Human 5′ UTR design and variant effect prediction from a massively parallel translation assay

By Paul J. Sample, Ban Wang, David W. Reid, Vladimir Presnyak, I. McFadyen, D. Morris, Georg Seelig. Nature Biotechnology, 2018

408 citations recorded by the source corpus.

The ability to predict the impact of cis-regulatory sequences on gene expression would facilitate discovery in fundamental and applied biology. Here we combine polysome profiling of a library of 280,000 randomized 5′ untranslated regions (UTRs) with deep learning to build a predictive model that relates human 5′ UTR sequence to translation. Together with a genetic algorithm, we use the model to engineer new 5′ UTRs that accurately direct specified levels of ribosome loading, providing the ability to tune sequences for optimal protein expression. We show that the same approach can be extended to chemically modified RNA, an important feature for applications in mRNA therapeutics and synthetic biology. We test 35,212 truncated human 5′ UTRs and 3,577 naturally occurring variants and show that the model predicts ribosome loading of these sequences. Finally, we provide evidence of 45 single-nucleotide variants (SNVs) associated with human diseases that substantially change ribosome loading and thus may represent a molecular basis for disease. Ribosome loading is predictably controlled through design of 5′ UTR sequences.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7f066c4bbef626e9eba6d02c54ecbb760e698704

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In Molecular Biology

Citations in the corpus

Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions (2022, methodology): “Furthermore, 1D CNN is designed to predict the protein expression levels regulated by human 5’ UTRs from RNA sequence [37].”
Model-based reinforcement learning for biological sequence design (2020, background): “Recently, machine learning approaches have been shown to be effective in optimizing real-world DNA and protein sequences (Wang et al., 2019; Chhibbar & Joshi, 2019; de Jongh et al., 2019; Liu et al., 2019; Sample et al., 2019; Wu et al., 2019).”
mRNA-based therapeutics: powerful and versatile tools to combat diseases (2022, background): “Notably, researchers tried to design effective UTRs with the help of bioinformation and machine learning.(64,65)”