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Researchers create a tool that models binding interactions of SARS-CoV-2 and the human body

Viral diseases often persist within the population due to the unique ability to evolve and mutate the proteins on its surface. This is the case with Covid-19, the ongoing pandemic that seems to have a new variant every couple of months.


Coronavirus isn’t a new virus, having been discovered to infect humans in 1965, however, these infections resulted in mild respiratory illness and were either not easily spread or not very severe. But SARS-CoV-2 is different; originating as a bat disease, it mutated in just the right way to be both highly transmissible and highly infectious, becoming the behemoth we all know today.


The key to staying one step ahead of the virus is being able to predict the possible changes to its surface proteins and to create effective drugs that block the interaction between virus-human proteins.


The labs of Cornell University researchers Dr. Gary Whittaker and Dr. Haiyuan Yu, have developed a prediction tool of SARS-CoV-2 that allows the user to test binding interactions between the virus and the human body.


The Nature paper published from these labs on Nov. 29th, 2021, details their high-resolution tool, showing the viral and human protein interface, or the sections of the proteins that come into contact with each other, in unprecedented detail.


Coined the term interactome, a network of possible protein-protein interactions between a pathogen and the human body, the researchers have provided the roadmap for other scientists and drug developers to explore COVID-19’s mechanisms of infection and pathology.


Additionally, the tool allows researchers to predict how genetic variations in human proteins affect viral-protein interactions, resulting in the diverging responses to COVID-19 seen in people of similar health and age, with some being asymptomatic and others showing severe reactions.


This tool provides a comprehensive resource to aid in the ongoing research on SARS-CoV-2, providing concrete, structure-based evidence to supplement hypotheses and future discoveries.


Image taken from the paper: User interface of the tool



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