Generative AI designs functional thiolation domains for reprogramming non-ribosomal peptide synthetases

Abstract

Abstract Large language models and generative protein design promise to accelerate biotechnology, but it remains unclear whether they can engineer dynamic megasynth(et)ases whose activity depends on transient, context-specific domain interfaces. Non-ribosomal peptide synthetases (NRPSs) exemplify this challenge and produce many clinically used therapeutics. Here we integrate pretrained generative models (ESM3, ProteinMPNN and EvoDiff) with design–build–test–learn cycles and data-guided prioritization to generate 76 de novo thiolation (T) domains. We build and test 578 recombinant NRPS variants in vivo spanning minimal, full-length, and hybrid assembly lines. AI-designed T-domains support product formation across architectures, enable catalytically active hybrids at recombined junctions, and increase yields by up to ~3-fold relative to NRPSs carrying the native T-domain. A representative design shows improved soluble expression, refolding, and a 12 °C higher melting temperature, while molecular dynamics simulations indicate preserved global stability but reshaped, state-dependent interdomain contact networks. Together, these results establish generative design as an effective route to context-conditioned engineering and reprogramming of biosynthetic assembly lines.