Large genome models used to design new viruses - Ars Technica

Skip to content

AI

Biz & IT

Cars

Culture

Gaming

Health

Policy

Science

Security

Space

Tech

Forum

Subscribe

Story text

Size

Small<br>Standard<br>Large

Width

Standard<br>Wide

Links

Standard<br>Orange

* Subscribers only

Learn more

Pin to story

Theme

Search

Sign In

Sign in dialog...

Text<br>settings

Story text

Size

Small<br>Standard<br>Large

Width

Standard<br>Wide

Links

Standard<br>Orange

* Subscribers only

Learn more

Minimize to nav

A lot of the AI work in biology has been focused on designing proteins. That’s in part because proteins do most of the business of life, catalyzing the interesting chemistry and structuring cells. So, figuring out how to make a new protein can mean directly tinkering with biochemistry, providing new and potentially useful functions.

Since the genetic code provides a layer of abstraction between DNA and proteins, it wasn’t obvious what a model trained on DNA could do. Yet people went ahead and made a large genome model, and it turned out to be able to output DNA sequences that could encode functional proteins in bacteria and mimic the gene structures found in complex cells. Now, those same models have been used to output the genomes of viruses that infect bacteria.

This isn’t science fiction—all the viruses the models created are closely related to an existing virus. But they do have some distinct features that would be challenging to evolve. And the researchers who did the work, based at Stanford University, suggest we may want to start thinking now about preparing for the potential that someone could develop a related AI that can design a virus that targets vertebrates.

Large genome models

Large language models are essentially trained on their ability to predict the next bit of verbiage in as large a compendium of human-generated text as their developers can get ahold of. Large genome models are the same approach, but applied to DNA. To an extent, that simplifies matters, given that DNA only uses four “letters,” A, T, C, and G. But it’s more complicated in that genomes typically have areas where the next letter matters a great deal, interspersed with sequences where the next base could be anything and it wouldn’t matter.

So large genome models need to recognize the biological context of the sequence they’re outputting in many situations where we humans haven’t figured it out yet. Yet, if we feed them enough genome sequences, they seem to be able to. Bacterial genes with related functions tend to cluster together. Prompt a large genome model with the sequence of part of a cluster, and it’ll output DNA sequences that encode proteins with related functions—potentially including working proteins that look like nothing else we’ve identified so far.

But our own lack of knowledge places limits on what we can do with these models. If we prompt them with a bit of sequence from a complex cell, it will respond with a string of bases that contain what look like genes and regulatory DNA. But, since most genes can be located essentially anywhere in a eukaryotic genome, we have no idea what functions these hallucinated genes might perform (if any), so we can’t really test them in any way.

Still, as a precaution while training these models, termed Evo 1 and Evo 2, the researchers did not provide them with any sequences from viruses that target complex cells. Even if we can’t understand what they output, there’s a chance that they’ll output something dangerous.

But there’s a whole world of viruses that attack bacteria and are unable to infect humans. And those, the Evo developers figured, are fair game. So, rather than looking at whether Evo 1 and Evo 2 could output reasonable-looking genes, they decided to test if they could output an entire genome.

Order from chaos

The model virus they chose as a test case is the catchily named ΦX174, part of a larger family of viruses that infect E. coli. In addition to the convenience of being able to use the best-studied bacteria on the planet for tests, ΦX174 has a number of notable features. It’s fairly simple: At 11 genes spread over about 5,400 bases, all 11 of those genes have identified functions, and the virus’s infection cycle is well characterized.

It also has a useful feature from the perspective of working with a large genome model: The end of the virus always has the same short sequence of bases. So, if you use those bases as a prompt, the large genome model should be able to recognize that it needed to respond by outputting a sequence that is in some way related to ΦX174.

To further prepare their models, Evo 1 and Evo 2 were fed over 2 million additional bases of DNA sequences from viruses that infect bacteria (termed bacteriophages), and then fine-tuned with sequences specific to Microviridae, the group that ΦX174 belongs to. After that, they experimented with different prompts. Prompt with too much of the ΦX174 start sequence, and it would simply spit the...