Computational Models Predict Future Strains.

• Position: COVID-19 VARIANTS

Efforts to contain the spread of SARS-CoV-2 may benefit from a new analytical tool developed by a team led by biologists at Boston College, who report their computer simulation of molecular interactions can predict mutations of the virus and help develop insights into future variants of concern before they emerge.

'We computationally predict what mutations allow better binding to host receptors and better evasion of antibodies," says Babak Momeni, assistant professor of biology and a lead researcher on the project. "Such mutations can potentially lead to a future variant of concern. Having this knowledge from our model would help with readiness for detecting and preventing, as well as treating, emerging and future variants."

Quantum mechanical modeling allowed the team to develop an initial set of predictions about the role of mutations on infectivity and immune response evasion of Omicron and other SARS-CoV-2 variants with human host cells.

The spike protein of the coronavirus, or SARS-CoV-2, binds to ACE2, a receptor on the host cells, which allows the virus to enter the cells and infect it. Binding is the first step for infection, and several mutations in previous variants of concern have been shown to be important for increasing the spike's binding to human ACE2.

The Omicron variant of the coronavirus is suspected to be the most infectious yet by binding to human receptors better than the Delta variant, and the team's findings show it may have the...