To explore the utility of our genome design method for creating resilient phage therapies, we evolved a generated phage cocktail against three different ΦX174-resistant E. coli strains. The generated cocktail rapidly overcame resistance against all strains while ΦX174 did not.

By directly competing the phages against each other, we observed several generated phages that outcompeted ΦX174 or showed faster lytic dynamics, highlighting the ability of our method for designing high fitness mutations.

The viable generated phages harbored hundreds of novel mutations, many of which do not map to any sequence seen in nature. The cryo-EM structure of one phage revealed a genome packaging mechanism designed by Evo that was previously found lethal in rational engineering attempts.

We synthesized and tested 285 generated phage genomes in E. coli C. 16 generated phages inhibited growth in E. coli C but showed no off-target infection in E. coli strains outside of ΦX174’s natural range, demonstrating the intended host specificity.

By fine-tuning Evo 1 and Evo 2 on Microviridae sequences, we honed the models’ understanding of ΦX174-like genomes, which allowed us to generate sequences fulfilling our design criteria with a high success rate.

ΦX174 is a small Microviridae phage that infects its host E. coli C. It has a very intricate genetic architecture, making it a challenging template. We established our design criteria on ΦX174 and Microviridae sequences, including a “tropism constraint” for host specificity.

We first needed clear design criteria to guide our genome generation process. As a design template, we chose ΦX174, a classic phage in molecular biology, which was the first genome ever sequenced and synthesized.

But can DNA language models generate complete, viable genomes? To investigate this, we developed a modular framework for designing phages targeting a chosen bacteria, to maximize benefit for phage-based biotechnologies and therapeutics.

Designing a genome is an incredibly complex task. The overwhelming number of considerations has limited what we’ve previously been able to achieve in synthetic biology.

We chose to generate bacteriophage genomes, given their utility in biotechnology and therapeutics, and because they are safe and feasible to test in the lab. Phages are viruses that infect and kill bacteria, and are emerging as a promising strategy to combat rising antibiotic resistance.

Many of the most complex and useful functions in biology emerge at the scale of whole genomes.

Today, we share our preprint “Generative design of novel bacteriophages with genome language models”, where we validate the first, functional AI-generated genomes 🧵