PromoterDiff: De Novo Design Approach for Escherichia coli Promoters Based on a Diffusion Model

Promoter components play a critical role in the regulation of gene expression, directly determining the expression intensity of downstream target genes. Opting for high-quality promoters is essential for synthetic biology. In the existing literature, the method of designing high-quality promoters using Generative Adversarial Networks (GANs) is constrained by training difficulties, mode collapse, implicit generation, and information loss in deep networks after vector representation of promoters. Diffusion models are more easily trainable, possess elegant mathematical explanations, and can directly model the target distribution, potentially overcoming the issues faced by GANs mentioned above. In this paper, we propose a new method for promoter design, PromoterDiff, based on the diffusion model. Specifically, we adjusted the convolutional network structure of the diffusion model and introduced a bridging structure to adapt to the diffusion and reconstruction steps of the diffusion model when dealing with DNA sequence tensors containing a large number of zero elements. We used natural promoters of Escherichia coli as the training set to train the model, resulting in the successful design of 14,080 entirely new promoters. Through the analysis of the motifs, k-mer frequencies, and motif spacing constraints of these promoters, we confirmed that the diffusion model can capture the characteristics of natural promoters, and the quality of the generated promoters surpasses existing models. Through biological experiments, it has been confirmed that 83% of the promoter sequences designed by PromoterDiff surpass the activity of natural promoters, exceeding the current leading model by 13%. This underscores the immense potential of applying the diffusion model to de novo promoter design. This paper also provides a clear mathematical representation of the de novo promoter design task. According to our survey, this is the first time the diffusion model has been applied in the field of de novo promoter design.