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Generative AI Designs Functional Thiolation Domains for Reprogramming Non-Ribosomal Peptide Synthetases

MCML Authors

Vincent Pauline

→ Group Stefan Bauer
Algorithmic Machine Learning & Explainable AI

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) are an especially demanding target, yet a high-value one because they produce many clinically important natural products and offer a route to analogs that are often difficult or impractical to access by chemical synthesis. 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 built and tested 578 recombinant NRPS variants in vivo spanning minimal, full-length and hybrid assembly lines. AI-designed T-domains supported product formation across architectures, enabled catalytically active hybrids at recombined junctions and increased yields by up to ∼3-fold relative to NRPSs carrying the native T-domain. A representative design showed improved soluble expression, refolding, and a 12 °C higher melting temperature, while molecular dynamics simulations indicated preserved global stability but reshaped, state-dependent interdomain contact networks. Together, these results establish generative design as an effective route to context-conditioned optimization and reprogramming of biosynthetic assembly lines.

misc BBG+26

Preprint

Mar. 2026

Authors

E. F. Bülbül • S. Bang • K. George • G. Bianchi • P. Raj • S. Chung • V. Pauline • R. Hochstrasser • H. A. Minas • W. A. M. Elgaher • A. M. Kany • A. K. H. Hirsch • S. Schmitt • D. W. Heinz • O. V. Kalinina • D. Klakow • K. A. J. Bozhüyük