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Cited by 205RNA and protein synthesis mechanismsRNA Research and SplicingFungal and yeast genetics research

Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences

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Cuperus, Josh T., Groves, Benjamin, Kuchina, Anna, Rosenberg, Alexander B., Jojic, Nebojsa, Fields, Stanley, Seelig, Georg·2017·Genome Research
DOI 10.1101/gr.224964.117PMC PMC5741052

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Abstract

Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of cis -regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the protein expression of the 5′ untranslated region (UTR) of mRNAs in the yeast Saccharomyces cerevisiae. We constructed a library of half a million 50-nucleotide-long random 5′ UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on protein expression of Kozak sequence composition, upstream open reading frames (uORFs), and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the protein expression of both a held-out set of the random 5′ UTRs as well as native S. cerevisiae 5′ UTRs. The model additionally was used to computationally evolve highly active 5′ UTRs. We confirmed experimentally that the great majority of the evolved sequences led to higher protein expression rates than the starting sequences, demonstrating the predictive power of this model.

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  • Earlier MPRAs designed to learn aspects of 5′ UTR cis-regulation relied on FACS or growth selection to stratify libraries by activity.

    “Earlier MPRAs designed to learn aspects of 5′ UTR cis-regulation relied on FACS10,11 or growth selection12 to stratify libraries by activity.”

    From Human 5′ UTR design and variant effect prediction from a massively parallel translation assay (2019) · cites this paper as [12]

  • FACS- and growth-selection-based MPRAs require expression of a single library variant per cell that must be transcribed within the cell from a DNA template, making it difficult to distinguish between the effects of transcriptional and translational control.

    “These techniques require the expression of a single library variant per cell that must be transcribed within the cell from a DNA template, making it difficult to distinguish between the effects of transcriptional and translational control.”

    From Human 5′ UTR design and variant effect prediction from a massively parallel translation assay (2019) · cites this paper as [12]

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Cited by

Sorted by citations

Authors
Cuperus, J. T., Groves, B., Kuchina, A., Rosenberg, A. B., Jojic, N., Fields, S., & Seelig, G.
Affiliations
  • Howard Hughes Medical Institute (US)
  • University of Washington (US)
Corresponding author
  • Fields, Stanley
  • Seelig, Georg
ORCID
Venue
Genome Research
Publisher
Cold Spring Harbor Laboratory
Published
2017-11-02
Volume
27
Issue
12
Pages
2015-2024
ISSN
  • 1088-9051 (ISSN-L)
  • 1549-5469
DOI
10.1101/gr.224964.117
PMC
PMC5741052
OpenAlex
W2951203227
Cited by
205
References
4
Citations by year
2026: 19 · 2025: 21 · 2024: 25 · 2023: 25 · 2022: 23 · 2021: 27 · 2020: 26 · 2019: 24 · 2018: 14 · 2017: 1
Type
posted content
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Genome Research (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • HighWire Press Open Archive (submittedVersion)
Funders
  • Defense Advanced Research Projects Agency (W911NF-11-2-0068)
  • National Science Foundation (CCF 1317653)
  • National Institutes of Health (P41 GM103533)
  • Howard Hughes Medical Institute
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
Keywords
5' untranslated region, protein expression prediction, Saccharomyces cerevisiae, Kozak sequence, upstream open reading frames, mRNA secondary structure, convolutional neural networks, computational evolution, synthetic biology, gene design
MeSH terms
Machine Learning, Computer Simulation, Models, Genetic, RNA, Fungal, RNA, Messenger, Saccharomyces cerevisiae, Gene Library, Neural Networks, Computer, Alternative Splicing, 5' Untranslated Regions

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