Generative design of bacteriophages with genome language models

• 成果类型: Article
• 署名作者: King, Samuel H.; Driscoll, Claudia L.; Li, David B.; Guo, Daniel; Merchant, Aditi T.; Brixi, Garyk; Wilkinson, Max E.; Hie, Brian L.
• 署名单位: Stanford University; Stanford University; Stanford University; Stanford University; Stanford Medicine; Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute; Stanford University
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-8075; 1095-9203
• DOI: 10.1126/science.aec2657
• 发表日期: 2026-08-06
• 页码: eaec2657
• 关键词: NUCLEOTIDE-SEQUENCE; PHI-X174; dna; protein; EVOLUTION; genes; mechanisms; prediction; software; mutants

摘要

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 Phi X174 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 Phi X174-resistant Escherichia coli 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.