AI Predicts Nightmare Covid-19 Mutations

For all their simplicity, viruses are sneaky little life forces.

Take SARS-Cov-2, the virus behind Covid-19. Challenged with the human immune system, the virus has gradually reshuffled parts of its genetic material, making it easier to spread among a human population. The new strain has already terrorized South Africa and shut down the UK, and recently popped up in the United States.

The silver lining is that our existing vaccines and antibody therapies are still likely to be effective against the new strain. But that’s not always the case. “Viral escape” is a nightmare scenario, in which the virus mutates just enough so that existing antibodies no longer recognize it. The consequences are dire: it means that even if you’ve already had the infection, or produced antibodies from a vaccine, those protections are now kneecapped or useless.

From an evolutionary perspective, viral mutations and our immune system are constantly engaged in a cat-and-mouse game. Last week, thanks to an utterly unexpected resource, we may now have a leg up. In a mind-bending paper published in Science, one team developed a tool to predict viral escape—and it came from natural language processing (NLP), the AI field of mimicking human speech.

Weird, right?

The team’s critical insight was to construct a “viral language” of sorts, based purely on its genetic sequences. This language, if given sufficient examples, can then be analyzed using NLP techniques to predict how changes to its genome alter its interaction with our immune system. That is, using artificial language techniques, it may be possible to hunt down key areas in a viral genome that, when mutated, allow it to escape roaming antibodies.

It’s a seriously kooky idea. Yet when tested on some of our greatest viral foes, like influenza (the seasonal flu), HIV, and SARS-CoV-2, the algorithm was able to discern critical mutations that “transform” each virus just enough to escape the grasp of our immune surveillance system.

“The language of viral evolution and escape … provides a powerful framework for predicting mutations that lead to viral escape,” said Drs. Yoo-Ah Kim and Teresa Przytycka at the National Institute of Health, who were not involved in the study but provided perspectives on it.

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