Machine-learning model finds SARS-COV-2 growing more infectious

A novel machine learning model developed by researchers at Michigan State University suggests that mutations to the SARS-CoV-2 genome have made the virus more infectious.

The model, developed by lead researcher Guowei Wei, professor in the departments of Mathematics and Biochemistry and Molecular Biology, analyzed SARS-CoV-2 genotyping from more than 20,000 viral genome samples. The researchers analyzed mutations to the spike protein—a protein primarily responsible for facilitating infection—and found that five of the six known virus subtypes are now more infectious.

As with any virus, many mutations are ultimately benign, posing little to no risk to infected patients. Some mutations even reduce infectiousness. But some mutations lead to a more infectious virus.

Wei and his team have studied and analyzed mutation patterns and locations for months, tracking changes against the official viral genome sample captured in January.

“Knowledge about the infectivity of SARS-CoV-2 is a vital factor for preventive measurements against COVID-19 and reopening the global economy,” Wei said. “A crucial question is what are the ramifications of these mutations to COVID-19 transmission, diagnostics, prevention and treatment.”

Viral infection occurs when the spike protein interacts with a human host cell receptor called angiotensin-converting enzyme 2—ACE2 for short. As it relates to ACE2, scientists are concerned about a concept known as binding affinity, or the strength of the binding interaction between the spike protein and host receptor during the…

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