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

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.

doi:10.64898/2026.09.11.750945

bioRxiv · 10.64898/2026.09.11.750945

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

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.

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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.

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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.. 2026-09-17. Generative Design of New-to-nature Biosynthetic Assembly Lines with Genomic Language Modeling. https://doi.org/10.64898/2026.09.11.750945

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