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

The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing 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.We gratefully acknowledge funding that supported this research support from the Ryerson University Faculty of Science (CNA), as well as funding support in the form of a CIFAR Catalyst Grant (JPJ and CNA), an NSERC Alliance Grant (CNA) and the Ryerson COVID-19 SRC Response Fund award (CNA). BW is partly supported by CIFAR AI Chairs Program. This work was also supported by a Mitacs award (BW), the European Union’s Horizon 2020 research and innovation program under a Marie Sklodowska-Curie grant (ER), by the CIFAR Azrieli Global Scholar program (JPJ), by the Ontario Early Researcher Awards program (JPJ and CNA), and by the Canada Research Chairs program (JPJ). We also thank Dr. James Rini (University of Toronto) for the kind gift of the 9.8E12 antibody used to detect the 229E Spike protein, and Dr. Scott Gray-Owen (University of Toronto) for the kind gift of the NL63 human coronavirus.Peer reviewe

Efficacy as an Intrinsic Property of the M₂ Muscarinic Receptor in Its Tetrameric State

Muscarinic and other G protein-coupled receptors exhibit an agonist-specific heterogeneity that tracks efficacy and commonly is attributed to an effect of the G protein on an otherwise homogeneous population of sites. To examine this notion, M₂ muscarinic receptors were purified from <i>Sf</i>9 cells as monomers devoid of G protein and reconstituted as tetramers in phospholipid vesicles. In assays with <i>N</i>-[³H]­methylscopolamine, seven agonists revealed a dispersion of affinities indicative of two or more classes of sites. Unlabeled <i>N</i>-methylscopolamine and the antagonist quinuclidinylbenzilate recognized one class of sites; atropine recognized two classes with a preference that was the opposite of that of agonists, as indicated by the effects of <i>N</i>-ethylmaleimide. The data were inconsistent with an explicit model of constitutive asymmetry within a tetramer, and the fit improved markedly upon the introduction of cooperative interactions (<i>P</i> < 0.00001). Purified monomers appeared to be homogeneous or nearly so to all ligands except the partial agonists pilocarpine and McN-A-343, where heterogeneity emerged from intramolecular cooperativity between the orthosteric site and an allosteric site. The breadth of each dispersion was quantified empirically as the area between the fitted curve for two classes of sites and the theoretical curve for a single class of lower affinity, which approximates the expected effect of GTP if a G protein were present. The areas measured for 10 ligands at reconstituted tetramers correlated with similar measures of heterogeneity and with intrinsic activities reported previously for binding and response in natural membranes (<i>P</i> ≤ 0.00002). The data suggest that the GTP-sensitive heterogeneity typically revealed by agonists at M₂ receptors is intrinsic to the receptor in its tetrameric state. It exists independently of the G protein, and it appears to arise at least in part from cooperativity between linked orthosteric sites

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

The 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.We gratefully acknowledge funding that supported this research support from the Ryerson University Faculty of Science (CNA), as well as funding support in the form of a CIFAR Catalyst Grant (JPJ and CNA), an NSERC Alliance Grant (CNA) and the Ryerson COVID-19 SRC Response Fund award (CNA). BW is partly supported by CIFAR AI Chairs Program. This work was also supported by a Mitacs award (BW), the European Union’s Horizon 2020 research and innovation program under a Marie Sklodowska-Curie grant (ER), by the CIFAR Azrieli Global Scholar program (JPJ), by the Ontario Early Researcher Awards program (JPJ and CNA), and by the Canada Research Chairs program (JPJ).N

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

  The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing 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.  </p