AI generated its first working genome: a tiny bacteria killer

Artificial intelligence can dash off more than routine emails. It has now written tiny working genomes. 

Two AI models designed the blueprints for 16 viruses capable of attacking Escherichia coli in lab dishes, researchers report September 17 in a paper posted to bioRxiv.org. A mixture of these AI-generated bacteriophages stopped virus-resistant E. coli strains from growing, suggesting that the technique could help scientists design therapies capable of taking on tough-to-treat microbial infections. The work has not yet been peer-reviewed.

It’s the first time that AI has successfully generated an entire genome, says Brian Hie, a computational biologist at Stanford University and the Arc Institute in Palo Alto, Calif. And while it’s debatable whether viruses are alive or not, the work is a step toward using the technology to design living organisms.

AI models have already been used to devise individual genes and proteins. Creating an entire genetic blueprint from scratch, however, adds an extra layer of complexity because numerous genes and proteins need to work together, Hie says.

Hie and colleagues turned to two of their own AI models, called Evo 1 and Evo 2, to see if they could create genomes for bacteria-killing viruses. The models were trained on billions of pairs of the genetic alphabet’s basic units, A, C, G and T’s, from phage genomes the way ChatGPT was trained on novels and internet posts. The team used a bacteriophage called ΦX174 — which in 1977 became the first DNA-based genome ever sequenced — as a guide to help the AI design a similar genome.

Because ΦX174 has been so well-studied, “if the AI was making novel mutations to the phage, we would be able to see how novel they are,” Hie says. What’s more, bacteriophages don’t infect people, so it was safe to work with in the lab. Out of concern that the AI might design viruses that could harm people, the team did not train the models on any examples of viral pathogens.

Evo 1 and Evo 2 generated roughly 300 potential phage genomes. Of those, 16 produced viable viruses that could infect E. coli. Some of the phages even killed E. coli more quickly than ΦX174 did. And although ΦX174 couldn’t kill three phage-resistant strains of E. coli on its own, cocktails of AI-generated phages rapidly evolved to overcome the bacteria’s resistance to infection. 

The findings suggest that AI could help researchers develop viruses to use in phage therapy, a potential option to treat antibiotic-resistant bacterial infections. In such cases, “the need to find a phage that targets the bacterial strain would be very urgent,” says Kimberly Davis, a microbiologist at Johns Hopkins Bloomberg School of Public Health who wasn’t involved in the work. “Utilizing AI could be a powerful way of rapidly generating a phage match to treat patients.”

Davis notes that “the use of AI-generated phages would need to be tightly controlled.” For instance, extensive testing could make sure that such phages don’t interact with or harm other microbes.

AI-generated phages would ideally not only kill just one bad type of bacteria while sparing good bacteria that keep people healthy, Hie says, but might also evolve in ways that keep up with virus-resistant bacteria. Using AI to design entire organisms could also speed up microbial manufacturing processes such as antibiotic production or cultivate microbes that degrade plastic. 

And AI has the potential to help researchers make sense of genomes that are even more complex and develop new treatments for complicated diseases, Hie says. The human genome is more than half a million times the size of ΦX174’s genome, “so there’s a lot of work to go.”