ArticleNucleic acids research2020
Synthetic promoter design in Escherichia coli based on a deep generative network.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers.
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Who cites it
99 citing papers in PubMed, 192 citations in OpenAlex.
- Deep learning-drivenSynthetic and systems biotechnology · 2027Article
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- Engineered E. coli swarming for binary and analog input recording.Molecular systems biology · 2026Article
- PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- A CRISPR interference system for tunable gene expression integrated with a promoter library forMicrobiology spectrum · 2026Article
- Deciphering key factors of active learning performance in biomolecular design.Bioinformatics (Oxford, England) · 2026Article
- TargetGAN: A generative AI framework for the design of plant core promoters with targeted activity.Plant communications · 2026Article
- Sequence design for three-dimensional genome folding using Akita Semifreddo.bioRxiv : the preprint server for biology · 2026Article
- Context-aware synthetic promoter design using neural networks enables rewiring of eukaryotic transcriptional networks.NPJ systems biology and applications · 2026Article
- Engineering genetic elements for microbial protein expression systems: Advances, challenges, applications, and prospects.Synthetic and systems biotechnology · 2026Review
- Design prokaryotic cis-regulatory elements using language model.Nucleic acids research · 2026Article
- Advances in natural product discovery: strategies, technologies, and insights.Natural products and bioprospecting · 2026Review
- The renaissance ofFrontiers in immunology · 2026Review
- BlendSplice: A Frequency-Blended Generative Framework forComputational and structural biotechnology journal · 2026Article
- 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
- Unraveling the start element and regulatory divergence of core promoters across the domain bacteria.Nucleic acids research · 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
39 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Promoter design remains one of the most important considerations in metabolic engineering and synthetic biology applications. Theoretically, there are 450 possible sequences for a 50-nt promoter, of which naturally occurring promoters make up only a small subset. To explore the vast number of potential sequences, we report a novel AI-based framework for de novo promoter design in Escherichia coli. The model, which was guided by sequence features learned from natural promoters, could capture interactions between nucleotides at different positions and design novel synthetic promoters in silico. We combined a deep generative model that guides the search for artificial sequences with a predictive model to preselect the most promising promoters. The AI-designed promoters were optimized based on the promoter activity in E. coli and the predictive model. After two rounds of optimization, up to 70.8% of the AI-designed promoters were experimentally demonstrated to be functional, and few of them shared significant sequence similarity with the E. coli genome. Our work provided an end-to-end approach to the de novo design of novel promoter elements, indicating the potential to apply deep learning methods to de novo genetic element design.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.