Abstract
Promoters are core elements in regulating gene expression. The design and optimization of functional promoters is crucial for enhancing metabolic pathway construction and advancing gene therapy. Deep learning-based methods have shown great potential in promoter design. However, existing studies mainly focus on designing strong promoters, neglecting the practical need for promoters with varying regulatory intensities. Here, we propose a novel promoter design method, PromoDGDE, to design promoters with desirable expression levels and apply it to the promoter design of Escherichia coli and Saccharomyces cerevisiae. It uses Diffusion-GAN to learn the feature distribution of natural sequences and generate new promoters. Then, reinforcement learning and evolutionary algorithms are combined to dynamically optimize the synthetic sequences. In silico analyze results demonstrate that PromoDGDE outperforms existing methods, generating promoters that not only possess biological significance but also achieve the intended function. In vivo experiment results demonstrate that the synthetic promoters exhibit expression activity, with over 60% of the sequences showing the expected regulatory effects. These results confirm the practical effectiveness of PromoDGDE and demonstrate its ability to provide an efficient and flexible solution for complex design needs in synthetic biology.