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Predicting antiviral resistance mutations in SARS-CoV-2 main protease with computational and experimental screening
Vishnu M Sasi; Sven Ullrich; Jennifer Ton; Sarah E Fry; Jason Johansen-Leete; Richard J Payne; Christoph Nitsche; Colin J Jackson.
Afiliação
  • Vishnu M Sasi; Australian National University
  • Sven Ullrich; Australian National University
  • Jennifer Ton; Australian National University
  • Sarah E Fry; The University of Sydney
  • Jason Johansen-Leete; The University of Sydney
  • Richard J Payne; The University of Sydney
  • Christoph Nitsche; Australian National University
  • Colin J Jackson; Australian National University
Preprint em Inglês | bioRxiv | ID: ppbiorxiv-505060
ABSTRACT
The main protease (Mpro) of SARS-CoV-2 is essential for viral replication and has been the focus of many drug discovery efforts since the start of the COVID-19 pandemic. Nirmatrelvir (NTV) is an inhibitor of SARS-CoV-2 Mpro that is used in the combination drug Paxlovid for the treatment of mild to moderate COVID-19. However, with increased use of NTV across the globe, there is a possibility that future SARS-CoV-2 lineages will evolve resistance to NTV. Early prediction and monitoring of resistance mutations could allow for measures to slow the spread of resistance and for the development of new compounds with activity against resistant strains. In this work, we have used in silico mutational scanning and inhibitor docking of Mpro to identify potential resistance mutations. Subsequent in vitro experiments revealed five mutations (N142L, E166M, Q189E, Q189I, and Q192T) that reduce the potency of NTV and of a previously identified non-covalent cyclic peptide inhibitor of Mpro. The E166M mutation reduced the half-maximal inhibitory concentration (IC50) of NTV 24-fold, and 118-fold for the non-covalent peptide inhibitor. Our findings inform the ongoing genomic surveillance of emerging SARS-CoV-2 lineages. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC="FIGDIR/small/505060v1_ufig1.gif" ALT="Figure 1"> View larger version (32K) org.highwire.dtl.DTLVardef@14f0713org.highwire.dtl.DTLVardef@15995feorg.highwire.dtl.DTLVardef@8689a7org.highwire.dtl.DTLVardef@b73a64_HPS_FORMAT_FIGEXP M_FIG C_FIG
Licença
cc_by_nc_nd
Texto completo: Disponível Coleções: Preprints Base de dados: bioRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2022 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: bioRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2022 Tipo de documento: Preprint
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