Science重磅：AI首次生成完整噬菌体基因组，可存活、能抑菌

TL;DR - Stanford researchers published in Science the first AI-generated complete phage genomes, using the Evo 1/Evo 2 DNA foundation models to design viable ΦX174-like bacteriophages that infect and lyse E. coli C. It marks a shift from single-gene/circuit design to generative whole-genome design, with direct implications for phage therapy and biosecurity governance.

• Pipeline: Evo 1/Evo 2 (pretrained on >2M phage genomes) were fine-tuned on ~15,000 Microviridae sequences, prompted with ΦX174-like start consensus (~4–9 nt prompts, sampling temperature 0.7–0.9), then filtered by length, GC content, ΦX174-like gene architecture, host tropism, and distance from wild-type; Evo 2 showed stronger base generation.
• Yield and specificity: ~300 designed genomes tested → 16 viable phages; viability correlated with similarity to known natural genomes, and all infected E. coli C without inhibiting 6 other tested strains.
• Novel sequence combinations: 13 of the viable genomes carried mutations unexplained by any single natural sequence; Evo-Φ36 swapped DNA packaging protein J for a shorter homolog from distant phage G4 — previously reported as non-viable — and cryo-EM confirmed compatible capsid interactions.
• Function and resistance: generated phages spanned a wider range of lysis kinetics than natural ΦX174-like phages, and cocktails overcame ΦX174-resistant strains CR1/CR2 in 1–2 passages (natural cocktails failed after 5), with breakthrough phages arising from recombination of 2–3 generated genomes; authors flag biosafety/biosecurity risks and call for safety experts across the design lifecycle.