Science | 利用基因组语言模型进行噬菌体从头生成设计

TL;DR - A Science paper (King et al., 2026) uses genome language models (Evo 1/Evo 2) to generate complete bacteriophage genomes de novo, yielding 16 experimentally viable synthetic phages — the first demonstration that generative AI can design functional genome-scale living systems, not just single proteins or circuits.

• Evo models were fine-tuned on ~15,000 Microviridae genomes and prompted with the ΦX174 start region (5.4 kb ssDNA, 11 genes); multi-layer filters enforced host tropism (major spike protein similarity), genome length, GC content, coding density, and evolutionary divergence.
• Of 302 designs synthesized, 285 assembled and 16 ("Evo-Φ") suppressed E. coli C growth, retaining target-host specificity while showing varied infection kinetics.
• Designs contained gene gains/losses, altered gene order, and non-coding regulatory changes; the replication origin stayed highly conserved, implying the model implicitly learned regional constraint. One Evo-Φ borrowed a DNA-packaging protein from distant phage G4, confirmed as a stable particle by cryo-EM.
• Cocktails of designed phages rapidly overcame ΦX174-resistant E. coli, while natural ΦX174-like cocktails did not — suggesting therapeutic value. Limits noted: small genomes only, training-data bias, and biosafety review needs.