Generative Design of New-to-nature Biosynthetic Assembly Lines with Genomic Language Modeling

Authors

Lanclos, N., Ibrahim, K., Cornman, A., Huang, M., Gill, V., Jiang, A., Abraham, J., Gin, J., Chen, Y., Petzold, C., Baerwald, J., Kortemme, T., Keasling, J., Hwang, Y.

Abstract

Reprogramming biosynthetic assembly lines can extend biosynthesis beyond the chemical space explored by nature. However, this remains difficult because assembly-line function depends on coordinated interactions across large multidomain enzymes. Here, we couple gLM2, a genomic language model trained on metagenomic sequences, with discrete diffusion and domain-level conditioning to enable generative design and optimization of biosynthetic gene clusters. We apply this approach to a chimeric type I polyketide synthase (PKS) engineered to produce {delta}-valerolactam, a molecule not naturally synthesized by PKSs. Through iterative redesign of two multi-domain regions in the context of the full PKS sequence, gLM2 progressively improved {delta}-valerolactam production, yielding variants with up to 9.4-fold higher titer than the starting enzyme. Together, these results demonstrate that evolutionary sequence information can be learned and applied to complex, multi-domain enzyme design problems, expanding biosynthetic assembly lines to produce molecules outside their natural biosynthetic repertoire.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 18 Sep 2026.

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