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Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning

Author

Listed:
  • Daniel P. Cetnar

    (The Pennsylvania State University)

  • Ayaan Hossain

    (The Pennsylvania State University)

  • Grace E. Vezeau

    (The Pennsylvania State University)

  • Howard M. Salis

    (The Pennsylvania State University
    The Pennsylvania State University
    The Pennsylvania State University)

Abstract

mRNA degradation is a central process that affects all gene expression levels, though it remains challenging to predict the stability of a mRNA from its sequence, due to the many coupled interactions that control degradation rate. Here, we carried out massively parallel kinetic decay measurements on over 50,000 bacterial mRNAs, using a learn-by-design approach to develop and validate a predictive sequence-to-function model of mRNA stability. mRNAs were designed to systematically vary translation rates, secondary structures, sequence compositions, G-quadruplexes, i-motifs, and RppH activity, resulting in mRNA half-lives from about 20 seconds to 20 minutes. We combined biophysical models and machine learning to develop steady-state and kinetic decay models of mRNA stability with high accuracy and generalizability, utilizing transcription rate models to identify mRNA isoforms and translation rate models to calculate ribosome protection. Overall, the developed model quantifies the key interactions that collectively control mRNA stability in bacterial operons and predicts how changing mRNA sequence alters mRNA stability, which is important when studying and engineering bacterial genetic systems.

Suggested Citation

  • Daniel P. Cetnar & Ayaan Hossain & Grace E. Vezeau & Howard M. Salis, 2024. "Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54059-7
    DOI: 10.1038/s41467-024-54059-7
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    References listed on IDEAS

    as
    1. Travis L. LaFleur & Ayaan Hossain & Howard M. Salis, 2022. "Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Linxia Liu & Jinlong Li & Yuanming Gai & Zhizhong Tian & Yanyan Wang & Tenghe Wang & Pi Liu & Qianqian Yuan & Hongwu Ma & Sang Yup Lee & Dawei Zhang, 2023. "Protein engineering and iterative multimodule optimization for vitamin B6 production in Escherichia coli," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    3. Inigo Barrio-Hernandez & Jingi Yeo & Jürgen Jänes & Milot Mirdita & Cameron L. M. Gilchrist & Tanita Wein & Mihaly Varadi & Sameer Velankar & Pedro Beltrao & Martin Steinegger, 2023. "Clustering predicted structures at the scale of the known protein universe," Nature, Nature, vol. 622(7983), pages 637-645, October.
    4. Grace E. Vezeau & Lipika R. Gadila & Howard M. Salis, 2023. "Automated design of protein-binding riboswitches for sensing human biomarkers in a cell-free expression system," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
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