AI-Designed Viruses Are Replicating and Killing Bacteria

A petri dish full of dead bacteria isn’t usually cause for celebration. But for Stanford’s Brian Hie it was a game-changer in his efforts to create synthetic life.

The perpetrator was a type of virus called a bacteriophage that infects and kills bacteria but not human cells. Bacteriophages have evolved over eons to take out dangerous bacteria and are potentially a powerful tool in the fight against antibacterial resistance.

But the new virus erased evolution from the equation. An AI similar to ChatGPT designed its entire genome. The new genetic code allowed the synthetic virus to replicate, infect, and destroy bacteria, marking the first step towards an AI-designed life form.

To be clear, although the virus works like its natural counterparts, it’s not exactly “alive.” Viruses are made of tiny scraps of genetic material and need a host—in this case, bacteria—to replicate and spread.

Even so, these viruses are the closest scientists have come to engineering new forms of life using generative AI. The results could bolster treatments against dangerous bacterial infections and shed light on how to build more complex artificial cells.

“This is the first time AI systems are able to write coherent genome-scale sequences,” Hie told Nature. The work was published as a preprint on bioRxiv and not peer-reviewed.

Genetic Tinkering

The genetic playbook for all life on Earth is relatively simple. Four molecules represented by the letters A, T, C, and G are arranged in three-letter groups that code amino acids and proteins.

Synthetic biologists fiddle with this genetic code by adding beneficial genes or deleting those that cause disease. Thanks to their tinkering, we can now produce insulin and a variety of other medications in E. Coli, a bacteria commonly used in the lab and biomanufacturing.

Now generative AI is changing the game again.

These algorithms can already dream up DNA sequences, protein structures, and large molecular complexes from scratch. But building a functional genome is much harder. The sequences need to encode life’s machinery and make sure it works together as expected.

“Many important biological functions arise not from single genes, but from complex interactions encoded by entire genomes,” wrote the team.

The new study turned to Evo 1 and Evo 2, two generative AI models developed at the nonprofit Arc Institute. Rather than inhaling blogs, YouTube comments, and Reddit posts, Evo 2 was trained on roughly 128,000 genomes—9.3 trillion DNA letter pairs—spanning all of life’s domains, making it the largest AI model for biology to date.

The models eventually learned how changes in DNA sequences alter RNA, proteins, and overall health, allowing them to write new proteins and small genomes from scratch.

Evo 1, for example, generated new CRISPR gene-editing tools and bacterial genomes—although the latter often contained wildly unnatural sequences that prevented them from powering living synthetic bacteria. Evo 2 produced a full set of human mitochondrial DNA that churned out proteins similar to naturally occurring ones. The model also created a minimal bacterial genome and a yeast chromosome. But none of these were tested in living cells to see if they worked.

Genome Creator

The new work focused on simpler biological systems—bacteriophages. These viruses attack bacteria and are now in clinical trials to combat antibiotic resistance. Synthetic bacteriophages could, in theory, be even deadlier.

The team began with phiX174, a virus with just a single strand of DNA, 11 genes, and 7 chunks of gene-regulating DNA. Despite its petite genome, the virus has all it needs to infect hosts, replicate, and spread. It also has a long history in synthetic biology. Its genome has been fully sequenced and synthesized in the lab, so it’s easier to tinker with. It’s also been shown to be safe and “has continually served as a pivotal model within molecular biology,” wrote the team.

Although the Evo AI models were already trained on around two million genomes, the team fine-tuned their abilities by putting them through a kind of “masterclass” on phage DNA. They also added genome and protein constraints seen in these viruses and prompts to encourage novelty.

The AI models next generated thousands of genomes, some containing obvious errors. Both models relied on the template from training but also came up with their own spins on a phage genome. Roughly 40 percent of their DNA letters were similar to phiX174, but some sequences were out the box with completely different genetic identities.

The team zeroed in on and synthesized 302 potential candidates and tested them for their ability to infect and destroy bacteria. Overall, 16 AI-designed candidates acted like bacteriophages. They tunneled into E. Coli bacteria, replicated, burst through the bacteria’s membranes, and spread to neighboring cells. Surprisingly, a combination of the synthetic viruses could also infect and kill other strains of E. Coli, which they were not designed to do.

“These results demonstrate that genome language models…can design viable phage genomes,” wrote the team.

A Biosafety Brake

Generative AI could massively speed up scientists’ ability to write synthetic life. Instead of extensive trial-and-error lab tests to decode how genes and other molecular components work together, Evo has essentially internalized those interactions. 

With more testing, the technology could be a boon for phage therapy, helping researchers treat serious bacterial infections in people or crops, such as cabbage and bananas.

But the thought of AI-generated viruses can be alarming. So, the team added a series safeguards. Evo’s initial training intentionally left out information on viruses that infect eukaryotes, including human cells. And without humans guiding the models—an approach called supervised learning—the algorithms struggled to design functional genomes. Also, both the phiX174 virus and E. Coli have a long and safe history in biomedical research.

Regardless, the techniques here could potentially be used to enhance human-infecting viruses. “One area where I urge extreme caution is any viral enhancement research, especially when it’s random so you don’t know what you are getting,” J. Craig Venter, a pioneer in synthetic biology, told MIT Technology Review.

Engineering a larger genome, such as that of E. Coli, would need more work. Viruses hijack their host’s cells to replicate. Bacteria, in contrast, need the molecular machinery to grow and proliferate. Meanwhile, debates on the ethics and safety of synthetic life are gaining steam.

The authors say their results lay the foundations for the design of useful living systems at the genome scale with generative AI. Although there’s likely a long and bumpy road ahead, Hie is optimistic. With lots more work, “the next step is AI-generated life,” he said.