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Cis-regulatory Sequences02:02

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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Design prokaryotic cis-regulatory elements using language model.

Yan Xia1,2, Jinyuan Sun3, Xiaowen Du1

  • 1Department of Gastroenterology, Aerospace Center Hospital, College of Life Science, Beijing Institute of Technology, Beijing 100081, China.

Nucleic Acids Research
|February 16, 2026
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Summary
This summary is machine-generated.

Researchers developed PromoGen2, a language model for designing prokaryotic cis-regulatory elements (CREs) without prior data. This tool enables functional promoter design for thousands of prokaryotes, advancing synthetic biology and microbiology.

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Area of Science:

  • Computational Biology
  • Genomics
  • Synthetic Biology

Background:

  • Designing cis-regulatory elements (CREs) for prokaryotes is crucial for synthetic biology and microbiology.
  • Existing deep learning models for CRE design are limited in scope and applicability across diverse prokaryotic species.
  • A broadly applicable platform for generating functional promoters for thousands of prokaryotes is needed.

Purpose of the Study:

  • To introduce PromoGen2, a novel language model for designing prokaryotic CREs without prior experimental data.
  • To develop a framework (Promoter-Factory) for designing promoters from unannotated genomes using PromoGen2.
  • To create a taxonomy-aware model (PromoGen2-proka) for CRE design based on prokaryotic genera.

Main Methods:

  • Pretraining PromoGen2 on CREs from 17,000 prokaryotic genomes.
  • Evaluating PromoGen2's zero-shot prediction accuracy for promoter strength across species.
  • Designing and experimentally validating artificial CREs in multiple bacterial species.
  • Developing and applying the Promoter-Factory framework for promoter design in unannotated genomes.
  • Introducing and validating PromoGen2-proka for genus-specific CRE design.

Main Results:

  • PromoGen2 achieved the highest zero-shot prediction correlation of promoter strength (0.50 Spearman correlation), significantly outperforming baselines.
  • Designed artificial CREs showed a 100% success rate in four different bacterial species.
  • Promoter-Factory successfully designed active promoters for a newly isolated halophilic bacterium, driving lycopene overproduction.
  • PromoGen2-proka demonstrated reliable success rates in experimental validation for genus-specific CRE design.

Conclusions:

  • PromoGen2 offers a broadly applicable platform for designing functional prokaryotic CREs without prior experimental data.
  • The Promoter-Factory framework enables promoter design from unannotated genomes, supporting synthetic biology applications.
  • PromoGen2-proka enhances CRE design specificity by considering prokaryotic taxonomy.
  • The combined tools address a critical need in prokaryotic synthetic biology and microbiology research.