Researchers report that mutations in Omicron can increase the coronavirus’ affinity for human cells, enabling the virus to evade antibodies and spread more easily.
A team from Boston College in the US developed a computer model that predicts mutations in the SARS-CoV-2 virus, providing insight into future variants of concern before they emerge.
Based on the model, the team developed preliminary predictions about how mutations may affect Omicron and other SARS-CoV-2 variants’ infectivity and immune response evasion.
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“We find that Omicron has not reached its full potential to bind human host cells,” said Babak Momeni, Assistant Professor at Boston College.
“We identify mutations that can strengthen the virus affinity for the human cell, which could increase infectivity and evasion of antibodies,” said Momeni, lead researcher on the project.
Coronavirus Omicron is believed to be the most infectious yet because it binds better to human receptors than Delta. The study also indicates the spike proteins of Omicron bind better to the ACE2 receptor than their Delta counterparts.
The yet-to-be peer-reviewed research posted on bioRxiv indicates that Omicron may be able to evolve even stronger binding in order to increase transmission and infectivity.
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The spike protein of Coronavirus binds to a receptor on the host cell called ACE2, allowing it to enter and infect the cells.
First, the spike must bind to ACE2, and several mutations in previous variants of concern have been found to be important for increasing its binding to ACE2.
“We use a fully quantum mechanical model to theoretically assess how different mutations in the spike can contribute to its increased, or decreased, binding strength to human ACE2,” said Momeni.
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“The modelling shows that Omicron binds to receptor proteins stronger than the Delta variant,” he said. Researchers said the model predicts what mutations allow for better binding to host receptors and better antibody evasion. According to the researchers, such mutations could 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,” Momeni added.
(With inputs from agencies)