ArticleNature communications2023
Deep flanking sequence engineering for efficient promoter design using DeepSEED.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- UnconditionalSynthetic and systems biotechnology · 2026Article
- PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Programmable microbial therapeutics: advances in engineered bacteria for targeted in vivo delivery and precision medicine.Journal of advanced research · 2026Review
- Deciphering key factors of active learning performance in biomolecular design.Bioinformatics (Oxford, England) · 2026Article
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Multi-gene Co-expression systems inSynthetic and systems biotechnology · 2026Review
- Engineering oncolytic bacteria as precision cancer therapeutics: design principles, therapeutic strategies, and translational perspectives.Protein & cell · 2026Review
- De novo design of functional nucleic acids of aptamers.Nature computational science · 2026Article
- Design prokaryotic cis-regulatory elements using language model.Nucleic acids research · 2026Article
- Deciphering the 3D genome organization across species from Hi-C data.Nucleic acids research · 2026Article
- A unified computational framework for quantitative design and optimization of transcriptional regulation across bacterial species.Nucleic acids research · 2026Article
- Engineering bidirectional chloroplast promoters for tunable co-expression of multiple genes in microalgae (Chlamydomonas reinhardtii).Communications biology · 2026Article
- Advancing biological taxonomy in the AI era: deep learning applications, challenges, and future directions.Science China. Life sciences · 2026Review
- Synthetic tunable promoters for flexible control of multi-gene expression in mammalian cells.Journal of advanced research · 2025Article
- Deep Generative Model-Driven Design of Microbial Synthetic Promoters.Journal of microbiology and biotechnology · 2025Review
- Engineering wetware and software for the predictive design of compressed genetic circuits for higher-state decision-making.Nature communications · 2025Article
- iPro-MP: a BERT-based model to predict multiple prokaryotic promoters.Genome biology · 2025Article
- Current status and future perspectives of multi-modal bacteria-based cancer therapies.Clinical and translational medicine · 2025Review
- Review
- De novo promoter design method based on deep generative and dynamic evolution algorithm.Nucleic acids research · 2025Article
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.
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