{"version":1,"type":"story","url":"https://digestai.news/story/generative-ai-designs-de-novo-thiolation-domains-that-boost-nrps-yield","json":"https://digestai.news/story/generative-ai-designs-de-novo-thiolation-domains-that-boost-nrps-yield.json","markdown":"https://digestai.news/story/generative-ai-designs-de-novo-thiolation-domains-that-boost-nrps-yield.md","slug":"generative-ai-designs-de-novo-thiolation-domains-that-boost-nrps-yield","headline":"Generative AI designs de novo thiolation domains that boost nrps yields up to ~3‑fold","summary":"Researchers combined pretrained protein generators—ESM3, ProteinMPNN and EvoDiff—with iterative design‑build‑test‑learn cycles to create 76 new thiolation (T) domains for non‑ribosomal peptide synthetases (NRPSs). The designs were inserted into 578 recombinant NRPS variants spanning minimal, full‑length and hybrid assembly lines and evaluated in vivo.\n\nAcross the experiments, many AI‑designed T‑domains supported peptide production, and the top performers raised product titers by roughly three times compared with the native T‑domain. A representative design, called AI‑2, also displayed improved biochemical properties: higher soluble expression, efficient refolding and a melting temperature about 12 °C above the wild‑type carrier. Molecular dynamics simulations suggested that while overall fold stability was retained, the engineered domains reshaped state‑dependent interdomain contact networks, explaining their context‑dependent activity.\n\nThe work demonstrates that generative protein models can engineer dynamic, multi‑domain enzymes whose function depends on transient interfaces, offering a new route for reprogramming biosynthetic assembly lines and expanding the toolkit for synthetic biology and drug discovery.","keyPoints":["76 de novo thiolation domains were generated with ESM3, ProteinMPNN and EvoDiff.","578 NRPS variants were built and tested, with the best designs increasing product titers up to ~3‑fold.","The AI‑2 design showed 12 °C higher melting temperature and higher soluble expression than the native domain."],"whyItMatters":"Shows generative protein models can reliably redesign dynamic enzyme domains, accelerating biotech synthesis of novel therapeutics and expanding synthetic biology capabilities.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":[],"models":["ESM3","ProteinMPNN","EvoDiff"],"people":[]},"firstPublishedAt":"2026-09-22T00:00:00Z","updatedAt":"2026-09-22T00:00:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"Nature Machine Learning","title":"Generative AI designs functional thiolation domains for reprogramming non-ribosomal peptide synthetases","url":"https://nature.com/articles/s41467-026-77963-6","publishedAt":"2026-09-22T00:00:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Generative AI designs de novo thiolation domains that boost nrps yields up to ~3‑fold\", 22 September 2026, https://digestai.news/story/generative-ai-designs-de-novo-thiolation-domains-that-boost-nrps-yield","publisher":"Digest AI","title":"Generative AI designs de novo thiolation domains that boost nrps yields up to ~3‑fold","datePublished":"2026-09-22T00:00:00Z","url":"https://digestai.news/story/generative-ai-designs-de-novo-thiolation-domains-that-boost-nrps-yield"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}