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TitleGenerative design of bacteriophages with genome language models.
Journal, issue, pagesScience, Vol. 393, Issue 6811, Page eaec2657, Year 2026
Publish dateAug 6, 2026
AuthorsSamuel H King / Claudia L Driscoll / David B Li / Daniel Guo / Aditi T Merchant / Garyk Brixi / Max E Wilkinson / Brian L Hie /
PubMed AbstractMany important biological functions arise not from single genes but from complex interactions encoded by entire genomes. We report the first generative design of complete bacteriophage genomes using ...Many important biological functions arise not from single genes but from complex interactions encoded by entire genomes. We report the first generative design of complete bacteriophage genomes using genome language models. We generated viable bacteriophages with target host tropism, using the phage ΦX174 as our design template. Experimental testing yielded 16 phages with diverse fitness profiles in laboratory conditions. Cryo-electron microscopy confirmed that a generated phage utilizes an evolutionarily distant DNA packaging protein in its capsid. A cocktail of generated phages rapidly overcomes ΦX174-resistant strains, demonstrating a path toward artificial intelligence-generated phage therapies against rapidly evolving bacterial pathogens. This work provides a blueprint for the design of diverse synthetic bacteriophages and useful biological systems at the genome scale.
External linksScience / PubMed:42561074
MethodsEM (single particle)
Resolution2.76 - 2.9 Å
Structure data

77390

36cq

EMDB-77390, PDB-36cq:
Structure of the PhiX174 bacteriophage
Method: EM (single particle) / Resolution: 2.76 Å

77391

36cr

EMDB-77391, PDB-36cr:
Structure of the Evo-Phi36 bacteriophage
Method: EM (single particle) / Resolution: 2.9 Å

Source
  • sinsheimervirus
KeywordsVIRUS / Phage / Microviridae / Icosahedral / Bacteriophage / AI / Evo

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