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Título

Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease

AutorSugiyama, Michel G.; Cui, Haotian; Redka, Dar’ya S.; Karimzadeh, Mehran; Rujas, Edurne CSIC ORCID; Maan, Hassaan; Hayat, Sikander; Cheung, Kyle; Misra, Rahul; McPhee, Joseph B.; Viirre, Russell D.; Haller, Andrew; Botelho, Roberto J.; Karshafian, Raffi; Sabatinos, Sarah A.; Fairn, Gregory D.; Madani Tonekaboni, Seyed Ali; Windemuth, Andreas; Julien, Jean-Philippe; Shahani, Vijay; MacKinnon, Stephen S.; Wang, Bo; Antonescu, Costin N.
Fecha de publicación13-abr-2021
EditorBioRxiv
CitaciónBioRxiv: 10.1101/2021.04.13.439274 (2021)
ResumenThe COVID-19 pandemic has led to an urgent need for the identification of new antiviral drug therapies that can be rapidly deployed to treat patients with this disease. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of COVID-19. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cellbased experimental assessment reveals several clinically-relevant repurposing drug candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.
Versión del editorhttps://doi.org/10.1101/2021.04.13.439274
URIhttp://hdl.handle.net/10261/239325
DOI10.1101/2021.04.13.439274
Aparece en las colecciones: (PTI Salud Global) Colección Especial COVID-19
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