ArticleNature communications2024
Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Long-range mRNA folding shapes expression and sequence of bacterial genes.Molecular cell · 2026Article
- Programmable in vivo mRNA circularization for enhanced gene expression in bacteria.Nucleic acids research · 2026Article
- Evaluation of synthetic post-transcription regulatory sequences reveals design principle to enhance mRNA stability and translation efficiency.Synthetic and systems biotechnology · 2026Article
- mRNA medicine for cardiovascular disease.Nature cardiovascular research · 2026Review
- Investigating Potential 5' UTR G-Quadruplexes Within NRF2 mRNA.Current issues in molecular biology · 2026Article
- A unified computational framework for quantitative design and optimization of transcriptional regulation across bacterial species.Nucleic acids research · 2026Article
- Recent Progress in Sequence Optimization of mRNA Vaccine: Biological Mechanism, Quantitative Metrics, and Computational Model.Bioinformatics and biology insights · 2026Review
- Rif-seq reveals Caulobacter crescentus mRNA decay is globally coordinated with transcription and translation.Cell reports · 2025Article
- Long-range mRNA folding shapes expression and sequence of bacterial genes.bioRxiv : the preprint server for biology · 2025Article
- High-resolution profiling reveals coupled transcriptional and translational regulation of transgenes.Nucleic acids research · 2025Article
- Automated Design of Oligopools and Rapid Analysis of Massively Parallel Barcoded Measurements.ACS synthetic biology · 2024Article
- High-resolution profiling reveals coupled transcriptional and translational regulation of transgenes.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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