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. 2025 Oct 24;331(Pt 2):148505.
doi: 10.1016/j.ijbiomac.2025.148505. Online ahead of print.

Directed optimization and generation of yeast promoter sequences driven by deep learning

Chong Wang  1 , Shuxin Li  2 , Xiao Guo  3 , Shaolei Geng  3 , Kaili Qu  2 , Mengyao Li  2 , Chang Wang  2 , Xuezhi Zhou  2 , Wu Ren  2 , Shiqi Pan  2 , Yurong Chai  4 , Yi Yu  5 , Tian-Yun Wang  6
Affiliations

Directed optimization and generation of yeast promoter sequences driven by deep learning

Chong Wang et al. Int J Biol Macromol. .

Abstract

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.

Keywords: Deep learning; Precise design; Promoter generation; Promoter optimization; Yeast cells.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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