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

Nature Biotechnology volume 37, pages 803–809 (2019)Cite this article

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Abstract

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.

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Fig. 1: A library of 280,000 random 50-nucleotide oligomers as 5′ UTRs for eGFP.
Fig. 2: Modeling 5′ UTR sequences and ribosome loading.
Fig. 3: Design of new 5′ UTRs.
Fig. 4: Model performance with human 5′ UTRs and generalization to 5′ UTRs of varying length.

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Data availability

The authors declare that all data supporting the findings of this study are available from Gene Expression Omnibus under accession GSE114002.

Code availability

The code for the Optimus 5-Prime model is provided in the Supplementary Code file. All code is also available at https://github.com/pjsample/human_5utr_modeling.

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Acknowledgements

We would like to thank A. Rosenberg and J. Linder for helpful discussions on data analysis and modeling. We would also like to thank M. Moore, A. Hsieh and Y. Lim for constructive comments on the manuscript. We are grateful to C. Wang for providing fluorescence data27. This work was supported by a sponsored research agreement by Moderna and National Institutes of Health grant R01HG009892 to G.S.

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Author notes

  1. These authors contributed equally: Paul J. Sample, Ban Wang.

Authors and Affiliations

  1. Department of Electrical Engineering, University of Washington, Seattle, WA, USA

    Paul J. Sample, Ban Wang & Georg Seelig

  2. Moderna, Cambridge, MA, USA

    David W. Reid, Vlad Presnyak & Iain J. McFadyen

  3. Department of Biochemistry, University of Washington, Seattle, WA, USA

    David R. Morris

  4. Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA

    Georg Seelig

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  1. Paul J. Sample
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Contributions

P.J.S. and B.W. designed and performed experiments, performed data analysis and modeling, and wrote the manuscript. D.W.R. performed fluorescence validation experiments. V.P. and I.M. wrote the manuscript. D.R.M. helped design polysome profiling. G.S. designed experiments and wrote the manuscript.

Corresponding author

Correspondence to Georg Seelig.

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Competing interests

P.J.S., B.W., G.S. and DRM declare no competing interests. D.R., V.P. and I.M. are employees and shareholders of Moderna.

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Sample, P.J., Wang, B., Reid, D.W. et al. Human 5′ UTR design and variant effect prediction from a massively parallel translation assay. Nat Biotechnol 37, 803–809 (2019). https://doi.org/10.1038/s41587-019-0164-5

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