Abstract
COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has quickly become a global health crisis since the first report of infection in December of 2019. However, the infection spectrum of SARS-CoV-2 and its comprehensive protein-level interactions with hosts remain unclear. There is a massive amount of underutilized data and knowledge about RNA viruses highly relevant to SARS-CoV-2 and proteins of their hosts. More in-depth and more comprehensive analyses of that knowledge and data can shed new light on the molecular mechanisms underlying the COVID-19 pandemic and reveal potential risks. In this work, we constructed a multi-layer virus-host interaction network to incorporate these data and knowledge. We developed a machine-learning-based method to predict virus-host interactions at both protein and organism levels. Our approach revealed five potential infection targets of SARS-CoV-2 and 19 highly possible interactions between SARS-CoV-2 proteins and human proteins in the innate immune pathway.
•We built a virus-host interaction network with 7 human coronaviruses and 17 hosts•We developed an ML-based method to predict protein- and organism-level interactions•We revealed five potential infection targets of SARS-CoV-2•We predicted 19 highly possible interactions between SARS-CoV-2 and human proteins
SARS-CoV-2, a novel single-stranded RNA coronavirus causing COVID-19, is mounting an unprecedented threat against our society and the world. Although tremendous efforts have been devoted into SARS-CoV-2 research, most of them either focused on a few proteins or only provided high-level overviews. Deeper and more comprehensive analyses are needed to shed new light onto the molecular mechanisms underlying the COVID-19 pandemic. Moreover, there is a massive amount of data and knowledge about highly relevant RNA viruses which have yet to be fully utilized.
In this work, we constructed a multi-layer virus-host interaction network to incorporate these data and knowledge. We developed a machine-learning-based method to predict virus-host interactions at both protein and organism levels. Our approach revealed five potential infection targets of SARS-CoV-2 and 19 highly possible interactions between SARS-CoV-2 proteins and human proteins in the innate immune pathway.
Given a new virus, our method can utilize existing knowledge and data about other highly relevant viruses to predict multi-scale interactions between the new virus and potential hosts.
