AI ‘Speaks’ Genetic ‘Dialect’ to Predict Future SARS-CoV-2 Mutations
It’s been five years since COVID-19 was declared a global pandemic. As SARS-CoV-2 shifts to endemic status, questions about its future evolution remain. New variants of the virus will likely emerge, driven by positive selection for traits such as increased transmissibility, longer infection duration and the ability to evade immune defenses. These changes could allow the virus to spread among previously immunized populations, potentially triggering new waves of infection.
Predicting new mutations in viruses is crucial for advancing life science research, particularly when trying to understand how viruses evolve, spread and affect public health. Traditionally, researchers rely on wet-lab experiments to study mutations. However, these experiments can be costly and time-consuming.
Researchers from the College of Engineering and Computer Science at Ó£»¨ÊÓƵ have developed a new method to predict mutations in protein sequences called Deep Novel Mutation Search (DNMS), a type of artificial intelligence model that uses deep neural networks.
For the study, they focused on the SARS-CoV-2 spike protein – the part of the virus responsible for helping it enter hum