Home News Can an AI Predict the Language of Viral Mutation?

Can an AI Predict the Language of Viral Mutation?

Viruses lead a rather repetitive existence. They enter a cell, hijack its machinery to turn it into a viral copy machine, and those copies head on to other cells armed with instructions to do the same. So it goes, over and over again. But somewhat often, amidst this repeated copy-pasting, things get mixed up. Mutations arise in the copies. Sometimes, a mutation means an amino acid doesn’t get made and a vital protein doesn’t fold—so into the dustbin of evolutionary history that viral version goes. Sometimes the mutation does nothing at all, because different sequences that encode the same proteins make up for the error. But every once in a while, mutations go perfectly right. The changes don’t affect the virus’s ability to exist; instead, they produce a helpful change, like making the virus unrecognizable to a person’s immune defenses. When that allows the virus to evade antibodies generated from past infections or from a vaccine, that mutant variant of the virus is said to have “escaped.”

Scientists are always on the lookout for signs of potential escape. That’s true for SARS-CoV-2, as new strains emerge and scientists investigate what genetic changes could mean for a long-lasting vaccine. (So far, things are looking okay.) It’s also what confounds researchers studying influenza and HIV, which routinely evade our immune defenses. So in an effort to see what’s possibly to come, researchers create hypothetical mutants in the lab and see if they can evade antibodies taken from recent patients or vaccine recipients. But the genetic code offers too many possibilities to test every evolutionary branch the virus might take over time. It’s a matter of keeping up.

Last winter, Brian Hie, a computational biologist at MIT and a fan of the lyric poetry of John Donne, was thinking about this problem when he alighted upon an analogy: What if we thought of viral sequences the way we think of written language? Every viral sequence has a sort of grammar, he reasoned—a set of rules it needs to follow in order to be that particular virus. When mutations violate that grammar, the virus reaches an evolutionary dead end. In virology terms, it lacks “fitness.” Also like language, from the immune system’s perspective, the sequence could also be said to have a kind of semantics. There are some sequences the immune system can interpret—and thus stop the virus with antibodies and other defenses—and some that it can’t. So a viral escape could be seen as a change that preserves the sequence’s grammar but changes its meaning.

The analogy had a simple, almost too simple, elegance. But to Hie, it was also practical. In recent years, AI systems have gotten very good at modeling principles of grammar and semantics in human language. They do this by training a system with data sets of billions of words, arranged in sentences and paragraphs, from which the system derives patterns. In this way, without being told any specific rules, the system learns where the commas should go and how to structure a clause. It can also be said to intuit the meaning of certain sequences—words and phrases—based on the many contexts in which they appear throughout the data set. It’s patterns, all the way down. That’s how the most advanced language models, like OpenAI’s GPT-3, can learn to produce perfectly grammatical prose that manages to stay reasonably on topic.

One advantage of this idea is that it’s generalizable. To a machine learning model, a sequence is a sequence, whether it’s arranged in sonnets or amino acids. According to Jeremy Howard, an AI researcher at the University of San Francisco and a language model expert, applying such models to biological sequences can be fruitful. With enough data from, say, genetic sequences of viruses known to be infectious, the model will implicitly learn something about how infectious viruses are structured. “That model will have a lot of sophisticated and complex knowledge,” he says.

This article is sourced from wired

Can an AI Predict the Language of Viral Mutation?
Pen Pusher Hackette
Pen Pusher Hackette is a content media organization that focuses on creating the most authentic content available on the internet. Apart from just writing the content for internet consumers, the organization also focuses on inventing the right marketing tool to help businesses target their potential consumers. From Entertainment to the knowledge-based information, we cover all aspects to connect with the billions of internet consumers.

LEAVE A REPLY

Please enter your comment!
Please enter your name here

Must Read

Fans Are Freaking – Hollywood Life

Is Cap back? Chris Evans hung up his star-studded shield after ‘Avengers: Endgame,’ but he’s reportedly in talks to return as Captain America. Needless...

Can an AI Predict the Language of Viral Mutation?

Viruses lead a rather repetitive existence. They enter a cell, hijack its machinery to turn it into a viral copy machine, and those copies...

Canada to receive one million COVID-19 vaccine doses a week starting in April: general

Maj.-Gen. Dany Fortin, the military commander leading Canada's COVID-19 vaccine logistics, said today that manufacturers are expected to deliver up to one million doses...

Sessions and top aides pushed for separating migrant families despite warnings, report finds

Former Attorney General Jeff Sessions and his aides were the "driving force" behind the separation of thousands of migrant families in 2017 and 2018,...

Stars We’ve Lost – Hollywood Life

A look back at the many celebs we’ve lost in 2021. From ‘General Hospital’ star John Reilly, to ‘That 70s Show’s Tanya Roberts, read...