Promoter design and optimization are critical for precise control of gene expression. However, existing methods remain constrained by insufficient understanding of sequence features and a dependence on pre-trained sequence-to-expression (S2E) predictors. To address this limitation, we present DOSDiff, a diffusion-based framework that learns promoter features and enables targeted optimization through local sequence editing without requiring an S2E model. DOSDiff controllably generates desired promoters by capturing promoter encoding rules, achieving a 4-mers distribution similarity of 0.8910 ± 0.0002 (n = 3), surpassing WGAN-GP (0.7282 ± 0.1103) and DBGM (0.7549 ± 0.0274) by 22.36 % and 18.03 %, respectively. In Saccharomyces cerevisiae, locally optimized genes of interest achieved one-to-one expression enhancement. Additionally, DOSDiff demonstrated generalization across different yeast species, with all optimized Pichia pastoris promoters retaining functional activity and achieving up to 1.70-fold expression gain in vivo validation. These results highlight DOSDiff as a promising and adaptable framework for precise promoter engineering.

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Wang C, Li S, Guo X, Geng S, Qu K, Li M, Wang C, Zhou X, Ren W, Pan S, ... Show all

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