Species-specific design of artificial promoters by transfer-learning based generative deep-learning model
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Abstract
Native prokaryotic promoters share common sequence patterns, but are species dependent. For understudied species with limited data, it is challenging to predict the strength of existing promoters and generate novel promoters. Here, we developed PromoGen, a collection of nucleotide language models to generate species-specific functional promoters, across dozens of species in a data and parameter efficient way. Twenty-seven species-specific models in this collection were finetuned from the pretrained model which was trained on multi-species promoters. When systematically compared with native promoters, the Escherichia coli- and Bacillus subtilis-specific artificial PromoGen-generated promoters (PGPs) were demonstrated to hold all distribution patterns of native promoters. A regression model was developed to score generated either by PromoGen or by another competitive neural network, and the overall score of PGPs is higher. Encouraged by in silico analysis, we further experimentally characterized twenty-two B. subtilis PGPs, results showed that four of tested PGPs reached the strong promoter level while all were active. Furthermore, we developed a user-friendly website to generate species-specific promoters for 27 different species by PromoGen. This work presented an efficient deep-learning strategy for de novo species-specific promoter generation even with limited datasets, providing valuable promoter toolboxes especially for the metabolic engineering of understudied microorganisms.
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Adjustments to promoter strength, ribosome binding site sequences, and gene copy number affect protein expression, and systematically varying these regulatory components can identify gene expression configurations that enhance metabolic fluxes.
“Adjustments to promoter strength, modifications to ribosome binding site sequences, and optimisation of gene copy number are techniques that affect protein expression within the cyanobacterial host cell. By systematically varying these regulatory components, researchers can identify gene expression configurations that enhance metabolic fluxes in cyanobacterial engineered pathways [156].”
From Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression (2026) · cites this paper as [156]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredRecent deep-learning approaches have demonstrated the ability to design species-specific artificial promoters using transfer-learning models, which is particularly relevant for non-model cyanobacteria where experimentally characterised promoter datasets remain limited.
“In gene-expression engineering, recent deep-learning approaches have demonstrated the ability to design species-specific artificial promoters using transfer-learning models, which is particularly relevant for non-model cyanobacteria where experimentally characterised promoter datasets remain limited [156].”
From Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression (2026) · cites this paper as [156]
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“By systematically varying these regulatory components, researchers can identify gene expression configurations that enhance metabolic fluxes in cyanobacterial engineered pathways [156].”
- Authors
- Xia, Y., Du, X., Liu, B., Guo, S., & Huo, Y.-X.
- Affiliations
- Beijing Institute of Technology (CN)
- Corresponding author
- Guo, Shuyuan
- Huo, Yi-Xin
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-05-23
- Volume
- 52
- Issue
- 11
- Pages
- 6145-6157
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC11194083
- OpenAlex
- W4398761959
- Cited by
- 31
- References
- 55
- Citations by year
- 2026: 21 · 2025: 9 · 2024: 1
- FWCI
- 4.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- National Key R&D Program of China (2021YFC2100500)
- National Natural Science Foundation of China (32370095, 32371489, 62325202)
- Science and Technology Program of Tangshan (23130228E)
- Biological & Medical Engineering Core Facilities of the Beijing Institute of Technology
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- synthetic promoters, prokaryotic promoters, promoter design, transfer learning, generative deep learning, Bacillus subtilis, Escherichia coli, metabolic engineering
- MeSH terms
- Deep Learning, Bacillus subtilis, Escherichia coli, Promoter Regions, Genetic, Species Specificity, Neural Networks, Computer
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