ArticleNature communications2025
Systematic representation and optimization enable the inverse design of cross-species regulatory sequences in bacteria.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Modular synthetic cross-kingdom promoters enable coordinated expression in Escherichia coli and Saccharomyces cerevisiae.Nucleic acids research · 2026Article
- PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deciphering key factors of active learning performance in biomolecular design.Bioinformatics (Oxford, England) · 2026Article
- Engineering genetic elements for microbial protein expression systems: Advances, challenges, applications, and prospects.Synthetic and systems biotechnology · 2026Review
- Synthetic Biology Strategies for Activating Cryptic BGCs inACS omega · 2026Review
- A unified computational framework for quantitative design and optimization of transcriptional regulation across bacterial species.Nucleic acids research · 2026Article
- The specificity landscape of WRKY transcription factors reveals the bidirectional influence of non-CG methylation.Nucleic acids research · 2025Article
- Systematic representation and optimization enable the inverse design of cross-species regulatory sequences in bacteria.Nature communications · 2025Article
- Predicting antibiotic resistance genes and bacterial phenotypes based on protein language models.Frontiers in microbiology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Regulatory sequences encode crucial gene expression signals, yet the sequence characteristics that determine their functionality across species remain obscure. Deep generative models have demonstrated considerable potential in various inverse design applications, especially in engineering genetic elements. Here, we introduce DeepCROSS, a generative artificial intelligence framework for the inverse design of cross-species and species-preferred 5' regulatory sequences in bacteria. DeepCROSS constructs a meta-representation using 1.8 million regulatory sequences from thousands of bacterial genomes to depict the general constraints of regulatory sequences, employs artificial intelligence-guided massively parallel reporter assay experiments in E. coli and P. aeruginosa to explore the potential sequence space, and performs multi-task optimization to obtain de novo regulatory sequences. The optimized regulatory sequences achieve similar or better performance to functional natural regulatory sequences, with high success rates and low sequence similarities with the natural genome. Collectively, DeepCROSS efficiently navigates the sequence-function landscape and enables the inverse design of cross-species and species-preferred 5' regulatory sequences.
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Registered trials
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.