PEAC: Joint Generation of Promoter-Enhancer Pairs with Activity Conditioning

Designing regulatory DNA with predictable gene-expression activity remains challenging since promoters and enhancers usually interact as pairs, while current approaches always focus on the design of individual regulatory elements. Ignoring promoter–enhancer interactions results in regulatory elements that perform well individually but fail to achieve the desired activity when combined. To address this problem, we propose PEAC, a Promoter-Enhancer Activity-Conditioned co-design framework that achieves joint modeling of promoter-enhancer pairs. Specifically, we first train a student model that is distilled from a promoter teacher and an enhancer teacher simultaneously, and then fine-tune the student model with the activity constraint to achieve the conditional generation of the target activity. Furthermore, we also incorporate pair-level fusion and latent distribution alignment to make the generated promoter-enhancer pairs better matched. Extensive experiments show that PEAC achieves excellent performance in generating activity conditioned promoter-enhancer pairs with sequence realism. In oracle-based evaluation, PEAC demonstrated the ability to generate pairs across low-, medium-, and high-activity regimes, with target regime fractions of 79.1%, 91.7% and 67.8%, compared with 31.8%, 68.6%, and 11.6% for baseline. This improvement is accompanied by realistic GC content, high motif enrichment, and low biological distance compared to reference. Together, these results establish PEAC as a native promoter-enhancer generator that makes pair-level activity the explicit design target and provides an oracle-evaluated framework for activity-conditioned regulatory sequence co-design.