DAPE-PD: Diffusion adaptation of genomic foundation model enables tailored-generation of cis-regulatory DNA
Genomic foundation models (GFMs) have transformed our ability to learn the evolutionary language encoded in genomes, yet their potential for programmable regulatory DNA engineering remains underexplored. Regulatory function depends on combinatorial interactions among distributed sequence elements, motivating generation with bidirectional context. Here, we introduce DAPE-PD (Diffusion Adaptation of Pretrained Evo for Promoter Design), which adapts the autoregressive Evo model into a bidirectional masked diffusion model without foundation-scale retraining. DAPE-PD combines pretrained genomic representations with iterative unmasking to generate regulatory DNA sequences. Following genus-specific fine-tuning on genome-derived intergenic sequences, DAPE-PD generated sequences that retained genus-associated compositional features despite low similarity to the fine-tuning datasets. Experimental validation identified functional de novo promoters, while in silico prioritization recovered active designs from small experimental candidate sets across three non-model gut commensals. This framework connects genomic pretraining with diffusion-based generation, extending GFMs from sequence understanding toward functional cis-regulatory DNA engineering in living cells.