-2 infection, although montelukast binding was outside the ACE2-binding site. Molecular dynamics simulation of SARS-CoV-2 variant RBDs correctly predicted interference of montelukast with infection by the beta but not the more infectious alpha variant. With distinct binding sites for RBD and the leukotriene receptor, montelukast also prevented SARS-CoV-2-induced IL-6 release from immune cells. The inhibition of SARS-CoV-2 infection through a molecule binding distal to the ACE-binding site of the RBD points towards an allosteric mechanism that is not conserved in the more infectious alpha and delta SARS-CoV-2 variants.

Original languageEnglish
Pages (from-to)799-811
Number of pages13
JournalComputational and Structural Biotechnology Journal
Volume20
DOIs
StatePublished - Jan 2022
Externally publishedYes

Keywords

  • Binding site similarity
  • Docking
  • Molecular dynamics simulations
  • Neutralization
  • SARS-CoV-2
  • Structural modeling

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