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Systematic representation and optimization enable the inverse design of cross-species regulatory sequences in bacteria

Author

Listed:
  • Pengcheng Zhang

    (Tsinghua University)

  • Qixiu Du

    (Tsinghua University)

  • Ye Wang

    (Tsinghua University
    Columbia University)

  • Lei Wei

    (Tsinghua University)

  • Xiaowo Wang

    (Tsinghua University)

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.

Suggested Citation

  • Pengcheng Zhang & Qixiu Du & Ye Wang & Lei Wei & Xiaowo Wang, 2025. "Systematic representation and optimization enable the inverse design of cross-species regulatory sequences in bacteria," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57031-1
    DOI: 10.1038/s41467-025-57031-1
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    References listed on IDEAS

    as
    1. Manish Kushwaha & Howard M. Salis, 2015. "A portable expression resource for engineering cross-species genetic circuits and pathways," Nature Communications, Nature, vol. 6(1), pages 1-11, November.
    2. Timothy C. Yu & Winnie L. Liu & Marcia S. Brinck & Jessica E. Davis & Jeremy Shek & Grace Bower & Tal Einav & Kimberly D. Insigne & Rob Phillips & Sriram Kosuri & Guillaume Urtecho, 2021. "Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    3. Andrew W. Senior & Richard Evans & John Jumper & James Kirkpatrick & Laurent Sifre & Tim Green & Chongli Qin & Augustin Žídek & Alexander W. R. Nelson & Alex Bridgland & Hugo Penedones & Stig Petersen, 2020. "Improved protein structure prediction using potentials from deep learning," Nature, Nature, vol. 577(7792), pages 706-710, January.
    4. Ashkaan K. Fahimipour & Thilo Gross, 2020. "Mapping the bacterial metabolic niche space," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    5. Stefan M. Gaida & Nicholas R. Sandoval & Sergios A. Nicolaou & Yili Chen & Keerthi P. Venkataramanan & Eleftherios T. Papoutsakis, 2015. "Expression of heterologous sigma factors enables functional screening of metagenomic and heterologous genomic libraries," Nature Communications, Nature, vol. 6(1), pages 1-10, November.
    6. Jinsen Li & Tsu-Pei Chiu & Remo Rohs, 2024. "Predicting DNA structure using a deep learning method," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
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