ABSTRACT
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. Cell-based 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.
Subject(s)
Coronavirus Infections , COVID-19ABSTRACT
Coronavirus disease 2019 (COVID-19) is a global pandemic caused by a novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). SARS-CoV-2 infection of host cells occurs predominantly via binding of the viral surface spike protein to the human angiotensin-converting enzyme 2 (ACE2) receptor. Hypertension and pre-existing cardiovascular disease are risk factors for morbidity from COVID-19, and it remains uncertain whether the use of angiotensin converting enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARB) impacts infection and disease. Here, we aim to shed light on this question by assessing ACE2 expression in normal and diseased human myocardial samples profiled by bulk and single nucleus RNA-seq.