Generative design and construction of functional plasmids with a DNA language model
DNA language models offer a new paradigm for sequence design, yet their ability to generate functional genomic sequences remains underexplored. Plasmids act as a good testbed for evaluating DNA language model generation potential due to their simplicity and ease of construction. Here, we develop an end-to-end pipeline for generative design of Escherichia coli plasmid backbones, from large-scale data curation through fine-tuning, sampling, bioinformatic assessment, and candidate selection. A curated plasmid library was assembled from PlasmidScope and Addgene, and PlasmidGPT, a GPT-2-style DNA model, was fine-tuned on these corpora using circular-aware batching and random crops. Generations (1,000 per model) were produced under two prompting strategies: a minimal ATG seed to expose default tendencies, and a GFP cassette to enforce functional context. From 1000 generated synthetic plasmids, 16 candidates survived strict filtering and these were prioritised for wet-lab validation. Three shortlisted plasmids were synthesised and found to be functional, supporting growth, antibiotic resistance, and GFP expression in E. coli. These represent, to our knowledge, the first full AI-generated plasmids to be synthesised and validated in vivo. This work demonstrates that curated fine-tuning and prompt-aware generation enable DNA language models to progress from raw sequence sampling to experimentally testable plasmid designs. The approach offers a foundation for extending DNA design optimisation beyond E. coli, toward broader applications across engineering biology.