Gu Yijun, Su Jianye, Xia Junfeng, Wu Panpan, Wu Hang, Su Yansen, Wei Pi-Jing, Zheng Chun-Hou
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, 111 Jiulong Road, Hefei 230601 Anhui, China.
Institutes of Physical Science and Information Technology, Anhui University, 111 Jiulong Road, Hefei 230601 Anhui, China.
Nucleic Acids Res. 2025 Aug 27;53(16). doi: 10.1093/nar/gkaf833.
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.
启动子是调控基因表达的核心元件。功能性启动子的设计与优化对于加强代谢途径构建和推进基因治疗至关重要。基于深度学习的方法在启动子设计中已显示出巨大潜力。然而，现有研究主要集中在设计强启动子，而忽略了对具有不同调控强度的启动子的实际需求。在此，我们提出一种新颖的启动子设计方法PromoDGDE，用于设计具有理想表达水平的启动子，并将其应用于大肠杆菌和酿酒酵母的启动子设计。它使用扩散生成对抗网络（Diffusion-GAN）来学习天然序列的特征分布并生成新的启动子。然后，将强化学习和进化算法相结合，对合成序列进行动态优化。计算机模拟分析结果表明，PromoDGDE优于现有方法，生成的启动子不仅具有生物学意义，而且能实现预期功能。体内实验结果表明，合成启动子具有表达活性，超过60%的序列显示出预期的调控效果。这些结果证实了PromoDGDE的实际有效性，并证明其能够为合成生物学中的复杂设计需求提供高效且灵活的解决方案。
